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Forests are the potential source for managing carbon sequestration, regulating climate variations and balancing universal carbon equilibrium between sources and sinks. Further, assessment of biomass, carbon stock, and its spatial distribution is prerequisite for monitoring the health of forest ecosystem. Moreover, vegetation field inventories are v...
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Landsat 8 OLI/TIRS has the ability to generate Land Surface Temperature (LST) with band 10 (10,60 - 11,19 µm) and 11 (11,50 - 12,51 µm). That condition has led to the development of methods for Landsat 8 in LST processing. The SWA has various methods of extracting LST, one using FVC. This research aims to establish the level of accuracy of using FV...
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Droughts during the growing season are projected to increase in frequency and severity in Central Europe in the future. Thus, area-wide monitoring of agricultural drought in this region is becoming more and more important. In this context, it is essential to know where and when vegetation growth is primarily water-limited and whether remote sensing...
Citations
... Optical RS images with differing spatial, spectral, and temporal resolutions have been extensively utilized for AGB estimation across various scales (Nandy & Kushwaha, 2021). Coarse-resolution RS data like MODIS have proven successful for regional to national-scale AGB estimation (Fararoda et al., 2021;Pandey et al., 2019). Nationwide and regional studies have also been carried out to systematically analyse multi-decade carbon stocks changes in aboveground forest biomass at a 5 km grid resolution, as influenced by forest fraction in each grid. ...
... Random forest (RF) (Breiman, 2001) classifier is a popular MLA, which combines the output of multiple decision trees to furnish a single result (Speiser et al., 2019). In recent studies, multispectral satellite images and different spectral vegetation indices were used for biomass mapping of forest ecosystems (Pandey et al., 2019;Silveira et al., 2019;Shen et al., 2020;Malhi et al., 2022;Ma et al., 2023), tea-based agroforestry system (Kalita et al., 2022), and vegetation carbon density mapping . Multispectral satellite image Sentinel 2 A data provide Global Monitoring for Environment and Security (GMES) program is applicable in natural resource monitoring, land management, agriculture, and forestry production (eos.com/find-satellite/sentinel-2/). ...
The Himalayan region is a most fragile ecosystem globally. Trees make up around 90 % of the global biomass
carbon pool and previous studies have shown that tree carbon balance cannot be easily assessed by conventional
methods. Considering trees as a backbone of the forest ecosystem, present study assessed the heterogeneity in
tree carbon density using field-inventoried data and NDVI-based modelling with Sentinel 2 A imagery on Google
Earth Engine. The specific aim of the study was to assess the spatial distribution of tree carbon density in the
Darjeeling Himalayas using Sentinel 2 A. The object-based classification of forest area using a random forest
algorithm showed a high accuracy (Kappa coefficient value of 0.92, OOB error 0.17). The regression model using
NDVI as a predictor of tree carbon demonstrated a good fit (R2 = 0.78) for predicting tree carbon density.
Validation results show high accuracy of the regression model in predicting tree carbon density with a low RMSE
of 9.39 Mg ha− 1 (R2 = 0.80, % RMSE = 11.55 %). The classification of tree carbon density into five classes
revealed that a significant proportion of the forest area (57.05 %) falls under moderate carbon density (50–75
Mg ha− 1
). In Darjeeling Himalayas, majority of forests are under the carbon density between 50 and 75 Mg ha− 1
.
Improvement and conservation efforts must be directed for very low carbon density (01–25 Mg ha− 1
) areas
covering 0.05 %, and high carbon density (75–100 Mg ha− 1
) covering 36.22 % of the forest area, respectively, to
balance the overall carbon storage potential of the region.
Keywords:
Darjeeling himalayas
Carbon management
NDVI-Carbon modelling
Tree carbon density
Random forest
... A form factor of 0.45 was used for Pinus roxburghii, and a form factor of 0.59 was used for broadleaved species [36]. Afterward, the AGB was estimated using the following equation [37,38]: ...
