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Red and Photographic Infrared Linear Combinations for Monitoring Vegetation
Abstract
The relationships between various linear combinations of red and photographic infrared radiances and vegetation parameters are investigated. In situ spectrometers are used to measure the relationships between linear combinations of red and IR radiances, their ratios and square roots, and biomass, leaf water content and chlorophyll content of a grass canopy in June, September and October. Regression analysis shows red-IR combinations to be more significant than green-red combinations. The IR/red ratio, the square root of the IR/red ratio, the vegetation index (IR-red difference divided by their sum) and the transformed vegetation index (the square root of the vegetation index + 0.5) are found to be sensitive to the amount of photosynthetically active vegetation. The accumulation of dead vegetation over the year is found to have a linearizing effect on the various vegetation measures.
... One of the most prevalent and widely used vegetation indexes, the Normalized Difference Vegetation Index (NDVI), is abbreviated as NDVI, to assess the vegainess, the fitness of vegetation, and the disparity of vegation content. This knowledge can be obtained through a comparison of the red (R) bands of the electromagnetic spectrum with the nearinfrared (NIR) bands (Tucker, 1979) [38] . It involves various areas of research, such as the estimation of vegetation cover, the prediction of phenological changes, and the calculation of net primary production (Huang et al., 2014;Wu et al., 2008) [19,40] . ...
... One of the most prevalent and widely used vegetation indexes, the Normalized Difference Vegetation Index (NDVI), is abbreviated as NDVI, to assess the vegainess, the fitness of vegetation, and the disparity of vegation content. This knowledge can be obtained through a comparison of the red (R) bands of the electromagnetic spectrum with the nearinfrared (NIR) bands (Tucker, 1979) [38] . It involves various areas of research, such as the estimation of vegetation cover, the prediction of phenological changes, and the calculation of net primary production (Huang et al., 2014;Wu et al., 2008) [19,40] . ...
... The Normalized Difference Vegetation Index was calculated as NDVI = (Near Infrared-Red) / (Near Infrared + Red). An NDVI's range is from -1 to 1 with greater values indicating denser and more compact vegetation (Tucker, 1979) [38] . The instance created by Pettorelli et al. (2005) is served for crop monitoring, forest cover mapping, and land degradation analysis. ...
... Além disso, sugeriu-se, ainda naquela época, que a radiação NIR apresenta espalhamento no interior do dossel superior à radiação vermelho e que sombras no dossel, influenciadas pelo ângulo de incidência solar, podem resultar em índices de vegetação distintos. Tucker (1979) demonstrou a correlação negativa entre a radiação vermelho e biomassa e a correlação positiva entre biomassa e a radiação NIR. Ou seja, à medida que a biomassa de uma área aumenta, ocorrem a diminuição da reflectância no vermelho e o aumento da reflectância no NIR. ...
... Ou seja, à medida que a biomassa de uma área aumenta, ocorrem a diminuição da reflectância no vermelho e o aumento da reflectância no NIR. Nesse trabalho (Tucker, 1979), são apresentadas ainda diversas combinações entre bandas espectrais, as quais podemos chamar de diferentes índices de vegetação, para estimativa de biomassa. ...
Para o sucesso da atividade agrícola na atualidade são necessárias
tomadas de decisão cada vez mais rápidas e assertivas. Diante
dessa necessidade, a busca por informações por meio do sensoriamento
remoto tem atraído os principais gestores da cadeia produtiva
da soja e o uso de índices de vegetação tem ocorrido de forma ampla
e popular. Para esse sensoriamento, há praticamente 50 anos, foi
proposto o NDVI (Índice de Vegetação da Diferença Normalizada)
que, ainda hoje, constitui-se no índice de vegetação mais popular e
uma das ferramentas mais utilizadas no processamento digital de
imagens dentro da chamada agricultura de precisão.
Nos últimos anos, com a popularização de drones ou VANTs (veículos
aéreos não tripulados), o NDVI teve uma ampla difusão em
diferentes setores do agronegócio. Entretanto, é preciso ponderar na
utilização do NDVI para o monitoramento da soja, se a resposta espectral
obtida é inerente às plantas de soja ou à mistura espectral
com solo, palhada e demais objetos que possam estar dispostos na
área de interesse. Isso é crucial para que o uso do NDVI no monitoramento
agrícola não caia em descrença pelos setores ligados à cadeia
produtiva da soja.
Nesse cenário, a presente publicação reúne dados espectrais coletados
em diferentes experimentos conduzidos na Embrapa Soja, na
Universidade Estadual de Maringá e em áreas de parceiros entre as
safras 2010/2011 e 2023/2024 com o objetivo de demonstrar que as
variações nos valores de NDVI da soja coletados no nível do dossel
ocorrem, de forma majoritária, em função do percentual de cobertura
do solo, da biomassa e da área foliar e com magnitude diminuta em
função de estresses fisiológicos, como deficiência hídrica e nutricional,
ocorrência de doenças e presença de insetos.
Espera-se, com essa publicação, subsidiar o melhor emprego do
NDVI como uma ferramenta digital eficiente para o monitoramento
da cobertura do solo nos sistemas de produção de soja, trazendo ao
leitor o contexto histórico da formulação do NDVI, apresentando os
parâmetros técnico-científicos que embasaram a teoria do índice e
destacando sua aplicabilidade no monitoramento da vegetação.
... Vegetation indices, such as NDVI [41], are widely used for vegetation mapping and monitoring [42]. Here, we combined Otsu thresholding [43] and NDVI to extract S. alterniflora and itinerant biological structures. ...
... The input data of the classifier include spectral and texture features. The spectral features include the red, green, blue, NIR and SWIR bands, normalized difference water index (NDWI) [41], modified normalized difference water index (mNDWI) [53], NDVI, enhanced vegetation index (EVI) [54], and land surface water index (LSWI) [55]. The texture feature is the standard deviation of NDVI in a box with a radius of 5 pixels. ...
The invasion of Spartina alterniflora (S. alterniflora) has posed serious threats to the sustainability, quality and biodiversity of coastal wetlands. To safeguard coastal ecosystems, China has enacted large-scale S. alterniflora removal projects, which set the goal of effectively controlling S. alterniflora throughout China by 2025. The accurate monitoring of S. alterniflora with remote sensing is urgent and requisite for the scientific eradication, control and management of this invasive plant. In this study, we combined multi-temporal WorldView-2/3 (WV-2/3) and Sentinel-1/2 imagery to monitor the S. alterniflora dynamics before and after the S. alterniflora removal projects in Zhangjiang Estuary. We put forward a new method for S. alterniflora detection with eight-band WV-2/3 imagery. The proposed method first used NDVI to discriminate S. alterniflora from water, mud flats and mangroves based on Ostu thresholding and then used the red-edge, NIR1 and NIR2 bands and support vector machine (SVM) classifier to distinguish S. alterniflora from algae. Due to the contamination of frequent cloud cover and tidal inundation, the long revisit time of high-resolution satellite sensors and the short-term S. alterniflora removal projects, we combined Sentinel-1 SAR time series and Sentinel-2 optical imagery to monitor the S. alterniflora removal project status in 2023. The overall accuracies of the S. alterniflora detection results here are above 90%. Compared with the traditional SVM method, the proposed method achieved significantly higher identification accuracy. The S. alterniflora area was 115.19 hm2 in 2015, 152.40 hm2 in 2017 and 15.29 hm2 in 2023, respectively. The generated S. alterniflora maps clearly show the clonal growth of S. alterniflora in Zhangjiang Estuary from 2015 to 2017, and the large-scale S. alterniflora eradication project has achieved remarkable results with a removal rate of about 90% in the study area. With the continuous implementation of the “Special Action Plan for the Prevention and Control of Spartina alterniflora (2022–2025)” which aims to eliminate more than 90% of S. alterniflora in all provinces in China by 2025, the continual high-spatial resolution monitoring of S. alterniflora is crucial to control secondary invasion and restore coastal wetlands.
... Finally, the coefficient of determination (r), root mean square error (RMSE), and relative error (RE) are used as indicators to assess the correlation of the model's predicted values. ( Rouse et al., 1973) GNDVI GNDVI = (R Nir − R Green )=(R Nir + R Green ) ( Wagner, 1996) NGBDI NGBDI = (R Green − R Blue )=(R Green + R Blue ) ( Dong et al., 2015) NGRDI NGRDI = (R Green − R Re d )=(R Green + R Re d ) ( Dong et al., 2015) RERDVI RERDVI = (R Nir − R Red _ edge )=(R Nir + R Red _ edge ) (Xue and Su, 2017) SAVI SAVI = 2:5 * (R Nir − R Red )=(R Nir + R Red + 0:5) (Huete, 1988) OSAVI OSAVI = (R Nir − R Red )=(R Nir + R Red + 0:16) (Rondeaux et al., 1996) RVI RVI = R Nir =R Red (Wang et al., 2007) DVI DVI = R Nir − R Red (Tucker, 1979) GRVI GRVI = R Nir =R Green (Tucker, 1979) RE ¼ ...
... Finally, the coefficient of determination (r), root mean square error (RMSE), and relative error (RE) are used as indicators to assess the correlation of the model's predicted values. ( Rouse et al., 1973) GNDVI GNDVI = (R Nir − R Green )=(R Nir + R Green ) ( Wagner, 1996) NGBDI NGBDI = (R Green − R Blue )=(R Green + R Blue ) ( Dong et al., 2015) NGRDI NGRDI = (R Green − R Re d )=(R Green + R Re d ) ( Dong et al., 2015) RERDVI RERDVI = (R Nir − R Red _ edge )=(R Nir + R Red _ edge ) (Xue and Su, 2017) SAVI SAVI = 2:5 * (R Nir − R Red )=(R Nir + R Red + 0:5) (Huete, 1988) OSAVI OSAVI = (R Nir − R Red )=(R Nir + R Red + 0:16) (Rondeaux et al., 1996) RVI RVI = R Nir =R Red (Wang et al., 2007) DVI DVI = R Nir − R Red (Tucker, 1979) GRVI GRVI = R Nir =R Green (Tucker, 1979) RE ¼ ...
Rapidly obtaining the chlorophyll content of crop leaves is of great significance for timely diagnosis of crop health and effective field management. Multispectral imagery obtained from unmanned aerial vehicles (UAV) is being used to remotely sense the SPAD (Soil and Plant Analyzer Development) values of wheat crops. However, existing research has not yet fully considered the impact of different growth stages and crop populations on the accuracy of SPAD estimation. In this study, 300 materials from winter wheat natural populations in Xinjiang, collected between 2020 to 2022, were analyzed. UAV multispectral images were obtained in the experimental area, and vegetation indices were extracted to analyze the correlation between the selected vegetation indices and SPAD values. The input variables for the model were screened, and a support vector machine (SVM) model was constructed to estimate SPAD values during the heading, flowering, and filling stages under different water stresses. The aim was to provide a method for the rapid acquisition of winter wheat SPAD values. The results showed that the SPAD values under normal irrigation were higher than those under water restriction. Multiple vegetation indices were significantly correlated with SPAD values. In the prediction model construction of SPAD, the different models had high estimation accuracy under both normal irrigation and water limitation treatments, with correlation coefficients of predicted and measured values under normal irrigation in different environments the value of r from 0.59 to 0.81 and RMSE from 2.15 to 11.64, compared to RE from 0.10% to 1.00%; and under drought stress in different environments, correlation coefficients of predicted and measured values of r was 0.69–0.79, RMSE was 2.30–12.94, and RE was 0.10%–1.30%. This study demonstrated that the optimal combination of feature selection methods and machine learning algorithms can lead to a more accurate estimation of winter wheat SPAD values. In summary, the SVM model based on UAV multispectral images can rapidly and accurately estimate winter wheat SPAD value.
