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... The SR index is obtained from the ratio of the wavelength with the highest reflectance for vegetation, obtained with Near Infrared Spectroscopy (NIR), and the wavelength of the deepest chlorophyll uptake (red) helps distinguish between densely and non-densely vegetated areas (Birth and McVey, 1968;Melillos and Hadjimitsis, 2020). ...
This study evaluates drought in different climate zones (Rasht, Shiraz, and Birjand) in Iran, using meteorological, agricultural, and remote sensing drought indices. For this purpose, NDVI, SAVI, and SR were extracted from Landsat images for 2002 and 2014-2020. Then, these indices were compared with the SPI, SPEI, and PDSI. The results indicate an increase in drought and a decrease in vegetation cover in the study area. In Rasht, where the vegetation cover is high, NDVI and SAVI were equal. In Shiraz and Birjand, where the soil effect is more significant, the distance between these two indices increased, which shows that SAVI performs better than NDVI for Shiraz and Birjand. The results also show that the drought severity could grow with decreasing rainfall and more water demand due to temperature increases, according to SPI, SPEI, and PDSI criteria. The comparison of drought indices showed that the highest correlations were between NDVI plus SAVI and SPI in Rasht, SR and SPEI in Shiraz, and NDVI and SPEI in Birjand. Based on the results of the Mann-Kendall test, the increasing trend of drought in the studied area is confirmed based on the SPI, SPEI, and PDSI. Therefore, it is suggested that remote sensing techniques combined with drought indices can be considered a suitable tool for optimal management of water resources, land use planning, and reduction of costs due to drought.
... Spectral indices were computed for the wetland mapping, including the Normalised Difference Mangrove Index (NDMI) [37], Modified Normalised Water Index (MNWI) [38], Simple Ratio Vegetation Index [39], Green Chlorophyll Vegetation Index (GCVI) [40] and normalised difference vegetation index (NDVI). ...
Pacific Island countries are vulnerable to the impacts of climate change, which include the risks of increased ocean temperatures, sea level rise and coastal wetland loss. The destruction of wetlands leads not only to a loss of carbon sequestration but also triggers the release of already sequestered carbon, in turn exacerbating global warming. These climate change effects are interrelated, and small island nations continuously need to develop adaptive and mitigative strategies to deal with them. However, accurate and reliable research is needed to know the extent of the climate change effects with future predictions. Hence, this study develops a new hybrid Convolutional Neural Network (CNN) Multi-Layer Bidirectional Long Short-Term Memory (BiLSTM) deep learning model with Multivariate Variational Mode Decomposition (MVMD) to predict the sea level for study sites in the Solomon Islands and Federated States of Micronesia (FSM). Three other artificial intelligence (AI) models (Random Forest (FR), multilinear regression (MLR) and multi-layer perceptron (MLP) are used to benchmark the CNN-BiLSTM model. In addition to this, remotely sensed satellite Landsat imagery data are also used to assess and predict coastal wetland changes using a Random Forest (RF) classification model in the two small Pacific Island states. The CNN-BiLSTM model was found to provide the most accurate predictions (with a correlation coefficient of >0.99), and similarly a high level of accuracy (>0.98) was achieved using a Random Forest (RF) model to detect wetlands in both study sites. The mean sea levels were found to have risen 6.0 ± 2.1 mm/year in the Solomon Islands and 7.2 ± 2.2 mm/year in the FSM over the past two decades. Coastal wetlands in general were found to have decreased in total area for both study sites. The Solomon Islands recorded a greater decline in coastal wetland between 2009 and 2022.
... It is determined from the ratio between the amount of reflected near-infrared and red radiation. For green plant leaves this ratio can take on values of several tens, while for unplanted soil it is about zero [16]: ...
... This index, which is obtained from the ratio of the wavelength with the highest reflection for vegetation (near-infrared, NIR) and the wavelength of the deepest absorption of chlorophyll (Red), is useful for distinguishing between areas with dense and non-dense vegetation (Birth & McVey 1968;Melillos & Hadjimitsis 2020). This index can be calculated based on the following equation: ...
Dry and humid climates have different potentials for providing soil moisture. Agricultural drought is a confirmed criterion for evaluating production potential in agriculture, which is discussed in this research. Therefore, this research aims to investigate drought using meteorological and agricultural drought indicator data in four climatic regions of China (humid, semi-humid, semi-arid and dry). For this purpose, climatic information was collected in the last 20 years, and the values of the standard precipitation index (SPI) and reconnaissance drought index (RDI) were determined. Examining the indicators indicates that the indicators are high in all the years under review in dry areas. In the semi-arid region, there was a significant decrease in the average value of the indices in July and August in the years 2017–2022. Drought indicators did not show a critical situation in humid and semi-humid areas, and there was sufficient moisture for plants throughout the year. The results showed that there was a high correlation between the SPI and the RDI in all the identified areas. In addition to rainfall, the RDI also includes transpiration and is more sensitive, especially in dry areas where transpiration is higher than rainfall.
