Synergistic evaluation of Sentinel 1 and 2 for biomass estimation in a tropical forest of India

Forest degradation is considered to be one of the major threats to forests over the globe, which has considerably increased in recent decades. Forests are gradually getting fragmented and facing biodiversity losses because of climate change and anthropogenic activities. Future prediction of forest degradation spatiotemporal dynamics and fragmentation is imperative for generating a framework that can aid in prioritizing forest conservation and sustainable management practices. In this study, a random forest algorithm was developed and applied to a series of Landsat images of 1998, 2008, and 2018, to delineate spatiotemporal forest cover status in the sanctuary, along with the predictive model viz. the Cellular Automata Markov Chain for simulating a 2028 forest cover scenario in Shoolpaneshwar Wildlife Sanctuary (SWS), Gujarat, India. The model’s predicting ability was assessed using a series of accuracy indices. Moreover, spatial pattern analysis—with the use of FRAGSTATS 4.2 software—was applied to the generated and predicted forest cover classes, to determine forest fragmentation in SWS. Change detection analysis showed an overall decrease in dense forest and a subsequent increase in the open and degraded forests. Several fragmentation metrics were quantified at patch, class, and landscape level, which showed trends reflecting a decrease in fragmentation in forest areas of SWS for the period 1998 to 2028. The improvement in SWS can be attributed to the enhanced forest management activities led by the government, for the protection and conservation of the sanctuary. To our knowledge, the present study is one of the few focusing on exploring and demonstrating the added value of the synergistic use of the Cellular Automata Markov Chain Model Coupled with Fragmentation Statistics in forest degradation analysis and prediction.

https://www.mdpi.com/2220-9964/9/9/530/pdf

An Integrated Spatiotemporal Pattern Analysis Model to Assess and Predict the Degradation of Protected Forest Areas

In the era of climate change, monitoring and effective retrieval of soil, water bodies, vegetation parameters etc. are of utmost importance which is successfully being executed using remote sensing from last few decades. The advancement of technologies has enabled us to reach effective decision making through these sensors. The advantage of acquiring multitemporal spatially continuous data sometimes turns into a disadvantage due to class imbalance where minority class instances are often misclassified by most of the classifiers. The current work explored the solution to handle this problem by resampling the datasets before the application of classification algorithms by proposing a new computationally efficient class wise resampling technique which is based on SMOTE and centroid-based clustering. The experiment was conducted on two benchmarked publicly available hyperspectral datasets. The output of the current work shows the superiority of the current work over past studies based on the performance evaluation metrics, accuracy, precision, recall and kappa values.

Enhanced classification of hyperspectral images using improvised oversampling and undersampling techniques

Interband information overlapping enhances redundancy in hyperspectral data. This makes identification of application-specific optimal bands essential for obtaining accurate information about folia...

Band selection algorithms for foliar trait retrieval using AVIRIS-NG: a comparison of feature based attribute evaluators

Species diversity quantification is a crucial step towards the biodiversity conservation and ecosystem health. The technological advancements and existing limitations of multispectral remote sensing has increased the popularity of hyperspectral remote sensing which found its use in the estimation of species diversity. The contiguous narrow bands available in hyperspectral data enables the improvised assessment of diversity index but the overlapping of the information could result in the redundancy that needs to be handled. Due to this, the idenfication of optimal bands is very important; hence, the current study provides modified hyperspectral indices through detection of optimum bands for estimating species diversity within Shoolpaneshwar Wildlife Sanctuary (SWS), India. Narrow hyperspectral bands of EO-1 Hyperion image were screened and the best optimum wavelength from visible and Near Infrared (NIR) regions were identified based on coefficient of determination (r2) between band reflectance and in situ measured species diversity. For in situ species diversity measurements, quadrat sampling was carried out in SWS and different Diversity Indices (DIs) namely the Shannon Weiner DI, Margalef DI, McIntosh DI and Brillouin DI were calculated. The identified optimum wavelengths were then employed for modifying 38 existing spectral indices which were then investigated for testing their relation with the in situ DIs. The obtained optimum bands in visible and NIR regions were found to be in correspondence with four DIs. Among several indices used in this study, during validation, modified Non-linear index, modified Red Edge Position Index, modified Structure Insensitive Pigment Index and modified Red Green Ratio Index were identified as the best hyperspectral indices for determining Shannon Weiner DI, Margalef DI, McIntosh DI and Brillouin DI, respectively.