... NDVI is relatively mature and is widely used to observe plant canopies (Tucker, 1979). The calculation Equations (3-5) formula is as follows: ...
... Because the measurement error from observation angle can be reduced by fixing the observation direction, it is necessary to study the measurement changes caused by crop planting density. Figure 5 indicates that the NDVI correlates well with LAI, and this vegetation index is relatively mature and widely used in canopy measurement (Tucker, 1979). ...
Canopy height serves as an important dynamic indicator of crop growth in the decision-making process of field management. Compared with other commonly used canopy height measurement techniques, ultrasonic sensors are inexpensive and can be exposed in fields for long periods of time to obtain easy-to-process data. However, the acoustic wave characteristics and crop canopy structure affect the measurement accuracy. To improve the ultrasonic sensor measurement accuracy, a four-year (2018−2021) field experiment was conducted on maize and wheat, and a measurement platform was developed. A series of single-factor experiments were conducted to investigate the significant factors affecting measurements, including the observation angle (0−60°), observation height (0.5−2.5 m), observation period (8:00−18:00), platform moving speed with respect to the crop (0−2.0 m min⁻¹), planting density (0.2−1 time of standard planting density), and growth stage (maize from three−leaf to harvest period and wheat from regreening to maturity period). The results indicated that both the observation angle and planting density significantly affected the results of ultrasonic measurements (p-value< 0.05), whereas the effects of other factors on measurement accuracy were negligible (p-value > 0.05). Moreover, a double-input factor calibration model was constructed to assess canopy height under different years by utilizing the normalized difference vegetation index and ultrasonic measurements. The model was developed by employing the least-squares method, and ultrasonic measurement accuracy was significantly improved when integrating the measured value of canopy heights and the normalized difference vegetation index (NDVI). The maize measurement accuracy had a root mean squared error (RMSE) ranging from 81.4 mm to 93.6 mm, while the wheat measurement accuracy had an RMSE from 37.1 mm to 47.2 mm. The research results effectively combine stable and low-cost commercial sensors with ground-based agricultural machinery platforms, enabling efficient and non-destructive acquisition of crop height information.
... In this short communication, we explore the potentiality of using the Google Earth Engine (GEE) platform (Gorelick et al., 2017) to monitor the development of the Burundi Landscape Restoration and Resilience Project by the World Bank (PRRPB) 6 . The project had been experimenting with landscape restoration measures in the Isare and Buhinyuza "Communes", or Municipalities, respectively in Bujumbura Rural and Muyinga Provinces, on a total of 22 "Collines", namely the third administrative level of the country. ...
... The NDVI (Tucker, 1979) was used as a measure of the effectiveness of the landscape restoration effect within the plot under study, similar to other approaches (Ruijsch et al., 2023). NDVI can be considered an indicator of vegetation's health and can be then used as a proxy for evaluating the impact of landscape restoration projects. ...
Evaluating the results of large‐scale land and water management project is key to ensuring their replicability and long‐term sustainability. The present work was developed within the Burundi Landscape Restoration and Resilience Project by the WorldBank (PRRPB), active from 2019 to 2024, where slow‐forming terraces were implemented together with rainwater harvesting trenches on contour lines. Such works were developed in two “communes” of the country, namely Isare and Buhinyuza, with opposite climatic conditions. For the dryer and hotter commune of Buhinyuza, the present work utilizes a set of remote sensing datasets and indicators to monitor the effects of PRRPB project, using the Google Earth Engine cloud computing platform.
... Given the limitations of pattern texture analysis in this context, prioritizing spectral information is crucial. Thus, this study utilized spectral indices including NDVI [20], normalized difference water index (NDWI) [21], normalized difference red edge index (NDRE) [22], green chlorophyll index (GCI) [23], and green leaf index (GLI) [24]. Additionally, each of the five bands was individually divided by the sum of all bands, resulting in five additional spectral indices, namely ALGB, ALGG, ALGR, ALGRE, and ALGNIR. ...
The prevalence of the invasive species African Lovegrass (Eragrostis curvula, ALG thereafter) in Australian landscapes presents significant challenges for land managers, including agricultural losses, reduced native species diversity, and heightened bushfire risks. Uncrewed aerial system (UAS) remote sensing combined with AI algorithms offer a powerful tool for accurately mapping the spatial distribution of invasive species and facilitating effective management strategies. However, segmentation of vegetations within mixed grassland ecosystems presents challenges due to spatial heterogeneity, spectral similarity, and seasonal variability. The performance of state-of-the-art artificial intelligence (AI) algorithms in detecting ALG in the Australian landscape remains unknown. This study compared the performance of four supervised AI models for segmenting ALG using multispectral (MS) imagery at four sites and developed segmentation models for two different seasonal conditions. UAS surveys were conducted at four sites in New South Wales, Australia. Two of the four sites were surveyed in two distinct seasons (flowering and vegetative), each comprised of different data collection settings. A comparative analysis was also conducted between hyperspectral (HS) and MS imagery at a single site within the flowering season. Of the five AI models developed (XGBoost, RF, SVM, CNN, and U-Net), XGBoost and the customized CNN model achieved the highest validation accuracy at 99%. The AI model testing used two approaches: quadrat-based ALG proportion prediction for mixed environments and pixel-wise classification in masked regions where ALG and other classes could be confidently differentiated. Quadrat-based ALG proportion ground truth values were compared against the prediction for the custom CNN model, resulting in 5.77% and 12.9% RMSE for the seasons, respectively, emphasizing the superiority of the custom CNN model over other AI algorithms. The comparison of the U-Net demonstrated that the developed CNN effectively captures ALG without requiring the more intricate architecture of U-Net. Masked-based testing results also showed higher F1 scores, with 91.68% for the flowering season and 90.61% for the vegetative season. Models trained on single-season data exhibited decreased performance when evaluated on data from a different season with varying collection settings. Integrating data from both seasons during training resulted in a reduction in error for out-of-season predictions, suggesting improved generalizability through multi-season data integration. Moreover, HS and MS predictions using the custom CNN model achieved similar test results with around 20% RMSE compared to the ground truth proportion, highlighting the practicality of MS imagery over HS due to operational limitations. Integrating AI with UAS for ALG segmentation shows great promise for biodiversity conservation in Australian landscapes by facilitating more effective and sustainable management strategies for controlling ALG spread.
... and was developed as an improvement to the standard NDVI to provide a more robust estimate of chlorophyll content [82] across a wide range of species and leaf structures [81]. Henceforth, this index will be referred to as "NDVI" in the text. ...
Background
Drought adaptation is critical to many tree species persisting under climate change, however our knowledge of the genetic basis for trees to adapt to drought is limited. This knowledge gap impedes our fundamental understanding of drought response and application to forest production and conservation. To improve our understanding of the genomic determinants, architecture, and trait constraints, we assembled a reference genome and detected ~ 6.5 M variants in 432 phenotyped individuals for the foundational tree Corymbia calophylla.
Results
We found 273 genomic variants determining traits with moderate heritability (h²SNP = 0.26–0.64). Significant variants were predominantly in gene regulatory elements distributed among several haplotype blocks across all chromosomes. Furthermore, traits were constrained by frequent epistatic and pleiotropic interactions.
Conclusions
Our results on the genetic basis for drought traits in Corymbia calophylla have several implications for the ability to adapt to climate change: (1) drought related traits are controlled by complex genomic architectures with large haplotypes, epistatic, and pleiotropic interactions; (2) the most significant variants determining drought related traits occurred in regulatory regions; and (3) models incorporating epistatic interactions increase trait predictions. Our findings indicate that despite moderate heritability drought traits are likely constrained by complex genomic architecture potentially limiting trees response to climate change.
... Identification of agriculture planning, urbanization and environmental conditions monitoring can be done using LULC analysis [19][20][21][22][23]. Yuan et al. [24] observed and recorded the LC changes with the aid of multitemporal Landsat TM data. Tucker et al. [51,52], showed that green foliage can be observed through their spectral properties. A large range of satellite data is acquired from satellite sensors with extensive spectral range and different spatial and temporal resolutions to acquire vegetation related information. ...
Land cover is more effected spatially and temporarily based on land use changes. Depending on increasing demands of population growth, changes in Land Use/Land Cover (LULC) occurs which are regular funders of land surface. It is essential to know the information of land cover for environmental policies, agriculture, economy. Remote sensing technology is more powerful to obtain Earth"s surface features at numerous temporal and spatial changes. Change in LULC disturbs the natural environmental, ecological conditions and also changes the quality of water in that area. The Normalized Difference Vegetation Index (NDVI) is the most used vegetation indices to analyze and assess remote sensing images to identify whether the land of interest has green vegetation or not. By computing NDVI with the help of multi spectral data, the spatial distribution of remote sensed data that is the information of water content, constructional area, vegetation and high-density vegetation can be obtained. While comprehensive surveys of related problem is not well surveyed. A huge quantity of classification techniques are established to address this problem and the objective of this paper is to classify and review these techniques. The aim of this survey is to afford up to date works that provide potential path for further research in land cover change assessment using NDVI.
... (1, 2, 3) (Tucker, 1979): ...
Continuous oil exploration and construction of oil refineries in southern Iraq, accompanied by gas emissions and fire flaring, may be attributed to increases in land surface temperature (LST) and lower vegetation. This study explores spatio-temporal distribution of LST and normalized difference vegetation index (NDVI) in the petroleum and non-petroleum regions in Al-Basra Governorate (southern Iraq) during two periods separated by 20-years (2000 and 2021) using Landsat 7, 8. Five land cover types were mapped for Al-Basra Governorate (water, dense vegetation, sparse vegetation, urban land, and bare soil). The results showed higher LSTmax in the petroleum region relative to the non-petroleum region in both periods. The highest LSTmax was recorded in the bare soil, followed by urban land and sparse vegetation, for both the petroleum and non-petroleum regions. Linear trend showed an increase in 0.733 °C from 2000 to 2021 in the petroleum region. Higher LSTmax was recorded in 2021 compared to 2000. The NDVI in the petroleum region was shown to be lower than the NDVI in the non-petroleum region. Negative correlations were observed between LST and NDVI in 2000 and 2021, r = − 0.86 and − 0.09, respectively. To our knowledge, this is the first study that explored the spatio temporal variation and the relationship between LST and NDVI in the petroleum Gulf countries. High LST and low NDVI in southern Iraq, particularly in the petroleum region might be associated with intense anthropogenic sources and climate change. It is concluded that southern Iraq (Al-Basra) including its major oil fields, is subjected to harsh climatic conditions due to anthropogenic activities, which greatly affected the distribution of vegetation cover over the past twenty years. This study has implications for decision-makers to support clean energy and raise awareness of fossil fuel combustion, thus reducing the effects of climate change.