... The ML models evaluated; MLR, RF, DT, SVR, XGB, KNN, and ANN are all good at handling a continuous dependent variable that is correlated with VIs. The use of spectral vegetation indices in conjunction with machine learning models proved to be an effective method for predicting chlorophyll content consistent with [64], and spectral indices have proven to be an essential technique for evaluating nitrogen [14]. This study did not remove the soil from the reflectance map to estimate vegetation indices because the crop completely covered the soil during the experimental period. ...
The use of satellite-based Remote Sensing (RS) is a well-developed field of research. RS techniques have been successfully utilized to evaluate the chlorophyll content for the monitoring of sugarcane crops. This research provides a new framework for inferring the chlorophyll content in sugarcane crops at the canopy level using unmanned aerial vehicles (UAVs) and spectral vegetation indices processed with multiple machine learning algorithms. Studies were conducted in a sugarcane field located in Sugarcane Research Institute (SRI, Uda Walawe, Sri Lanka), with various fertilizer applications over the entire growing season from 2020 to 2021. An UAV with multispectral camera was used to collect the aerial images to generate the vegetation indices. Ground measurements of leaf chlorophyll were used as indications for fertilizer status in the sugarcane field. Different machine learning (ML) algorithms were used ground-truthing data of chlorophyll content and spectral vegetation indices to forecast sugarcane chlorophyll content. Several machine learning algorithms such as MLR, RF, DT, SVR, XGB, KNN and ANN were applied in two ways: before feature selection (BFS) by training the algorithms with all twenty-four (24) vegetation indices with five (05)spectral bands and after feature selection (AFS) by training algorithms with fifteen (15) vegetation indices. All the algorithms with both BFS and AFS methods were compared with an estimated coefficient of determination (R2) and root mean square error (RMSE). Spectral indices such as RVIand DVI were shown to be the most reliable indices for estimating chlorophyll content in sugarcane fields, with coefficients of determination (R2) of 0.94 and 0.93, respectively. XGB model shows the highest validation score (R2) and lowest RMSE in both methods of BFS (0.96 and 0.14) and AFS (0.98 and 0.78), respectively. However, KNN and SVR algorithms show the lowest validation accuracy than other models. According to the results, the AFS validation score is higher than BFS in MLR, SVR, XGB and KNN. Even though, validation score of the ANN model is decreased in AFS. The findings demonstrated that the use of multispectral UAV could be utilized to estimate chlorophyll content and measure crop health status over a larger sugarcane field. This methodology will aid in real-time crop nutrition management in sugarcane plantations by reducing the need for conventional measurement of sugarcane chlorophyll content.
... According to the calculation method, VIP includes band ratio calculation and nonratio calculation (e.g., CAI [46], MNDVI [47], etc.). The band ratio calculation includes simple band ratio (e.g., SRI [48], WBI [49], etc.), normalized band ratio (such as NDVI [50], PRI [51], and SIPI), and enhanced band ratio (e.g., EVI [52], ARVI, etc.). The sensitivity of VIPs to biological and physical parameters of vegetation is much higher than that of single band reflectance. ...
With the initial establishment of global earth observation system in various countries, more and more high-resolution remote sensing data of multi-source, multi-temporal, multi-scale and different types of satellites are obtained. It is urgent to explore the advanced basic theory of remote sensing information science, design high-performance generic key technologies of remote sensing information system and global positioning system, and study complex engineering system of remote sensing applications and geographic information system. In this paper, the basic theory exploration, inversion technology research, and engineering application design and development of generic Optical Remote Sensing Product (ORSP) are systematically reviewed. We classify the ORSP scientifically, review the main algorithms and application scope of 16 kinds of generic ORSP, and expound the validation and quality evaluation methods of ORSP in engineering application. Furthermore, we analyze the current core problems and solutions, and prospects for state-of-the art research and the future development trend of generic ORSP. This will provide valuable reference for scientific research and construction of high-resolution earth observation system.
... reflectivity is composed of a simple ratio between two bands, one with the highest and the other with the lowest reflectance concerning vegetation. This index is calculated using the (NIR/R) equation(Melillos, Hadjimitsis 2020). The range of SR index variations for aquatic and non-vegetated zones is from 1 to 1, for regions with unhealthy vegetation from 1 to 2.2 and for regions with healthy plants from 2.2 to higher values(Robinson et al. 2017) (NIR/R)PHIThe plant health instructor index is also used to determine the health condition of the plants in some regions and can be calculated using the equation 1.4 × LN(DVI) + 1.298. ...
