Atmospherically Resistant Vegetation Index Research Articles

A Bio-inspired and Deep Learning Based Hybrid Model for Agricultural Drought Assessment

Agricultural droughts can cause many serious hazards. Drought monitoring indices, namely Normalized Difference Vegetation Index (NDVI), Atmospherically Resistant Vegetation Index (ARVI), Soil Adjusted Vegetation Index (SAVI), and Enhanced Vegetation Index (EVI) have been used for an agricultural drought assessment. Satellite images from the Kolar region of Karnataka are used to calculate these indices. This paper proposes an integration model based on Convolutional Neural Networks (CNN) and a bio-inspired algorithm (Sparrow Search Algorithm (SSA) and Barnacles Mating Optimizer (BMO)) considering the indices as population. Performance is compared with the standalone CNN model in terms of efficiency. For the CNN, the accuracy, time taken for Epoch1, and time taken for Epoch2 is 91%, 16s (3s/step), and 2s (2s/step), respectively. For the CNN integrated with SSA, it is 94%, 3s (3s/step) and 0s (43ms/step), respectively. For the CNN integrated with BMO, it is 94%, 3s (2s/step) and 0s (46ms/step) respectively.

Natural H2 Emissions in Colombian Ophiolites: First Findings

The exploration of natural H2 or white hydrogen has started in various geological settings. Ophiolitic nappes are already recognized as one of the promising contexts. In South America, the only data available so far concerns the Archean iron-rich rocks of the Mina Gerais in Brazil or the subduction context of Bolivia. In Colombia, despite government efforts to promote white hydrogen, data remain limited. This article introduces the initial dataset obtained through soil gas sampling within the Cauca-Patia Valley and Western Cordillera, where the underlying geology comprises accreted oceanic lithosphere. In this valley, promising areas with H2 potential were identified using remote sensing tools, in particular vegetation anomalies. The Atmospherically Resistant Vegetation Index (ARVI) appears to be well adapted for this context and the field data collection confirmed the presence of H2 in the soil in all pre-selected structures. The valley undergoes extensive cultivation, mainly for sugar cane production. While H2 emissions lead to alterations in vegetation, unlike reports from other countries, they do not result in its complete disappearance. Soil gas measurements along the thrusts bordering the Cauca Valley also show high H2 content in the fault zones. In the valley, the presence of sedimentary cover above the ophiolites which are presumably the H2 generating rocks, which addresses the possible presence of reservoirs and seals to define potential plays. Drawing parallels with the Malian case, it could be that the intrusive element could serve as seals.

Multi-Sensor Remote Sensing to Estimate Biophysical Variables of Green-Onion Crop (Allium cepa L.) under Different Sources of Magnesium in Ismailia, Egypt

Foliar feeding has been confirmed to be the fastest way of dealing with nutrient deficiencies and increasing the yield and quality of crop products. The synthesis of chlorophyll and photosynthesis are directly related to magnesium (Mg), which operates in the improvement of plant tissues and enhances the appearance of plants. This study aimed to analyze the correlation between two biophysical variables, including the leaf area index (LAI), the fraction of absorbed photosynthetically active radiation (FAPAR), and seven spectral vegetation indices. The spectral indices under investigation were Atmospherically Resistant Vegetation Index (ARVI), Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), Disease–Water Stress Index (DSWI), Modified Chlorophyll Absorption Ratio Index (MCARI), the Red-Edge Inflection Point Index (REIP), and Pigment-Specific Simple Ratio (PSSRa). These indices were derived from Sentinel-2 data to investigate the impact of applying foliar applications of Mg from various sources in the production of green-onion crops. The biophysical variables were derived using field measurements and Sentinel-2 data under the effects of different sources of Mg foliar sprays. The correlation coefficient between field-measured LAI and remotely sensed, calculated LAI was 0.72 in two seasons. Concerning FAPAR, it was found that the correlation between remotely sensed calculated FAPAR and field-measured FAPAR was 0.66 in the first season and 0.89 in the second season. The magnesium oxide nanoparticle (nMgO) treatments resulted in significantly higher yields than the different treatments of foliar applications. The LAI and FAPAR variables showed a positive correlation with yield in the first season (October) and in the second season (March). Yield in treatment by nMgO varied significantly from that in the other treatments, ranging from 69-ton ha−1 in the first season to 74.9-ton ha−1 in the second season. Linear regression between LAI and PSSRa showed the highest correlation coefficient (0.90) compared with other vegetation indices in the first season. In the same season, the highest correlation coefficient (0.94) was found between FAPAR and PSSRa. In the second season, the highest accuracy to the estimate LAI was found in the correlation between MCARI and PSSRa, with correlation coefficients of 0.9 and 0.91, respectively. In the second season, the highest accuracy to the estimate FAPAR was found with the correlation between PSSRa, ARVI, and NDVI, with correlation coefficients 0.97 and 0.96, respectively. The highest correlation coefficients between vegetation indices and yield were found with ARVI and NDVI in the first season, and only with NDVI in the second season.