... Before 1999, Landsat 5 operated and the image timeseries had a 16-day frequency; for the remainder, both satellites 5 and 7 were operating and two images were acquired, and used, in each 16-day interval. NDVI was calculated following [34]: ...
Climate change has altered the frequency and severity of extreme weather, which can affect vegetation condition and habitat quality for wildlife. Declines in vegetation productivity during droughts and heatwaves can negatively impact animals that depend on vegetation for water and nutrition. The ability to detect such effects on habitat suitability can reveal refugia, which can be prioritised for protection to improve threatened species conservation. We used the normalised difference vegetation index (NDVI) to look at relationships between vegetation productivity and the presence of koalas (Phascolarctos cinereus) in potential habitat throughout much of their range. Using a large, long-term koala presence dataset, we tested the hypothesis that locations where koalas had been observed would exhibit higher NDVI values than a random, representative sample from the same vegetation group (i.e. woodlands, open forests or tall open forests). We also identified the minimum NDVI threshold at which koalas occurred across time for each vegetation group and compared these to the minimum NDVI values observed across potential koala habitat before and during the millennium drought; one of the worst recorded in Australia. Additionally, we investigated whether vegetation above the minimum NDVI thresholds was significantly closer to perennial water than unsuitable vegetation. We found that koalas tend to occur at locations exhibiting higher NDVI than average for all vegetation groups. We also found that 49% of all vegetation groups maintained a minimum NDVI above the koalas’ NDVI threshold before drought, equating to 190,227 km2, which declined to 166,746 km2 during drought (a 12% reduction). We also found that unsuitable vegetation tended to occur farther from perennial water than suitable vegetation for some vegetation groups. Areas that remained above the NDVI thresholds during the drought should be considered potential refugia for populations during an event of similar magnitude and could indicate future core habitat extent.
... By normalizing the canopy structure, texture measures can also enhance the estimation of biomass using optical imagery (Sarker and Nichol 2011;Eckert 2012). The normalization of band can minimise the contributions from the angle of sun, sensor, and soil background (Tucker 1979;Huete et al. 1985). The use of NDTIs in combination with texture measurement and normalisation techniques can ameliorate the estimation of biomass. ...
Multispectral (MS) images offer essential spectral information for monitoring paddy crops’ Aboveground-biomass (AGB), but efficiency decreases due to background materials and high canopy biomass. Texture reveals canopy structure and can be employed in vegetation-indices (VIs) to enhance monitoring accuracy. This study focuses to estimate AGB of paddy crop by exploring the combined potential of spectral and textural features of unmanned aerial vehicle (UAV)-MS images using linear regression (LR), multi-linear regression (MLR), and random forest (RF) models. Results demonstrate that near infrared (NIR)-based VIs outperform Colour-Indices. Normalised difference texture indices (NDTIs) composed of NIR, red-edge (RE) and blue (B) bands outperform all-evaluated VIs and grey-level co-occurrence matrix (GLCM)-textures for different growth stages. Combining VIs and NDTIs, RF performs best compared to other models. The outcomes suggest that the combined spectral and texture information can significantly improve estimation of AGB in paddy crops compared to using either of them alone.
... Exposure to greenness was assessed using the Normalized Difference Vegetation Index (NDVI) 32 as a proxy for the overall greenness level, which ranges from −1 to +1. Values closer to +1 indicate greener and denser vegetation. ...
Background
Lower birth weight and preterm birth may increase the risk of adverse health outcomes later in life. We examined whether maternal exposure to air pollution and greenness during pregnancy is associated with offspring birth weight and preterm birth.
Methods
We analyzed data on 4286 singleton births from 2358 mothers from Respiratory Health in Northern Europe, a prospective questionnaire-based cohort study (1990–2010). Mixed-effects regression models with random intercepts for mothers and centers were used to estimate the association of exposures to particulate matter (PM 2.5 and PM 10 ), nitrogen dioxide (NO 2 ), ozone (O 3 ), black carbon (BC), and greenness (Normalized Difference Vegetation Index in 300m-buffers [NDVI 300m ]) with birth outcomes, adjusting for potential confounders.
Results
Median (interquartile range [IQR]) exposures to PM 2.5 , PM 10 , NO 2 , O 3 , BC, and NDVI 300m during pregnancy were 8.4(5.0) µg/m ³ , 14.4(8.3) µg/m ³ , 14.0(11.0) µg/m ³ , 54.7(10.2) µg/m ³ , 0.47(0.41) µg/m ³ , and 0.31(0.20), respectively. IQR increases in air pollution exposures during pregnancy were associated with decreased birth weight and the strongest association was seen for PM 2.5 (−49g; 95% confidence interval [CI] = −83, −16). However, O 3 showed an opposite association. IQR increase in NDVI 300m was associated with an increase in birth weight of 25 g (95% CI = 7, 44). Preterm birth was not associated with the exposures.
Conclusion
Increased greenness and decreased air pollution may contribute to healthier pregnancies and improve overall health in the next generation. This emphasizes the need to adopt policies that target the reduction of air pollution emissions and exposure of the population.
... The non-dimensional vegetation index, or NDVI, is defined by the difference in reflectance between visible and near-infrared light. One of the most often used indices for tracking vegetation dynamics at the regional and global levels is this one [51]. The assessment of the density of vegetation is done using the NDVI data [52,53]. ...
Groundwater resources are crucial for the future, facing increasing demand due to environmental changes and population growth. Economical and efficient exploration methods are becoming increasingly important, especially for middle-and lower-income regions. Deeper exploration is necessary to ensure a sustainable supply for future generations. This study examines the state-of-the-art in groundwater potential mapping. By analyzing scientific articles, it explores the recent advancements in this technique, which utilizes geographic databases and remote sensing. From a methodological perspective, the rise of machine learning algorithms is particularly noteworthy. Combining these methods with human expertise offers significant potential advantages, and experts believe it will lead to more accurate mapping. However, simply discovering groundwater is not enough. Water quality and quantity are equally important for sustainable use. Therefore, this study presents two case studies that demonstrate groundwater mapping and quality management techniques. Furthermore, due to the ever-increasing population and changing land use, continuous research is essential for a long-term perspective on groundwater availability in specific regions.
... Vì vậy, chỉ số khác biệt thực vật (NDVI) và chỉ số khác biệt nước (NDWI) được sử dụng kết hợp để việc phân tách các đối tượng hiện trạng được tốt hơn. + Chỉ số thực vật (NDVI) được tính theo công thức (1): NDVI = (NIR -RED)/(NIR + RED) (Tucker, 1979) (1) + Chỉ số khác biệt nước (NDWI) được tính theo công thức (2): NDWI = (GREEN -NIR)/(GREEN + NIR) (McFeeters, 1996) (2) Giá trị của chỉ số NDVI, NDWI nằm trong khoảng từ -1 đến +1 và thông thường chỉ số NDWI của ảnh Planet cho đất mặt nước có giá trị trong khoảng lớn hơn -0,2 (Nguyễn Phương Dung và Nguyễn Quang Thanh, 2022). ...
Nghiên cứu được thực hiện nhằm xây dựng bản đồ hiện trạng sử dụng đất (HTSDĐ) trên địa bàn huyện Tháp Mười, tỉnh Đồng Tháp sử dụng ảnh viễn thám. Đồng thời, so sánh đối chiếu với bản đồ HTSDĐ từ kết quả kiểm kê đất đai (KKĐĐ) để tìm ra giải pháp giúp nâng cao hiệu quả công tác KKĐĐ, lập bản đồ HTSDĐ giai đoạn tiếp theo trên địa bàn huyện. Cụ thể, nghiên cứu sử dụng ảnh PlanetScope để xây dựng bản đồ HTSDĐ thông qua phương pháp phân loại theo hướng đối tượng, kết hợp sử dụng chỉ số thực vật (NDVI) và chỉ số khác biệt nước (NDWI). Kết quả giải đoán ảnh đã thành lập được bản đồ HTSDĐ trên 6 loại hiện trạng với độ chính xác toàn cục là 97,2%, hệ số kappa là 0,94. Kết quả còn cho thấy, việc ứng dụng ảnh viễn thám trong xác định khác biệt giữa hiện trạng sử dụng đất và hồ sơ địa chính có tính khả thi cao. Trên cơ sở tham vấn ý kiến chuyên gia trực tiếp thực hiện công tác KKĐĐ trên địa bàn huyện, nghiên cứu đề xuất đƣợc quy trình thực hiện KKĐĐ, lập bản đồ HTSDĐ kết hợp giữa phương pháp truyền thống và ảnh viễn thám nhằm nâng cao hiệu quả ứng dụng công nghệ thông tin trong công tác quản lý đất đai tại địa phương.
... Chỉ số phổ thực vật NDVI (Normalized Differnce Vegetation Index) được kết hợp từ các kênh phổ nhìn thấy, cận hồng ngoại, hồng ngoại và đỏ là các tham số trung gian mà từ đó có thể đánh giá được các đặc tính khác nhau của thực vật. Tạo ảnh chỉ số thực vật NDVI theo công thức Tucker (1979) NDVI = (NIR -RED)/(NIR + RED) (-1 ≤ NDVI ≤ 1) Trong đó: NIR là giá trị phổ phản xạ của kênh cận hồng ngoại, RED là giá trị phổ phản xạ của kênh đỏ. NDVI biến động trong khoảng từ -1 đến 1. Giá trị NDVI càng gần 1 là nơi có thực vật phát triển tốt, mật độ cao. ...
Đô thị hóa tăng nhanh ngoài các mặt tích cực về kinh tế xã hội đồng thời cũng mang đến các mặt tiêu cực, điển hình như hiện tượng đảo nhiệt đô thị (UHI). UHI là hiện tượng tại khu vực đô thị có nhiệt độ nóng hơn đáng kể so với các vùng xung quanh. Nghiên cứu tiến hành nhằm đánh giá tình trạng đảo nhiệt đô thị bề mặt và phân tích ảnh hưởng của UHI đến người dân tại thành phố Cần Thơ, trung tâm kinh tế của Đồng bằng sông Cửu Long. Kết quả nghiên cứu cho thấy mức độ UHI phần lớn ở khoảng 3-5oC, tập trung tại các khu vực có mật độ xây dựng cao, khu công nghiệp, khu chế xuất. Kết quả phỏng vấn người dân tại những vùng chịu ảnh hưởng của đảo nhiệt cho thấy các đối tượng hầu như đều chịu ảnh hưởng bởi nắng nóng, đồng thời có biểu hiện và khoảng nhiệt độ bắt đầu gây khó chịu khác nhau trên từng nhóm đối tượng. Các nghiên cứu chuyên sâu về mức độ tổn thƣơng do nhiệt và giải pháp hướng đến thành phố xanh cần được tiếp tục quan tâm thực hiện.