... NDVI is one of the most preferred indices for comparing vegetation and non-vegetation areas [38]. Since the healthier and greener vegetation absorbs more visible light and reflects a higher infrared light compared to the unhealthy and sparse vegetation, this characteristic is utilized by the NDVI for indicating these differences and changes [70]. ...
Forests offer high potential for the fight against climate change. However, forests are faced with increased deforestation. REDD+ is a financial mechanism that offers hope to developing countries for tackling deforestation. Aboveground (AGB) estimation, however, is necessary for such financial mechanisms. Remote sensing methods offer various advantages for AGB estimation. A study, therefore, was conducted for the estimation of AGB using a combination of remote sensing Sentinel-1 (S1) and Sentinel-2 (S2) satellite data and field inventorying. The mean AGB for Sub-tropical Chir Pine Forest was recorded as 146.73 ± 65.11 Mg ha⁻¹, while for Sub-tropical Broadleaved Evergreen Forest it was 33.77 ± 51.63 Mg ha⁻¹. Results revealed weak associations between the S1 and S2 data with the AGB. Nonetheless, S1 and S2 offer advantages such as free data resources that can be utilized by developing countries for forest biomass and carbon monitoring.
... The conducted research has proved a high NDVI and LAI correlation and its dependence on field and laboratory data, revealed by other authors as well [27][28][29]. In particular, work [27] presents the results of studies carried out in winter time in the north of Europe, but works [28] and [19] in the tropical forest in northern India and in the Tripura region (India). ...
Morphometric indicators of trees' assimilative apparatus, including their reflection and light-absorbing properties, largely determine their physiological processes. Leaf initiation and development depend on chlorophyll resulting from photosynthesis, water absorption, its movement through a tree and evaporation. The aim of the paper is to evaluate leaf optical properties of Tilia cordata Mill. It was found that optical coefficients of leaf blades vary depending on the time of sample collection: at the end of the growing season, the leaf blade absorptance (Ab) ranges from 68.9% to 72.4%. During the intensive formation of Tilia flowers, the Ab coefficient decreases to 59.2%. It starts to rise before the beginning of the Tilia fruit maturation. The transmittance, Tr, is also lower at the period of intense flower development. Estimating NDVI (vegetation index) and LAI (leaf area index) from Sentinel-2 satellite imagery showed that the ratio between these indices (NDVI-LAI) changes during the entire growing season. The NDVI-LAI correlation varied up to the strongest one during the entire phenological cycle.
... We selected the MODIS data in this study because they have the eight-day temporal resolution and are dense in time series. Previous studies have proposed the correlations of the SR, LAI, the fraction of absorbed photosynthetically active radiation (FAPAR), GPP, and the canopy height (CH) with forest AGB (Muukkonen et al. 2006;Macdonald et al. 2012;Song et al. 2018;Pandey et al. 2019;Armstrong et al. 2020;Knapp et al. 2020). Hence, this study included the MODIS SR (MOD09A1), the LAI/FAPAR (MCD15A2), and GPP (MOD17A2) to estimate forest AGB. Figure 1. ...
Many studies have attempted to estimate forest aboveground biomass (AGB) from satellite data accurately. Temporal information may be beneficial to AGB estimation but remains underexplored. Thus, this paper aims to investigate whether and how temporal features extracted from multiple satellite-derived data products can improve prediction accuracy. To this end, we develop four methods to exploit the temporal features of moderate resolution imaging spectroradiometer (MODIS) data products: the method that uses all annual features (AAF), the method that selects essential features based on the Spearman correlation coefficient (SCC) criterion, the method that employs the seasonal average and principal component analysis (PCA) components (SAP), and the method that includes phenological characteristic parameters (PCP) as the predictors of forest AGB. Lidar-derived forest AGB in California serves as the reference AGB data, and the XGBoost ensemble algorithm is utilized to model forest AGB with temporal features of MODIS data products. The results demonstrate that the AAF-based features lead to the most accurate AGB prediction, whereas using information extracted by SAP and PCP gives rise to less accurate results. Annual MODIS surface reflectance data combined with forest canopy height can provide the AGB estimates, with an average R-squared (R²) of 0.58 and root-mean-squared error (RMSE) of 147.58 Mg/ha. The results of this study highlight the necessity of utilizing annual time-series data, particularly the annual surface reflectance data, for AGB prediction.