Pests and diseases can cause a variety of reactions in plants. In recent years, the boxwood dieback has become one of the essential concerns of practitioners and natural resources managers in Iran. To control the boxwood dieback spread, the early detection and disease distribution maps are required. The boxwood dieback causes a range of changes in colour, shape and leaf size with respect to photosynthesis and transpiration. Through remote sensing techniques, e.g. satellite image processing data, the variation of thermal and visual characteristics of the plant could be used to measure and illustrate the symptoms of the disease. In this study, five common vegetation indices like difference vegetation index (DVI), normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), simple ratio (SR), and plant health index (PHI) were extracted and calculated from Landsat 8 satellite image data from six regions in the Gilan province, located in the northern part of Iran out of 150 maps over the time period 2014‒2018. It turned out that among the aforementioned indices, based upon the results of the models, SR and NDVI indices were more useful for the disease spread, respectively. Our disease progression model fitting criteria showed that this technique could probably be used to assess the extent of the affected areas and also the disease progression in the investigated regions in future.
The proliferation of invasive plant species poses a significant ecological threat, necessitating effective mapping strategies for control and conservation efforts. Existing studies employing unmanned aerial vehicles (UAVs) and multispectral (MS) sensors in complex natural environments have predominantly relied on classical machine learning (ML) models for mapping plant species in natural environments. However, a critical gap exists in the literature regarding the use of deep learning (DL) techniques that integrate MS data and vegetation indices (VIs) with different feature extraction techniques to map invasive species in complex natural environments. This research addresses this gap by focusing on mapping the distribution of the Broad-leaved pepper (BLP) along the coastal strip in the Sunshine Coast region of Southern Queensland in Australia. The methodology employs a dual approach, utilising classical ML models including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM) in conjunction with the U-Net DL model. This comparative analysis allows for an in-depth evaluation of the performance and effectiveness of both classical ML and advanced DL techniques in mapping the distribution of BLP along the coastal strip. Results indicate that the DL U-Net model outperforms classical ML models, achieving a precision of 83%, recall of 81%, and F1–score of 82% for BLP classification during training and validation. The DL U-Net model attains a precision of 86%, recall of 76%, and F1–score of 81% for BLP classification, along with an Intersection over Union (IoU) of 68% on the separate test dataset not used for training. These findings contribute valuable insights to environmental conservation efforts, emphasising the significance of integrating MS data with DL techniques for the accurate mapping of invasive plant species.
Vegetation in East Antarctica, such as moss and lichen, vulnerable to the effects of climate change and ozone depletion, requires robust non-invasive methods to monitor its health condition. Despite the increasing use of unmanned aerial vehicles (UAVs) to acquire high-resolution data for vegetation analysis in Antarctic regions through artificial intelligence (AI) techniques, the use of multispectral imagery and deep learning (DL) is quite limited. This study addresses this gap with two pivotal contributions: (1) it underscores the potential of deep learning (DL) in a field with notably limited implementations for these datasets; and (2) it introduces an innovative workflow that compares the performance between two supervised machine learning (ML) classifiers: Extreme Gradient Boosting (XGBoost) and U-Net. The proposed workflow is validated by detecting and mapping moss and lichen using data collected in the highly biodiverse Antarctic Specially Protected Area (ASPA) 135, situated near Casey Station, between January and February 2023. The implemented ML models were trained against five classes: Healthy Moss, Stressed Moss, Moribund Moss, Lichen, and Non-vegetated. In the development of the U-Net model, two methods were applied: Method (1) which utilised the original labelled data as those used for XGBoost; and Method (2) which incorporated XGBoost predictions as additional input to that version of U-Net. Results indicate that XGBoost demonstrated robust performance, exceeding 85% in key metrics such as precision, recall, and F1-score. The workflow suggested enhanced accuracy in the classification outputs for U-Net, as Method 2 demonstrated a substantial increase in precision, recall and F1-score compared to Method 1, with notable improvements such as precision for Healthy Moss (Method 2: 94% vs. Method 1: 74%) and recall for Stressed Moss (Method 2: 86% vs. Method 1: 69%). These findings contribute to advancing non-invasive monitoring techniques for the delicate Antarctic ecosystems, showcasing the potential of UAVs, high-resolution multispectral imagery, and ML models in remote sensing applications.
The paper presents a computer system for monitoring plant growth, developed for the needs of precision agriculture for small agricultural areas. The work contains a description of the monitoring system with a breakdown into the key elements of the process. An exemplary method of preparing orthophotomaps of the area was presented. The method of making maps that can be implemented on a PC computer has been described. The paper describes the most frequently used Vegetation Index. A test of determining the coefficients was carried out on an exemplary aerals with an area of 5.28 ha. Typical positioning systems for agricultural machines are discussed. The DGPS navigation method was used in the tests. Tests have confirmed that it can be used in precision agriculture with small aerals. The solution is optimal in terms of positioning accuracy and economics of small farms. The presented system was tested during one cycle of vegetation of winter barley sown with the no-plowing method. On this basis, the complexity of the system was assessed and its implementation was proposed. The proposed solution does not require complex computer systems. It has been designed so that it can be implemented on standard PC equipment cooperating with a short-range drone equipped with a standard RGB camera.KeywordsPrecision agricultureField mappingVegetation indicatorGeological indicatorPrecision GPS