... To measure rice productivity, four NASA MODIS (moderate resolution imaging spectroradiometer) products were evaluated-NDVI (normalized difference vegetation index; Didan, 2015), EVI (enhanced vegetation index; Didan, 2015), LAI (leaf area index; Myneni et al., 2015), and GPP (gross primary productivity; Running et al., 2015) (Table 1). NDVI uses a ratio of the red and near-infrared (NIR) bands, i.e., (NIR-red)/(NIR + red), to evaluate vegetation health, vegetation density, and plant leaf structure (Tucker, 1979). EVI is similar to NDVI in principle, but is designed to reduce atmospheric interferences (Huete et al., 1994). ...
Measuring agricultural productivity is a multiscale spatiotemporal problem that requires multiscale solutions. In Vietnam, rice comprises a substantial portion of the cultivated area and is a major export crop that supplies much of the global food system. Understanding the when and where of rice productivity is vital to addressing changes to yields and food security, yet descriptive summarizations will vary depending on the spatial or temporal scale of analysis. This paper explores rice trends across Vietnam over a 19-year period, giving specific attention to modifiable spatiotemporal unit problems by evaluating productivity across multiple time periods and administrative levels. A generalizable procedure and tools are offered for visualizing multiscale time-series remote sensing data in matrix and map form, not only to elucidate the effects of modifiable spatiotemporal unit problems, but also to demonstrate how these problems serve as a useful research framework. Remote sensing indices (e.g., LAI and EVI) were evaluated against national and provincial estimates across Vietnam during multiple crop production periods using the Pearson Correlation Coefficient (PCC) to establish a relationship. To overcome challenges posed by long-term observations masking emerging phenomena, time-series matrices and multi-spatial and multi-temporal maps were produced to show when, where, and how rice productivity across Vietnam is changing. Results showed that LAI and EVI are favorable indices for measuring rice agriculture in Vietnam. At the province scale, LAI compared to nationally reported production estimates reached a Pearson’s r of 0.960; 0.974 for EVI during the spring crop production period. For questions such as, “What portion of Vietnam exhibits a negative linear trend in rice production?”, the answer depends on how space and time are organized. At the province scale, 25.4% of Vietnam can be observed as exhibiting a negative linear trend; however, when viewed at the district scale, this metric rises to 45.7%. This research contributes to the discussion surrounding ontological problems of how agricultural productivity is measured and conveyed. To better confront how agriculture is assessed, adopting a multiscale framework can provide a more holistic view than the conventional single spatial or temporal selection.
... The NDVI is the most extensively employed index for the extraction of the biophysical properties of vegetation canopies in remote sensing applications, as it capitalizes on the spectral reflectance traits of vegetation, exhibiting high reflectivity in the near-infrared band and strong absorption in the red band [40][41][42]. VFC is quantified using the pixel dichotomy model, which postulates that the NDVI value for each pixel represents a blend of both vegetation and soil components [18,43]. The mathematical formulation for this estimation is outlined in Table 2. ...
In the coal-rich provinces of Shanxi, Shaanxi, and Inner Mongolia, the landscape bears the scars of coal extraction—namely subsidence and deformation—that disrupt both the terrain and the delicate ecological balance. This research delves into the transformative journey these mining regions undergo, from pre-mining equilibrium, through the tumultuous phase of extraction, to the eventual restoration of stability post-reclamation. By harnessing a suite of analytical tools, including sophisticated remote sensing, UAV aerial surveys, and the meticulous ground-level sampling of flora and soil, the study meticulously measures the environmental toll of mining activities and charts the path to ecological restoration. The results are promising, indicating that the restoration initiatives are effectively healing the landscapes, with proactive interventions such as seeding, afforestation, and land rehabilitation proving vital in the swift ecological turnaround. Remote sensing technology, in particular, emerges as a robust ally in tracking ecological shifts, supporting sustainable practices and guiding ecological management strategies. This study offers a promising framework for assessing geological environmental shifts, which may guide policymakers in shaping the future of mining rehabilitation in arid and semi-arid regions.
... We used remote-sensing data to assess how the PPM defoliation event impacted canopy greenness and tree cover. For the period 1990-2021, we calculated the Normalized Difference Vegetation Index (NDVI), which is based on how healthy green vegetation differently reflects red and near-infrared radiation (Tucker, 1979), and the kernel NDVI (KNDVI), a non-linear version of the NDVI which is more accurate in detecting changes in leaf area index (Camps-Valls et al., 2021). We also considered the Normalized Difference Infrared Index (NDII; see Hunt and Rock, 1989) since it was found to detect severe defoliation due to PPM outbreaks (Sangüesa-Barreda et al., 2014). ...
Assessing and reconstructing the impacts of defoliation caused by insect herbivores on tree growth, carbon budget and water use, and differentiating these impacts from other stresses and disturbances such as droughts requires multi-proxy approaches. Here we present a methodological framework to pinpoint the impacts of pine processionary moth (Thaumetopoea pityocampa), a major winter-feeding defoliator, on tree cover (remote-sensing indices), radial growth and wood features (anatomy, density, lignin/carbohydrate ratio of cell walls, δ¹³C and δ¹⁸O of wood cellulose) of drought-prone pine (Pinus nigra) forests in north-eastern Spain. We compared host defoliated (D) and coexisting non-defoliated (ND) pines along with non-host oaks (Quercus faginea) following a strong insect outbreak occurring in 2016 at two climatically contrasting sites (cool-wet Huesca and warm-dry Teruel). Changes in tree-ring width and wood density were analyzed and their responses to climate variables (including a drought index) were compared between D and ND trees. The Normalized Difference Infrared Index showed reductions due to the outbreak of –47.3% and –55.6% in Huesca and Teruel, respectively. The D pines showed: a strong drop in growth (–96.3% on average), a reduction in tracheid lumen diameter (–35.0%) and lower lignin/carbohydrate ratios of tracheid cell-walls. Both pines and oaks showed synchronous growth reductions during dry years. In the wet Huesca site, lower wood δ¹³C values and a stronger coupling between δ¹³C and δ¹⁸O were observed in D as compared with ND pines. In the dry Teruel site, the minimum wood density of ND pines responded more negatively to spring drought than that of D pines. We argue that multi-proxy assessments that combine several variables have the potential to improve our ability to pinpoint and reconstruct insect outbreaks using tree-ring data.
... Among the several potential indices available, this study considered some of the commonly used indices that can potentially help to distinguish LULC classes such as between different vegetation types, bareland and urban areas, and water body. These were the normalized difference vegetation index (NDVI) (Tucker, 1979), modified normalized difference water index (MNDWI) (Xu, 2006), normalized difference build-up indices (NDBuI), and modified soil adjusted vegetation index (MSAVI) (Li et al., 2017, Rasul et al., 2018. Their formula is given from Eqs. (1) to (4) respectively. ...
... To identify possible early warning signals of forest dieback, several satellite indices have been calculated for the area. First, the Normalized Difference Vegetation Index (NDVI) was computed (Tucker, 1979), which is widely used to assess the health of tree vegetation through changes in canopy cover (Sun et al., 2022;Wang et al., 2021). This index is calculated as: ...
Climate change is expected to affect forests’ growth and functioning and to increase their vulnerability to stressors such as prolonged drought and pest outbreaks. Identifying vulnerable forest stands and predicting tree decline is critical for timely management interventions to preserve forests integrity and associated ecosystem services. This study combined dendrochronological and isotopic analyses with satellite remote sensing to detect early warning signs of forest decline in a Pinus pinea L. stand in southern Italy affected by one of the first outbreak of the pine parasite Toumeyella parvicornis reported in Italy. Furthermore, through a comparative study of the analysis techniques, this research aimed to identify the most effective data processing strategies for detecting tree dieback of this species. Satellite analysis revealed a gradual decline in vegetation indices (NDVI, EVI, EVI2) of the stand from 2015 onwards, which coincided with the onset of defoliation due to the pest outbreak. The decline in defoliation intensified in 2020, leading to a severe tree carbon deficit and subsequent mortality of the pine stand in 2023. In comparison, indices such as EVI and EVI2 have been shown to be more sensitive than NDVI in detecting changes in canopy cover. The inclusion of the NDMI index provided important information on the moisture dynamics of the stand. Dendrochronological analyses complemented remote sensing data: a strong decrease in growth was observed from 2020 onwards, undemanding a tipping point for the Pinus pinea stand, which led to tree mortality in 2023. The study highlighted the higher sensitivity of detrended chronologies such as BAI and TRW-I in detecting signs of forest dieback compared to raw tree-ring data. Moreover, intrinsic water use efficiency (WUEi) analysis provided insight into the eco-physiological dynamics underlying pine tree decline, revealing lower tree water retention induced by defoliation. Finally, correlations between growth and WUEi data with meteorological variables highlighted how defoliation increased the vulnerability of trees to the effects of climate, influencing their ability to recover after the pest attack. In conclusion, the combination of these analysis methods provided a detailed and comprehensive overview of tree species dieback due to new invasive pest. Our findings providing valuable insights into the eco-physiological dynamics and early detection of signs of tree decline, useful for planning effective forest management strategies to counteract the diffusion of Toumeyella parvicornis across Italy and Europe.
... Ngoài ra, trong lĩnh vực nông nghiệp chỉ số NDVI cũng góp phần trong đánh giá sự phát triển của cây trồng, dự báo năng suất. Chỉ số NDVI được tính theo công thức [12]: ...
Vegetation indicators based on remote sensing imaging material is an important criterion in assessing the health, structure and stability of vegetation. In remote sensing images, images obtained from unmanned aerial vehicles (UAVs) have many advantages, such as high resolution, proactive flight time, and minimizing atmospheric impacts, which are important sources of material in assessing the structure of forest vegetation. In particular, the development of UAVmounted
cameras is improving, enhancing the spectral ranges so that researchers can
identify a variety of plant indicators. In this study, the Phantom 4 Multispectral
UAV was used with 6 independent cameras attached, including 5 monochrome wave ranges, including blue (Rb): 450±16 nm, green (Rg): 560±16 nm, red (Rr): 650±16 nm, red edge (Rre): 730±16 nm, and near-infrared (Rnir): 840±26 nm, which
allowed us to identify most vegetation indicators in the forest area. The results
analysed 5 types of vegetation indicators, including NDVI, GNDVI, SAVI, EVI and
GCI, for forests in the study area. Indicators indicate that the forest vegetation here
is stable, the canopy layer has high coverage, belongs to medium and rich forest
types in the Central Highlands region. In addition, analysis of the correlation
between vegetation index forms at 30 points in the study area has shown that the
image obtained from the UAV has great advantages when applied to the
identification of plant indicators, with high similarity, limiting the influence from the
atmosphere (the correlation coefficient reaches ≥ 0.74). This is an important basis
for expanding the application of UAVs in forest ecology research, identifying their
structure and fluctuations over periods, as the basis for the planning and
conservation and sustainable development of forest resources.