... Large amounts of ground data is needed because that is the limitation of correlation. The vegetation index (VI) technique, also known as band rationing, is a spectral enhancement extensively used to estimate LAI, biomass, f PAR, fCOVER, and other parameters, with semi-empirical and empirical models being used to create the relationship between VI and ground data (Kumar et al. 2013;Pandey et al. 2019a;Pandey et al. 2019b). One of the most widely used band ratios to estimate the above parameters is the NDVI. ...
Advancements in Earth Observation (EO) technologies show huge potential for systematic and temporal assessment of forest regions. Space‐borne datasets with synoptic view are being employed to monitor large spatial extent with temporal resolution and help in continuous monitoring of forest conditions such as health, productivity, forest fires, and forest degradation. Without these datasets, these monitoring processes would be impossible to observe from the ground. This chapter focuses on the recent advancements and roles of drones for forestry research and practices. Earlier sections will focus on traditional methods of remote sensing approaches, while later sections will elucidate the importance of emerging technologies in present day with respect to drones. Drones are robust and can be deployed for certain needs and requirements with flexible operation and better dimensionally in terms of the spatial/spectral resolution, unlike space‐borne satellites. All types of sensors, including RGB camera, Multispectral, thermal, & LiDAR sensors may be mounted on drones, which are capable of providing accurate information with better potential in forest monitoring. Forest monitoring using drones and associated sensors will be incorporated in research to better understand the forest parameters, and can be utilized to facilitate conservation, protection, and biological resources management at the local, regional, and global scale.
... Large amounts of ground data is needed because that is the limitation of correlation. The vegetation index (VI) technique, also known as band rationing, is a spectral enhancement extensively used to estimate LAI, biomass, f PAR, fCOVER, and other parameters, with semi-empirical and empirical models being used to create the relationship between VI and ground data (Kumar et al. 2013;Pandey et al. 2019a;Pandey et al. 2019b). One of the most widely used band ratios to estimate the above parameters is the NDVI. ...
Carotenoid pigments are deeply involved in forests’ response to environmental stressors. Imaging spectroscopy has been widely applied for predicting leaf total carotenoid content. However, distinguishing carotene and xanthophylls, which is essential for monitoring plants’ stress at a broad-scale, remains challenging. To achieve this, calibrating models using field spectroscopy is necessary for applications to drone, airborne, and satellite imagery. In this respect, this chapter presents a novel approach based on Machine Learning (ML), which involves comparing various algorithms applied to continuum-removed field reflectance data for estimating carotenoid contents in leaves of riparian forest species. The first section of the chapter outlines recent advances and pitfalls in carotenoid retrieval using remote sensing. The next section describes the proposed approach, including the description of the dataset, the principles of commonly-used ML algorithms, as well as their performance in distinguishing carotene and xanthophylls. Finally, the last section discusses the perspectives of upscaling the approach to imaging spectrometers towards broad-scale, operational monitoring of forests’ response to environmental stressors.
... It was also an objective of the project to enhance "inventory-based biomass stock estimating methodologies." The results of the study reveal a correlation of 0.87 between the NDVI and the LAI in 2011 and 0.53 in 2014 [23]. ...
A river is a huge natural freshwater stream that plays a significant role in hydrological dynamics, water resource management, and global activities. Understanding the dynamics of the river ecosystem, such as water quality, morphological traits, and so on, is crucial to determining its health. This article provides a broad review on Geographic Information System (GIS) and Remote Sensing (RS) applications for achieving geographical advantages, particularly in the river ecology. In recent years, the accessibility, accuracy, and popularity of RS technology have all increased dramatically. Land use and cover mapping, land cover changes, deforestation vegetation dynamics, and water quality dynamics at many scales utilising efficient methods are all covered using remote sensing data. RS may now be utilised for a variety of engineering-related applications at the same time. The importance of Landsat data and multispectral sensors in mapping and monitoring many environmental parameters of river ecosystems is highlighted. According to a recent research study, these technologies will aid in the establishment of safety measures prior to disasters. Additionally, river cleaning can be done in conjunction with the creation of an appropriate drainage system to protect the river from becoming contaminated. Future research is expected to build on developing technology, enhance present methodologies, and include innovative analytical approaches.