Optimal band characterization in reformation of hyperspectral indices for species diversity estimation

After several years of research and development in hyperspectral imaging systems that enriched our knowledge and enhanced our capacity to explore the Earth, these systems have been widely accepted by the remote sensing community. They have evolved as major techniques and have now entered the mainstream of the earth observation data users. This chapter discusses the origin of hyperspectral remote sensing, its importance, preprocessing, inversion models suitable for hyperspectral datasets, as well as several possible applications, including but not limited to, vegetation analysis, agriculture, urban, water quality, and mineral identification. The chapter concludes by looking at the way forward for hyperspectral remote sensing.

Revisiting hyperspectral remote sensing: origin, processing, applications and way forward

The integration of ecological and atmospheric characteristics for biodiversity management is fundamental for long-term ecosystem conservation and drafting forest management strategies, especially in the current era of climate change. The explicit modelling of regional ecological responses and their impact on individual species is a significant prerequisite for any adaptation strategy. The present study focuses on predicting the regional distribution of Rhododendron arboreum, a medicinal plant species found in the Himalayan region. Advanced Species Distribution Models (SDM) based on the principle of predefined hypothesis, namely BIOCLIM, was used to model the potential distribution of Rhododendron arboreum. This hypothesis tends to vary with the change in locations, and thus, robust models are required to establish nonlinear complex relations between the input parameters. To address this nonlinear relation, a class of deep neural networks, Convolutional Neural Network (CNN) architecture is proposed, designed, and tested, which eventually gave much better accuracy than the BIOCLIM model. Both of the models were given 16 input parameters, including ecological and atmospheric variables, which were statistically resampled and were then utilized in establishing the linear and nonlinear relationship to better fit the occurrence scenarios of the species. The input parameters were mostly acquired from the recent satellite missions, including MODIS, Sentinel-2, Sentinel-5p, the Shuttle Radar Topography Mission (SRTM), and ECOSTRESS. The performance across all the thresholds was evaluated using the value of the Area Under Curve (AUC) evaluation metrics. The AUC value was found to be 0.917 with CNN, whereas it was 0.68 with BIOCLIM, respectively. The performance evaluation metrics indicate the superiority of CNN for species distribution over BIOCLIM.

Integrating Multi-Sensors Data for Species Distribution Mapping Using Deep Learning and Envelope Models

Spectroradiometry: Types, Data Collection, and Processing

Development of hyperspectral indices for anti-cancerous Taxol content estimation in the Himalayan region

Day by day water scarcity is increasing and simultaneously waste water generation is also increasing. Therefore, proper management and recycling of waste water is required in order to minimise its ill-effects. One of the productive uses of the industrial waste water is its application in agricultural lands but not every type of industrial waste water is found fit for such application. Thus, in this study comparative analyses of soil, water and vegetable produced (growth and biochemical parameters) by irrigating using tap water and oil refinery waste water were done to bring out the differences between the cultivated vegetables. Numerous parameters from physicochemical properties of water, soil, germination, morphology, growth rate to biochemical aspects of the vegetables were analysed. It was found that epicotyl, hypocotyl and the growth of root were better when using the effluents in irrigation. Nutritional value of effluent-irrigated plants was found to be higher, as fruits contained appreciable amounts of protein, ascorbic acid, calcium, magnesium, sodium, potassium and phosphorus.

Ayushi Gupta

Papers

Revisiting hyperspectral remote sensing: origin, processing, applications and way forward

Integrating Multi-Sensors Data for Species Distribution Mapping Using Deep Learning and Envelope Models

Spectroradiometry: Types, Data Collection, and Processing

Development of hyperspectral indices for anti-cancerous Taxol content estimation in the Himalayan region

Treated waste water as an alternative to fresh water irrigation with improved crop production