... There are five commonly used visible-light vegetation indices: the red-to-green ratio index (RGRI) [44], excess green index (EXG) [45,46], visible light band difference vegetation index (VDVI) [47][48][49], normalized green-red difference index (NGRDI) [50], and normalized green-blue difference index (NGBDI) [51]. Among them, the VDVI has the highest overall accuracy rate of 97.47% [40], which indicates that the visible-wave vegetation index can be used to calculate vegetation coverage and has the highest applicability. ...
To achieve the transition of rural areas from traditional to modern, the visualization of rural landscape data and feature evaluations are essential. Landscape character assessment (LCA) is a well-established tool that was developed to assess and understand rural landscape features. In recent years, drones have become increasingly attractive for various applications and services due to their low costs and relative ease of operation. Unlike most previous studies that relied solely on drone-based remote sensing or visual esthetic evaluations, this study proposes an innovative assessment method based on landscape characteristic assessment (LCA) and oblique drone photography technology, supported by specific data and survey results. These include various landscape metrics, such as the Shannon diversity index (SHDI), Shannon evenness index (SHEI), vegetation coverage, landscape character zoning, and delineations of various ecologically sensitive areas. This method was applied to study Zhanqi Village in Chengdu, Sichuan Province, China and revealed some unique characteristics of this village. By categorizing and describing the landscape features, the study makes judgments and decisions about them. This is a beneficial attempt to apply the scientific methods of landscape assessments to the production management of aerial drone surveys. This method provides a comprehensive framework for evaluating rural landscape features and demonstrates that the combination of LCA and oblique drone photography technology is feasible for rural landscape research. Additionally, this study emphasizes the need for further research to explore the potential application of this method in continuously evolving urban and rural environments in the future.
... The visible region typically exhibits low reflectance due to pigment absorption of chlorophyll, while the NIR has relatively high reflectance as individual leaves and whole plant canopies strongly scatter NIR energy. Early research illustrated that the difference in low visible and high NIR reflectance could be significantly related to various properties of plant canopy "greenness" (Tucker, 1979). This relationship forms the foundation for VIs (e.g., NDVI) that leverage the contrast between the visible and NIR regions to extract valuable vegetation information. ...
Stress caused by high temperatures is a critical limiting factor of crop growth and development. Although remote sensing has been used to investigate the impacts of high temperatures on crops, its ability to detect heat stress independently of other stressors and assess its effects on gross primary production (GPP) estimation is unclear. This study developed an innovative approach to distinguish crop heat stress periods from normal growth conditions in croplands independent of water stress and light limitation. Multispectral broad bands and spectral vegetation indices (VIs) derived from MODIS for 78 periods of heat stress were used to assess the sensitivity of canopy reflectance to heat stress and its impacts on GPP. Results reveal that heat stress significantly increased the reflectance in the red band. VIs, in general, enhanced the detection of heat-induced spectral variations, and exhibited sufficient skill in distinguishing crops under heat stress and normal conditions. Three visible-based indices (the Visible Atmospherically Resistant Index, the Green Leaf Index, and the Normalized Green–Red Difference Index) exhibited the highest discriminability (p-value < 0.01 in the Mann–Whitney U test), while the Enhanced Vegetation Index displayed the highest accuracy in GPP estimation (R2 = 0.62, RMSE = 5.49, RRMSE = 0.35) under heat conditions. Overall, the isolation of heat stress impact on crop growth has important implications for advancing large-scale crop modeling and climate change studies, for example, incorporating the suggested VIs into temperature response simulations within crop models.
... Over the past 50 years, remote sensing techniques have proven their usefulness in monitoring and analysing vegetation dynamics in different spatial scales. The normalized difference vegetation index (NDVI), derived from remotely sensed data, is widely employed to assess vegetation health and productivity at regional, continental and global scales, and over the time (Pettorelli et al., 2005;Tucker, 1979). NDVI, being influenced by canopy density, biomass, and chlorophyll content, serves as an indicator of vegetation cover and vigour, and by inference, be a measure of soil productivity of which soil depth is a factor. ...
... Based on the ecosystem types in this study, we selected the following spectral indices: Normalized Difference Vegetation Index (NDVI) [40], Enhanced Vegetation Index (EVI) [41,42], Modified Soil Adjusted Vegetation Index (MSAVI) [43], Spectral Variability Vegetation Index (SVVI) [44], Normalized Difference Built-Up Index (NDBI) [45], Modified Normalized Difference Water Index (MNDWI) [46], Wet Index (WET) [47,48] and Salinity index (SIT) [49,50]. The calculation formulae and significance for these indices are shown in Table 3. ...
Drylands are characterized by unique ecosystem types, sparse vegetation, fragile environments, and vital ecosystem services. The accurate mapping of dryland ecosystems is essential for their protection and restoration, but previous approaches primarily relied on modifying land use data derived from remote sensing, lacking the direct utilization of latest remote sensing technologies and methods to map ecosystems, especially failing to effectively identify key ecosystems with sparse vegetation. This study attempts to integrate Google Earth Engine (GEE), random forest (RF) algorithm, multi-source remote sensing data (spectral, radar, terrain, texture), feature optimization, and image segmentation to develop a fine-scale mapping method for an ecologically critical area in northern China. The results showed the following: (1) Incorporating multi-source remote sensing data significantly improved the overall classification accuracy of dryland ecosystems, with radar features contributing the most, followed by terrain and texture features. (2) Optimizing the features set can enhance the classification accuracy, with overall accuracy reaching 91.34% and kappa coefficient 0.90. (3) User’s accuracies exceeded 90% for forest, cropland, and water, and were slightly lower for steppe and shrub-steppe but were still above 85%, demonstrating the efficacy of the GEE and RF algorithm to map sparse vegetation and other dryland ecosystems. Accurate dryland ecosystems mapping requires accounting for regional heterogeneity and optimizing sample data and feature selection based on field surveys to precisely depict ecosystem patterns in complex regions. This study precisely mapped dryland ecosystems in a typical dryland region, and provides baseline data for ecological protection and restoration policies in this region, as well as a methodological reference for ecosystem mapping in similar regions.
... • Normalized Difference Vegetation Index (NDVI): The NDVI "is the primary vegetation index for monitoring crop conditions" [9]. It is widely used due to its ability to measure photosynthesis activity and thus correlate with vegetation density and vitality [43,44]. The NDVI is derived from satellite imagery in the visible and near-infrared (VNIR) parts of the electromagnetic spectrum. ...
This article presents a low-cost insurance system developed for smallholder farms in disaster-prone regions, primarily using free Earth observation (EO) data and free open source software’s (FOSS), collectively termed “sustainable geoinformatics.” The study examined 30 farms in Risaralda Department, Colombia. A digital elevation model (12.5 m pixels) from the ALOS PALSAR satellite sensor was used with a geographic information system (GIS) to map the terrain, drainage, and geohazards of each farming district. Google Earth Engine (GEE) was used to carry out timeseries analysis of 15 EO and weather datasets for 1998 to 2020. This analysis enabled the levels of risk from hydrometeorological hazards to be determined for each farm of the study, providing key data for the setting of insurance premiums. A parametric insurance product was developed using a proprietary mobile phone app that collected GPS-tagged, time-stamped mobile phone photos to verify crop damage, with further verification of crop health also provided by daily near-real-time satellite imagery (e.g., PlanetScope with 3 m pixels). Machine learning was used for feature identification with the photos and the satellite imagery. Key features of this insurance system are its low operational cost and rapid damage verification relative to conventional approaches to farm insurance. This relatively fast, low-cost, and affordable approach to insurance for small-scale farming enhances sustainable development by enabling policyholder farmers to recover more quickly from disasters.
... Hasil perhitungan NDVI sangat dipengaruhi oleh faktor atmosfer, tanah dan komponen piksel sehingga pola perilaku pada analisis NDVI akan menjadi rumit karena respon spektral dari kedua pita spektral yang digunakan tidak memiliki respon yang sama terhadap faktor-faktor tersebut (Huang et al., 2021). Persamaan yang digunakan dalam menganalisis NDVI yaitu menurut Tucker (1979), sebagai berikut: ...