... Satellite images have been widely used in estimating forest AGB through the aid of field AGB estimations across multiple spatial scales (Tan et al., 2007). Since remote sensing images cannot directly provide forest AGB estimates, these studies usually built a regression model between field AGB estimates and remote sensing indices (e.g., normalized difference vegetation index/NDVI) to provide spatially continuous forest AGB (Pandey et al., 2019;Xiao et al., 2019). However, the accuracy of forest AGB estimates using this approach can be limited to 10%-50% depending on the forest type (Lu, 2007) and may suffer from saturation effects (Huang et al., 2015). ...
Accurate estimates of forest aboveground biomass (AGB) are essential for global carbon cycle studies and have widely relied on approaches using spectral and structural information of forest canopies extracted from various remote sensing datasets. However, combining the advantages of active and passive data sources to improve estimation accuracy remains challenging. Here, we proposed a new approach for forest AGB modeling based on allometric relationships and using the form of power-law to integrate structural and spectral information. Over 60 km² of drone light detection and ranging (LiDAR) data and 1,370 field plot measurements, covering the four major forest types of China (coniferous forest, sub-tropical broadleaf forest, coniferous and broadleaf-leaved mixed forest, and tropical broadleaf forest), were collected together with Sentinel-2 images to evaluate the proposed approach. The results show that the most universally useful structural and spectral metrics are the average values of canopy height and spectral index rather than their maximum values. Compared with structural attributes used alone, combining structural and spectral information can improve the estimation accuracy of AGB, increasing R² by about 10% and reducing the root mean square error by about 22%; the accuracy of the proposed approach can yield a R² of 0.7 in different forests types. The proposed approach performs the best in coniferous forest, followed by sub-tropical broadleaf forest, coniferous and broadleaf-leaved mixed forest, and then tropical broadleaf forest. Furthermore, the simple linear regression used in the proposed method is less sensitive to sample size and outperforms statistically multivariate machine learning-based regression models such as stepwise multiple regression, artificial neural networks, and Random Forest. The proposed approach may provide an alternative solution to map large-scale forest biomass using space-borne LiDAR and optical images with high accuracy.
... Healthy ecosystems along with rich biodiversity are fundamental to the existence of life on our planet and important to enhance ecosystem productivity. BD loss due to climate change may have the potential to change the structures and alter the functions of forest ecosystems, require assessment and monitoring (Pandey et al. 2014;Pandey et al. 2019b;Anand et al 2020). According to the recent study of Trisos et al. (2020), climate change could cause sudden, potentially catastrophic biodiversity losses all over the world throughout the twenty-first century. ...
Climate change poses threats to humans and brings the toughest challenges for economic development in the twenty-first century. The scientific communities warn the world leaders regarding the threats of climate change and its inevitable impacts on the physical and cultural environment. The Intergovernmental Panel on Climate Change (IPCC) reported global warming of 1.5 °C which is a matter of great concern for all the stakeholders around the world. The manifestation of recent climate change is increasing flooding events, shrinking of the cryosphere (mass loss from ice sheets/glaciers, reductions in snow cover), vegetation changes, and loss of biodiversity which are having adverse effects on available resources and aesthetic/cultural aspects of the Indian Himalayas (IH). Hence, it is imperative to study the impacts and responses of the mountainous region towards climate change for sustainable planning and adaptability. This chapter aims to review the scientific works on emerging trends in climate change, its impacts, and sustainability issues in the IH. The review work suggests the need for more research on innovative ideas for better adaptation and to combat the increasingly adverse impacts of climate change. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