Estimation of Canopy Cover Based on NDVI and Condition of Canopy Closure in the Mangrove Ecosystem of Passo Village, Inner Ambon Bay Mangrove canopy cover plays an important role in maintaining and protecting environmental stability in coastal areas, namely as a habitat for various terrestrial species and as protection from direct exposure to ultraviolet light on associated aquatic organisms. Mangrove canopy cover is important in protecting coastal areas from strong winds. This research aims to analyze the condition of the mangrove area, the condition of canopy cover based on the NDVI value and the percentage value of canopy closure, and the relationship between NDVI and percentage canopy closure. Sentinel-2B image data was processed using QGIS 3.28.13 and ArcGIS 10.8 software. Maximum likelihood classification is used to separate mangrove delineation from other objects, and an accuracy test is carried out using a confusion matrix to test the accuracy of the classification results. Observations were determined using a purposive sampling method, and canopy closure data was collected using a simple hemispherical photography method. The condition of canopy cover was analyzed based on NDVI calculations and calculation of the percentage of canopy closure, which was carried out using image-j software and Microsoft Excel 2010. The analysis showed that ten types of mangroves included seven families and eight genera with a mangrove area of 21.66 ha. 62.70% of the mangrove area has a canopy cover condition based on the NDVI value, which is very dense. Also, 50% of observation stations have a canopy cover condition based on the canopy cover percentage value, which is still classified as dense. The correlation between the NDVI value and the percentage value of mangrove canopy cover has a unidirectional relationship and a very strong relationship with a correlation coefficient r of 0.949. Abstrak Tutupan Kanopi mangrove sangat berperan penting dalam menjaga dan melindungi kestabilan lingkungan pada wilayah pesisir, yaitu sebagai habitat bagi berbagai macam spesies terestrial dan juga sebagai pelindung dari terpaan sinar ultraviolet secara langsung terhadap organisme perairan yang berasosiasi, tutupan kanopi mangrove juga sangat berperan penting dalam menjaga dan melindungi wilayah pesisir dari terpaan angin kencang. Penelitian ini bertujuan untuk menganalisis kondisi luasan mangrove, kondisi tutupan kanopi berdasarkan nilai NDVI dan nilai persentase tutupan tajuk mangrove, serta menganalisis hubungan NDVI dan persentase tutupan tajuk. Pengolahan data citra Sentinel-2B dilakukan dengan menggunakan software QGIS 3.28.13 dan ArcGIS 10.8. klasifikasi maximum likelihood digunakan untuk memisahkan delineasi mangrove dengan objek lainnya dan dilakukan uji akurasi dengan menggunakan confusion matrix untuk menguji keakuratan hasil klasifikasi. Penentuan stasiun pengamatan dilakukan dengan menggunakan metode purposive sampling serta pengambilan data tutupan tajuk dilakukan dengan menggunakan metode hemispherical photography sederhana. Kondisi tutupan kanopi dianalisis berdasarkan perhitungan NDVI dan perhitungan persentase tutupan tajuk mangrove yang dilakukan dengan menggunakan bantuan software image-j dan Microsoft Excel 2010. Hasil analisis menunjukkan bahwa dapat ditemukan 10 jenis mangrove yang terdiri dari 7 famili dan 8 genera dengan luasan mangrove sebesar 21,66 ha. 62,70% luasan mangrove memiliki Kondisi tutupan kanopi berdasarkan nilai NDVI tergolong sangat padat dan 50% stasiun pengamatan memiliki kondisi tutupan kanopi berdasarkan nilai persentase tutupan tajuk masih tergolong padat. Hubungan korelasi antara nilai NDVI dan nilai persentase tutupan tajuk mangrove memiliki hubungan korelasi searah dan memiliki hubungan yang sangat kuat dengan koefisien korelasi r sebesar 0,949. Kata kunci : Mangrove, Kanopi, NDVI, Hemispherical, Korelasi PENDAHULUAN Ekosistem mangrove merupakan salah satu ekosistem penting yang berada pada wilayah pesisir. Keberadaan ekosistem mangrove pada wilayah pesisir memiliki peranan yang sangat
... Particularly, the REP_Li indice plays a critical role in the model's performance, indicating that it contains information crucial for prediction outcomes. This discovery underscores the importance of selecting appropriate spectral indices to enhance model accuracy.The results are shown in Table 15 The comparison of the five stage-optimal chlorophyll prediction models is shown in Figure 7, and the errors of the [17] R 800 (1/R 550 − 1/R 700 ) CCI [18] (R 777 −R 747 /R 673 RI db [19] R 735 /R 720 Datt1 [20] R 672 / (R 550 * R 708 ) Datt2 [20] R 672 /R 550 Datt3 [21] R 850 /R 710 Datt4 [21] (R 850 −R 710 / (R 850 −R 680 EVI [22] 2.5 (R 800 −R 670 / (R 800 −6R 670 − 7.5R 475 + 1 ) GNDVI 1 [23] (R 750 −R 550 / (R 750 +R 550 GNDVI 2 [23] (R 800 −R 550 / (R 800 +R 550 MCARI 1 [24] (R 700 −R 670 − 0.2 (R 700 −R 550 )) R 700 /R 670 MCARI 2 [25] 1. [29] (R 762 −R 527 / (R 762 +R 527 ND [30] (R 565 −R 735 / (R 565 +R 735 NDVI 1 [31] (R 800 −R 670 / (R 800 +R 670 NDVI 2 [32] (R 750 −R 705 / (R 750 +R 705 NDVI 3 [33] (R 682 −R 553 / (R 682 +R 553 OSAVI 1 [34] (1 + 0.16) (R 800 −R 670 / (R 800 +R 670 + 0.16 OSAVI 2 [26] (1 + 0.16) (R 750 −R 705 / (R 750 +R 705 + 0.16 PRI [35] (R 531 −R 570 / (R 531 +R 570 PSRI [36] (R 678 −R 500 /R 750 SPVI 1 [37] 0.4 × 3.7 (R 800 − R 670 ) − 1.2 |R 530 − R 670 | SPVI 2 [38] 0.4 × 3.7 (R 800 − R 670 ) − 1.2 |R 550 − R 670 | SR1 [39] R 605 /R 760 SR2 [39] R 695 /R 760 SR3 [39] R 710 /R 760 SR4 [39] R 695 /R 670 SR5 [40] R 675 /R 700 SR6 [41] R 750 /R 550 SR7 [41] R 750 /R 700 SR8 [41] R 752 /R 690 SR9 [42] R 700 /R 670 SR10 [43] R 740 /R 720 SR11 [44] R 750 /R 710 SR12 [30] R 565 /R 740 FD-SNV-SAA-RFR and VI-SPA-RFR models after dimensionality reduction are smaller than those of the models before dimensionality reduction, so the dimensionality reduction algorithms are essential in the process of analysing the hyperspectral data, and among them, the VI-SPA-RFR model has the highest accuracy, and it is the most suitable analytical model for the prediction of chlorophyll of ginseng leaves in the present study. ...
Rapid and accurate monitoring of the chlorophyll content of ginseng leaves to reflect the growth condition of ginseng is of great significance in guiding the cultivation of ginseng. To address the problem that the existing means of detecting chlorophyll in ginseng leaves are time-consuming and disruptive and cannot meet the demand for rapid detection of chlorophyll in ginseng leaves, this study firstly establishes a variety of prediction models for chlorophyll in ginseng leaves based on the hyperspectral reflectance data and the vegetation index, respectively, and determines the strengths and weaknesses of the models by comparing the RMSE and the MAE of the test sets; Secondly, the analytical model construction process used for ginseng leaf chlorophyll content prediction was obtained through comparison and summary, and a fast and non-destructive ginseng leaf chlorophyll prediction method based on hyperspectral imaging technology and combining vegetation indices with machine learning algorithms was proposed; The experimental results showed that the final VI-SPA-RFR ginseng leaf chlorophyll prediction model had the best prediction performance, which had an RMSE of 1.1568 and an MAE of 0.9936 in the test set. In summary, this study provides a method to achieve rapid and accurate monitoring of the chlorophyll content of ginseng leaves, which is expected to provide a scientific means of monitoring for the cultivation of ginseng and expand the application field of hyperspectral imagers in the field of agriculture.
... First, we used the NDVI. This index represents vegetation greenness based on surface spectral reflectance (Tucker, 1979). With values ranging between − 1 and +1, higher positive NDVI values represent more greenness. ...
... Os índices de vegetação baseiam-se nas diferenças de refletividade que a vegetação de cor verde apresenta nas regiões do visível e do infravermelho (JENSEN, 2011), com variação de -1 a 1. Os maiores valores dos índices de vegetação são encontrados em áreas com presença de vegetação, enquanto os menores valores ocorrem em áreas de solo descoberto (CUNHA et al., 2015;MARIANO et al., 2018;ZHAO et al., 2020). Dessa forma, para cada imagem MSI -Sentinel 2 foram processados os índices: NDVI (ROUSE JR. et al., 1973;TUCKER, 1979) e SAVI (HUETE, 1988), com o intuito de comparar as respostas espectrais dos índices às mudanças na paisagem a partir da variação da distribuição espacial dos pixels, para as classes: mata ciliar (vegetação) e reservatório (água), através de cartas imagens e análises estatísticas por representações gráficas, correlação de Pearson (α) e coeficiente de determinação (R²). ...
As matas ciliares são regiões ambientais que contribuem para a purificação da água, e reduzem a vulnerabilidade às inundações. Este estudo objetivou analisar as variações sazonais da cobertura vegetal da Caatinga nas proximidades do Reservatório de Serrinha II - PE, Brasil. O conjunto de dados orbitais foi composto por imagens do satélite MSI - Sentinel 2, de 2016 a 2021, entre os períodos: chuvoso e seco. Assim, avaliaram-se as respostas espectrais dos índices: Normalized Difference Vegetation Index (NDVI) e Soil Adjusted Vegetation Index (SAVI), correlacionados com o Standardized Precipitation Index (SPI) e a variação volumétrica do reservatório. Os resultados evidenciaram que os índices indicaram crescimento da vegetação no período chuvoso e redução no período seco. De 2016 a 2018 foi identificado, ao correlacionar o SPI com os índices, a redução da vegetação devido a eventos de seca. De 2019 a 2021 tem-se um estágio de regeneração com um aumento positivo dos valores de SPI. Esses eventos também proporcionaram a redução de volume acumulado do reservatório, que apresentou o menor valor em 2017 (11,66 hm³). Com isso, a associação do SPI aos índices possibilitou a identificação de mudanças na cobertura vegetal e no volume do reservatório, ocasionadas por déficit pluviométrico.
... Specifically, to monitor vegetation conditions, we produced a monthly NDVI product using Landsat imagery for the Los Planes basin for all months across the study period. The NDVI is a metric of vegetation greenness and overall plant photosynthetic activity [104], and is typically representative of vegetation activity [105]. NDVI values closer to 0 generally align with rock and barren soil, while values closer to 1 generally represent dense vegetation cover. ...
Estimates of actual evapotranspiration (ETa) are valuable for effective monitoring and management of water resources. In areas that lack ground-based monitoring networks, remote sensing allows for accurate and consistent estimates of ETa across a broad scale—though each algorithm has limitations (i.e., ground-based validation, temporal consistency, spatial resolution). We developed an ensemble mean ETa (EMET) product to incorporate advancements and reduce uncertainty among algorithms (e.g., energy-balance, optical-only), which we use to estimate vegetative water use in response to restoration practices being implemented on the ground using management interventions (i.e., fencing pastures, erosion control structures) on a private ranch in Baja California Sur, Mexico. This paper describes the development of a monthly EMET product, the assessment of changes using EMET over time and across multiple land use/land cover types, and the evaluation of differences in vegetation and water distribution between watersheds treated by restoration and their controls. We found that in the absence of a ground-based monitoring network, the EMET product is more robust than using a single ETa data product and can augment the efficacy of ETa-based studies. We then found increased ETa within the restored watershed when compared to the control sites, which we attribute to increased plant water availability.
Context: Crop growth models were widely applied for simulating the dynamic growth of crops at multi-scales. The data assimilation by integrating the remote sensing data retrieved crop parameters and crop models have showed great potentials for describing the crop growth and assessing the agricultural yields. Objective: The purpose of this study was to integrates sequential observations of crop phenotyping traits from Unmanned Aerial Vehicles (UAV) remote sensing into World Food Studies (WOFOST) model to improve the simulation of crop growth processes. Methods: Two years of Leaf Area Index (LAI) of summer maize retrieved from Unmanned Aerial Vehicles (UAV)-RGB images was assimilated into the WOFOST using the Ensemble Kalman Filter (EnKF). The sensitive crop parameters of WOFOST were firstly identified using the Extended Fourier Amplitude Sensitivity Test (EFAST) global sensitivity analysis approach, and then the parameters were adjusted and confirmed using the SUBPLEX optimization algorithm. The LAI data was assimilated into WOFOST model using EnKF by minimizing the differences between the UAV-retrieved LAI and crop-simulated LAI. Results indicated assimilating LAI into WOFOST model significantly improved the accuracy of maize yield prediction. Results: Compared with non-assimilation, data assimilation reduced the Root Mean Square Error (RMSE) from 413 to 132 kg/ha for 2020, and from 392 to 215 kg/ha for 2021, respectively. Through the effects of different ensemble size and different time-point for data assimilation, it was obtained that the accuracy of yield prediction achieved the highest when ensemble size was 100 at reproductive growth stage. Conclusions: Integrating UAV-based crop traits into WOFOST model using data assimilation (EnKF) could effectively improve the maize yield accuracy.
Exploring the influence of green space characteristics and proximity on health via air pollution mitigation, our study analysed data from 1,365 participants across Porto, Nantes, Sofia, and Høje-Taastrup. Utilizing Open-StreetMap and the AID-PRIGSHARE tool, we generated nine green space indicators around residential addresses at 15 distances, ranging from 100m to 1500m. We performed a mediation analysis for these 135 green space variables and revealed significant associations between self-rated air pollution and self-rated health for specific green space characteristics. In our study, indirect positive effects on health via air pollution were mainly associated with green corridors in intermediate Euclidean distances (800-1,000m) and the amount of accessible green spaces in larger network distances (1,400-1,500m). Our results suggest that the amount of connected green spaces measured in intermediate surroundings seems to be a prime green space characteristic that could drive the air pollution mitigation pathway to health.
In response to the pressing challenges of the ongoing biodiversity crisis, the protection of endangered species and their habitats, as well as the monitoring of invasive species are crucial. Habitat suitability modeling (HSM) is often treated as the silver bullet to address these challenges, commonly relying on generic variables sourced from widely available datasets. However, for species with high habitat requirements, or for modeling the suitability of habitats within the geographic range of a species, variables at a coarse level of detail may fall short. Consequently, there is potential value in considering the incorporation of more targeted data, which may extend beyond readily available land cover and climate datasets. In this study, we investigate the impact of incorporating targeted land cover variables (specifically tree species composition) and vertical structure information (derived from LiDAR data) on HSM outcomes for three forest specialist bat species (Barbastella barbastellus, Myotis bechsteinii, and Plecotus auritus) in Rhineland‐Palatinate, Germany, compared to commonly utilized environmental variables, such as generic land‐cover classifications (e.g., Corine Land Cover) and climate variables (e.g., Bioclim). The integration of targeted variables enhanced the performance of habitat suitability models for all three bat species. Furthermore, our results showed a high difference in the distribution maps that resulted from using different levels of detail in environmental variables. This underscores the importance of making the effort to generate the appropriate variables, rather than simply relying on commonly used ones, and the necessity of exercising caution when using habitat models as a tool to inform conservation strategies and spatial planning efforts.
Accurate monitoring of leaf phenology, from individual trees to entire ecosystems, is vital for understanding and modeling forest carbon and water cycles, as well as assessing climate change impact. However, the accuracy of many remote-sensing phenological products remains difficult to directly corroborate using ground-based monitoring, owing to variations in the observed indicators and the scales used. This limitation hampers the practical implementation of remote-sensing phenological metrics. In our study, the start of growing season (SOS) from 2016 to 2021 was estimated for the continental USA using Sentinel-2 images. The results were then matched with several ground-based spring vegetation phenology metrics obtained by the USA National Phenology Network (USA-NPN). In this study, we focused on the relationships between the leaf-unfolding degree (LUD), the SOS, and the factors that drive these measures. Our results revealed that: (1) the ground-based leaves and increasing leaf size stages were significantly correlated with the SOS; (2) with the closest match being observed for a leaf spread of 13%; (2) the relationship between the SOS and LUD varied according to the species and ecoregion, and the pre-season cumulative radiation was found to be the main factor affecting the degree of matching between the ground observations and the metrics derived from the Sentinel-2 data. Our investigations provide a ground-based spring phenology metric that can be used to verify or evaluate remote-sensing spring phenology products and will help to improve the accuracy of remote-sensing phenology metrics.
Increases in organic carbon within agricultural soils are widely recognized as a “negative emission” that removes CO2 from the atmosphere. Accurate quantification of soil organic carbon (SOC) to a certain depth in the spatial domain is critical for the effective implementation of improved land management practices in croplands. Currently, there is a lack of understanding regarding what depth strategy should be used to estimate SOC at 0–30 cm when sample datasets come from multiple depths. Furthermore, few studies have examined depth strategies for mapping SOC at the agricultural management level (i.e., field level), opting instead for point-based analysis. Here, three types of approaches with different depth strategies were evaluated for their ability to quantify 0–30 cm SOC content based on soil samples from 0–5 (surface), 5–30 (subsurface), and 0–30 cm (full column). These approaches involved the generalized additive model and machine learning techniques, i.e., artificial neural networks, random forest, and XGBoost. The soil samples used for the model evaluation and selection consisted of the newly collected samples in 2020–2022 and the Rapid Carbon Assessment (RaCA) legacy samples collected in 2010–2011. Environmental covariates corresponding to these SOC measurements were used in model training, including long-term physical climate, short-term weather, topographic and edaphic, and remotely sensed variables. Among the models evaluated in this study, the XGB regression model with a full column depth assignment strategy yielded the best prediction performance for 0–30 cm SOC content, with an r² (squared Pearson correlation coefficient) of 0.48, an RMSE (root mean square error) of 0.29%, an ME (mean error) of 0.06%, an MAE of 0.25%, and an MEC (modeling efficiency coefficient) of 0.36 at the pixel level and an r² of 0.64, an RMSE of 0.32%, an ME of −0.20%, an MAE of 0.28%, and an MEC of 0.48 at the field level. This study highlights that machine learning models with a full column depth strategy should be used to quantify 0–30 cm SOC content in agricultural soils over the continental United States (CONUS).
Chlorophyll, as a key component of crop leaves for photosynthesis, is one significant indicator for evaluating the photosynthetic efficiency and developmental status of crops. Fractional-order differentiation (FOD) enhances the feature spectral information and reduces the background noise. In this study, we analyzed hyperspectral data from grape leaves of different varieties and fertility periods with FOD to monitor the leaves’ chlorophyll content (LCC). Firstly, through sensitive analysis, the fractional-order differential character bands were identified, which was used to construct the typical vegetation index (VI). Then, the grape LCC prediction model was built based on the random forest regression algorithm (RFR). The results showed the following: (1) FOD differential spectra had a higher sensitivity to LCC compared with the original spectra, and the constructed VIs had the best estimation performance at the 1.2th-order differential. (2) The accuracy of the FOD-RFR model was better than that of the conventional integer-order model at different fertility periods, but there were differences in the number of optimal orders. (3) The LCC prediction model for whole fertility periods achieved good prediction at order 1.3, R2 = 0.778, RMSE = 2.1, and NRMSE = 4.7%. As compared to the original reflectance spectra, R2 improved by 0.173; RMSE and NRMSE decreased, respectively, by 0.699 and 1.5%. This indicates that the combination of FOD and RFR based on hyperspectral data has great potential for the efficient monitoring of grape LCC. It can provide technical support for the rapid quantitative estimation of grape LCC and methodological reference for other physiological and biochemical indicators in hyperspectral monitoring.
Innovations in precision agriculture enhance complex tasks, reduce environmental impact, and increase food production and cost efficiency. One of the main challenges is ensuring rapid information availability for autonomous vehicles and standardizing processes across platforms to maximize interoperability. The lack of drone technology standardisation, communication barriers, high costs, and post-processing requirements sometimes hinder their widespread use in agriculture. This research introduces a standardized data fusion framework for creating real-time spatial variability maps using images from different Unmanned Aerial Vehicles (UAVs) for Site-Specific Crop Management (SSM). Two spatial interpolation methods were used (Inverse Distance Weight, IDW, and Triangulated Irregular Networks, TIN), selected for their computational efficiency and input flexibility. The proposed framework can use different UAV image sources and offers versatility, speed, and efficiency , consuming up to 98 % less time, energy, and computing requirements than standard photogrammetry techniques, providing rapid field information, allowing edge computing incorporation into the UAV data acquisition phase. Experiments conducted in Spain, Serbia, and Finland in 2022 under the H2020 FlexiGroBots project demonstrated a strong correlation between results from this method and those from standard photo-grammetry techniques (up to r = 0.93). In addition, the correlation with Sentinel 2 satellite images was as strong as that obtained with photogrammetry-based orthomosaics (up to r = 0.8). The proposed approach could support irrigation leak detection, soil parameter estimation, weed management, and satellite integration for agriculture.
Globally, the attainment of the United Nations Sustainable Development Goals
(SDGs) established in 2015, especially poverty alleviation, zero hunger, clean water
and sanitation, decent work and economic growth, climate action, and life on land,
are both directly and indirectly associated with grasslands. The underlying reason
is that grasslands, also referred to as prairies, savannas, steppes, and meadows,
play a strategic role in sustaining diverse ecosystems and provide a wide array of
crucial benefits to the environment, wildlife, livestock, and human communities.
For instance, grasslands play an important role in carbon sequestration wherein the
grasses store bulk quantities of carbon in the soil through their deep root systems.
Grasslands are also significant in sustaining biodiversity (plants, birds, and animal
species), controlling floods, providing water through effective regulation of rainwater, and preventing erosion and improving infiltration and hotspots of recreational
activity such as hiking, birdwatching, and so on. Moreover, grasslands are precious
sources of feed for livestock, herbs for traditional medicines, and rearing spots for
honeybees. They also have cultural value in terms of spiritual and religious sites.
However, grasslands have remained quite vulnerable to invasive weed species, anthropogenic disturbances (e.g., land conversion for carrying out modern input-intensive
farming, uncontrolled grazing by livestock, etc.), and numerous types of climate
change. This book, Grasslands – Conservation and Development, has been tailored to
address these issues. It suggests practices for conserving grasslands that ensure the
protection and sustainable management of grassland ecosystems by maintaining the
biodiversity and ecological integrity for persistent provision of ecosystem services, as well as grassland development initiatives intended for improving the productivity and sustainability of grassland areas for agricultural purposes.
Vegetation is not only immediately affected by water flow, but also has a time-lag response to water elements accumulated in the soil of the wetland ecosystem. However, extensive studies focused on the direct response of vegetation to water regimen, the time-lag effects are less considered due to lacking appropriate quantitative methods. In this study, we aimed to quantitatively determine the response of wetland vegetation to two different forms of water. We extracted three vegetation patches using long-term Landsat data and constructed two kinds of hydrological variables (immediate and time-lag), analyzing their relationship. The results showed that:(1) different vegetation patches have different spatio-temporal dynamics over time. The distribution elevation of dense and high grass patches significantly reduces, and the ecological niche expands;(2) the temporal explicit pattern of wetland vegetation response to two hydrological variables are different: the sensitivity of the response to immediate hydrological variables is sparse grass patch>dense grass patch>high grass patch, and the response duration of time-lag is dense grass patch >sparse grass patch>high grass patch; compared to vegetation patches at high elevation, sparse grass patch at lower elevation is more susceptible to the accumulation of water regimen over time. Our study clarifies that wetland vegetation has a time-lag response to water regimen, which is a supplement to evaluating and predicting vegetation dynamics under wetland water regimen changes.
A network of sampling sites throughout the annual grassland region was established to correlate plant growth in stages and forage production to climatic and other environmental factors. Plant growth and range conditions were further related to geographic location and seasonal variations. A sequence of LANDSAT data was obtained covering critical periods in the growth cycle. Data were analyzed by both photointerpretation and computer aided techniques. Image characteristics and spectral reflectance data were then related to forage production, range condition, range site, and changing growth conditions.
A system analysis of remote sensing as a source of information has revealed the sources of many problems. More importantly, these analyses have shown methods for data preprocessing which should greatly improve results. The application of ERTS data to rangeland management problems was the ultimate goal of this research. Several noise sources (causing random fluctuations of radiance values) were identified as significantly degrading the quality of ERTS data. These included source (scene) noise, atmospheric propagation changes, radiometric errors, electronic system noise, data processing noise, and data analysis errors. Systems analysis suggested several data preprocessing methods to improve data quality. Multivariate analysis methods were used to evaluate variables, preprocessing results, and classification accuracy.
Results are presented for an investigation intended to evaluate various wavelengths and bandwidths, corresponding to simulated sensors, by integrating narrow bandwidth (0.005 micron) spectral reflectance curves of blue gramma grass plots for two sample periods, one early and one late in the growing season. Grass canopies were selected because they are morphologically one of the least complex vegetation types. Regression screening was used to evaluate the relationship between the various integrated reflectances and the plot variables in terms of coefficients of determination. Major conclusions are that the spectral regions of 0.37-0.50, 0.63-0.69, and 0.75-0.80 micron are statistically significant in a regression sense, and that inclusion of a bandwidth involving orange-red absorption and enhanced reflectance result in a near total degrading of any spectral sensitivity and should be avoided. An evaluation of Landsat channels is included.
Phenologic events which may be related to yields of economic plant species in humid and sub-humid temperate regions are difficult and expensive to observe and measure. Sequential multispectral reflectance data obtained by an orbiting satellite (LANDSAT-1) over 14 preselected sites in central and eastern United States during a 14-month period were examined. Analysis of data from four reflectance bands (0.5 to 1.1 μm) indicated that foliage color, leaf senescence, and regrowth differences among hardwood timber stands can be detected and quantified. The results suggest the need for continued development of these monitoring techniques for use in detecting and quantifying conditions of economic plants which may affect yield.
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The objective was to compare the infinite reflectance (R∞) of dead and live corn (Zea mays L.) leaves over the 0.5- to 2.5-μm waveband to find a means of distinguishing live from dead vegetation for remote sensing purposes; previous studies have been concerned only with R∞ of live leaves. In this study, for both dead and live leaves, reflectance measurements were made on a single leaf seciton and for sections stacked two, three, four, five, six, seven, and eight at a time over a spectrophotometer's port. Live leaf R∞ was essentially attained by stacking two leaves for the 0.5- to 0.75μm waveband (chlorophyll absorption region), eight leaves for the 0.75- to 1.35-μm waveband (near-infrared region), and three leaves for the 1.35- to 2.5-μm waveband (water absorption region). Dead leaf R∞ was reached over the entire 0.5- to 2.5-μm waveband by stacking only two or three leaves. Thus aircraft and spacecraft reflectance measuring techniques probably cannot distinguish density differences of dead vegetation but they should distinguish density differences of live vegetation. Near-infrared reflectance differences between dead and live vegetation also should be detectable with satellite multispectral scanner data.
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Possible cause-effect relationships in producing vegetation canopy reflectance are discussed. Hemispherical reflectance and even bidirectional reflectance measurements are shown to be inadequate to predict or understand vegetation canopy reflectance in many situations. Among the additional important parameters necessary for prediction and understanding of vegetation canopy reflectance are leaf hemispherical transmittance, leaf area and orientation, characteristics of other components of the vegetation canopy (stalks, trunks, limbs), soil reflectance, solar zenith angle, look angle, and azimuth angle. The effects of these parameters on vegetation canopy bidirectional spectral reflectance are described.
Leaf-area index of a forest can be measured by determining the ratio of light at 800 mμ to that at 675 mμ on the forest floor. It is based on the principle that leaves absorb relatively more red than infrared light, and therefore, the more leaves that are present in the canopy, the greater will be the ratio.
Analysis of in situ collected spectral reflectance data from a dormant or senescent grass canopy showed a direct relationship existed between spectral reflectance and biomass for the 0.50–0.80 μm spectral region. The data, collected four weeks after the end of the growing season, indicated that post senescent remote sensing of grass canopy biomass is possible and helps to elucidate the spectral contribution of recently dead vegetation in mixed live/dead canopy situations.
The non-Lambertian directional reflectance of a multilayer vegetative canopy is derived. Cause of the reflectance of the canopy is made traceable to the properties of the biological elements of the canopy. A new and possibly useful canopy property leading to the down sun “hot spot” is discussed.
Regression analysis of in situ spectral reflectance of a blue grama grass canopy sampled with approximately equal amounts of standing live and standing dead vegetation has identified spectral regions between 0.35 and 0.80 μm where the total wet biomass, total dry biomass, and leaf water content can be spectrally estimated. The amount of total wet or dry biomass was best estimated in the 0.35 to 0.44 μm region of the spectrum. This resulted from a combination of a relatively greater carotenoid than chlorophyll retention in the recent standing dead vegetation and of carotenoid and chlorophyll pigments present in the photosynthetically active or live vegetation. The leaf water content, highly related to the amount of photosynthetically active vegetation present, was best estimated in the regions. This resulted from strong chlorophyll absorption in the ∼0.46 to 0.50 and 0.63 to 0.69 μm regions and the high reflectance of living vegetation in the 0.74 to 0.80 μm region.
Red and photographic infrared data were collected with a hand-held radiometer under a variety of conditions at 4- to 12-day intervals throughout the growing season and were used to monitor corn and soybean growth and development. The normalized difference transformation was used to effectively compensate for the variation in irradiational conditions. With these data, plotted against time, green-leaf biomass dynamics were compared between the crops. By this approach, based entirely upon spectral inputs, the crop canopies were nondestructively monitored. Five spectral stages were defined and were related to crop development for corn and soybeans.
The asymptotic nature of grass canopy spectral reflectance has been evaluated from field experimental data collected over the wavelength region of 0.500-1.000 microm at 0.005-microm intervals. The spectral reflectance of green vegetation against a soil background decreases in regions of absorption and increases in regions of minimal or no absorption as the vegetational density increases until a stable or unchanging spectral reflectance, called the asymptotic spectral reflectance, is reached. Results indicated spectral reflectance asymptotes occurred at significantly lower levels of total wet biomass, total dry biomass, dry green biomass, chlorophyll content, and leaf water content in regions of strong pigment absorption (low detectability threshold) than in the photographic ir region where absorption was at a minimum (high detectability threshold). These findings suggested that photographic ir sensors were more suited to remote sensing of moderate to high biomass levels or vegetational density in a grass canopy than were sensors operating in regions of the spectrum where strong absorption occurred.
A simple hand-held instrument has been designed and constructed to nondestructively estimate above-ground gramineous biomass using radiometric measurements. The prototype unit consists of a modified two-channel digital radiometer interfaced to a pocket calculator. A digital interface was constructed to join electronically and control the radiometer and calculator to enable the radiometer-calculator system to solve a linear conversion solution from radiometric units to estimated biomass. This instrument has been used to estimate radiometrically gramineous biomass in a more efficient fashion with a high degree of accuracy.
A Monte Carlo model was used to predict the mean apparent directional reflectance of a simulated plant canopy and the covariance for seven wavelength channels in the visible portion of the spectrum. The non-Lambertian spectral response from Bouteloua gracilis canopies possessing two plant cover densities was simulated for two solar positions. The calculated spectral signatures as a function of look angle were then analyzed using the divergence criteria to select the best two wavelength channels for discrimination. These calculations indicate that different combinations of wavelength channels are appropriate for various sensor look angles, that target signatures have greater statistical separation for some scan angles than others, and that these effects are time varying.
Thesis--University of Michigan, 1973. Microfilm of typescript. Bibliography: leaves [172]-174.
In aircraft and satellite multispectral scanner data, soil background signals are superimposed on or intermingled with information about vegetation. A procedure which accounts for soil background would, therefore, make a considerable contribution to an operational use of Landsat and other spectral data for monitoring the productivity of range, forest, and crop lands. A description is presented of an investigation which was conducted to obtain information for the development of such a procedure. The investigation included a study of the soil reflectance that supplies the background signal of vegetated surfaces. Landsat data as recorded on computer compatible tapes were used in the study. The results of the investigation are discussed, taking into account a study reported by Kauth and Thomas (1976). Attention is given to the determination of Kauth's plane of soils, sun angle effects, vegetation index modeling, and the evaluation of vegetation indexes. Graphs are presented which show the results obtained with a gray mapping technique. The technique makes it possible to display plant, soil, water, and cloud conditions for any Landsat overpass.
There are no author-identified significant results in this report.
The soil or background spectra contribution to grass canopy spectral reflectance for the 0.35 to 0.80 micron region was investigated using in situ collected spectral reflectance data. Regression analysis was used to estimate accurately the unexposed soil spectral reflectance and to quantify maxima and minima for soil-green vegetation reflection contrasts.
The time trajectories of agricultural data points as seen in Landsat signal space form a pattern suggestive of a tasselled woolly cap. Most of the important crop phenomena can be described using this three dimensional construct: the distribution of signals from bare soil, the processes of green development, yellow development, and shadowing and harvesting. A linear preprocessing transformation which isolates green development, yellow development and soil brightness is used to reduce the dimension of the signal space. Specific measurable pattern elements of the tasselled cap are used to estimate and correct atmospheric haze and moisture effects.
Knowledge of the times when crop and forest vegetation experience seasonally related changes in development is important in understanding growth and yield relationships. This article describes how densitometry of earth resources technology satellite (ERTS-1) multispectral scanner (MSS) imagery can be used to identify such phenological events. Adjustments for instrument calibration, aperture size, gray-scale differences between overpasses, and normalization of changing solar elevation are considered in detail. Seasonal vegetation differences can be identified by densitometry of band 5 (0.6-0.7 microns) and band 7 (0.8-1.1 microns) MSS imagery. Band-to-band ratios of the densities depicted the changes more graphically than the individual band readings.
There are no author-identified significant results in this report.
Reflectance characteristics of agronomic crops are of major importance in the energy exchanges of a surface. In addition, unique reflectance patterns may be an aid in crop identification by means of remote sensing. Our study suggests that the ratio of the reflectances of the 545-nm to the 655-nm wavebands provides information about the viewed surface, regardless of the crop. The reflectance ratio is less than unity early and late in the growing season. For all crops studied, the ratio closely followed crop growth and development and appeared to be more desirable than the near-infrared reflectance as an index of growth.
Remote estimation of herbaceous biomass
- Johnson