Spatial Analysis of Mycobacterium Ulcerans-Hosting Water Bug Critical Zones in the Amansie West District of Ghana

The World Health Organization (WHO) recognized Buruli ulcer (BU), a skin disease caused by Mycobacterium ulcerans (MU), as the third most prevalent mycobacterial disease in 1998, following tuberculosis and leprosy. In Ghana, there have been over 2000 reported cases in the past decade, with outbreaks in 90 administrative districts, including Amansie West. The mode of transmission remains unclear, but research suggests water bugs may be involved. This study aimed to assess this transmission method by identifying potential water bug habitats using geospatial techniques. Water bugs are found to inhabit freshwater ponds, marshes, slow-moving pools, streams, and mats of vegetation. This study used the Normalized Difference Water Index (NDWI) to locate water bodies and marshy areas, the Atmospherically Resistant Vegetation Index (ARVI) to identify vegetation zones, and Land Surface Temperature (LST) to determine suitable areas for water bugs based on temperature. A spatial multi-criteria evaluation (MCE) pinpointed critical areas for Mycobacterium ulcerans-hosting water bugs, which were mainly located in illegal mining areas due to stagnant wastewater. Notably, 36 communities, including prominent ones like Manso Nkwanta and Pakyi Number 1 and 2, were situated within these critical zones. This study generated a geospatial distribution map highlighting the potential areas for Mycobacterium ulcerans-hosting water bugs in the Amansie West district, crucial for Buruli ulcer transmission. The study recommended improved environmental sanitation in the affected areas to combat this water bug-related health issue.

Quantification of Biophysical Parameters and Economic Yield in Cotton and Rice Using Drone Technology

New agronomic opportunities for more informed agricultural decisions and enhanced crop management have been made possible by drone-based near-ground remote sensing. Obtaining precise non-destructive information regarding crop biophysical characteristics at spatial and temporal scales is now possible. Drone-mounted multispectral and thermal sensors were used to assess crop phenology, condition, and stress by profiling spectral vegetation indices in crop fields. In this study, vegetation indices, viz., Atmospherically Resistant Vegetation Index (ARVI), Modified Chlorophyll Absorption Ratio Index (MCARI), Wide Dynamic Range Vegetation Index (WDRVI), Normalized Red–Green Difference Index (NGRDI), Excess Green Index (ExG), Red–Green Blue Vegetation Index (RGBVI), and Visible Atmospherically Resistant Index (VARI) were generated. Furthermore, Pearson correlation analysis showed a better correlation between WDRVI and VARI with LAI (R = 0.955 and R = 0.982) ground truth data. In contrast, a strong correlation (R = 0.931 and R = 0.844) was recorded with MCARI and NGRDI with SPAD chlorophyll ground truth data. Then, the best-performing indices, WDRVI and MCARI in cotton, and VARI and NGRDI in rice, were further used to generate the yield model. This study for determining LAI and chlorophyll shows that high spatial resolution drone imageries are accurate and fast. As a result, finding out the LAI and chlorophyll and how they affect crop yield at a regional scale is helpful. The widespread use of unmanned aerial vehicles (UAV) and yield prediction were technical components of large-scale precision agriculture.

Computing Vegetation Indices from the Satellite Images Using GRASS GIS Scripts for Monitoring Mangrove Forests in the Coastal Landscapes of Niger Delta, Nigeria

This paper addresses the issue of the satellite image processing using GRASS GIS in the mangrove forests of the Niger River Delta, southern Nigeria. The estuary of the Niger River Delta in the Gulf of Guinea is an essential hotspot of biodiversity on the western coast of Africa. At the same time, climate issues and anthropogenic factors affect vulnerable coastal ecosystems and result in the rapid decline of mangrove habitats. This motivates monitoring of the vegetation patterns using advanced cartographic methods and data analysis. As a response to this need, this study aimed to calculate and map several vegetation indices (VI) using scripts as advanced programming methods integrated in geospatial studies. The data include four Landsat 8-9 OLI/TIRS images covering the western segment of the Niger River Delta in the Bight of Benin for 2013, 2015, 2021, and 2022. The techniques included the ’i.vi’, ’i.landsat.toar’ and other modules of the GRASS GIS. Based on the GRASS GIS ’i.vi’ module, ten VI were computed and mapped for the western segment of the Niger River Delta estuary: Atmospherically Resistant Vegetation Index (ARVI), Green Atmospherically Resistant Vegetation Index (GARI), Green Vegetation Index (GVI), Difference Vegetation Index (DVI), Perpendicular Vegetation Index (PVI), Global Environmental Monitoring Index (GEMI), Normalized Difference Water Index (NDWI), Second Modified Soil Adjusted Vegetation Index (MSAVI2), Infrared Percentage Vegetation Index (IPVI), and Enhanced Vegetation Index (EVI). The results showed variations in the vegetation patterns in mangrove habitats situated in the Niger River Delta over the last decade as well as the increase in urban areas (Onitsha, Sapele, Warri and Benin City) and settlements in the Delta State due to urbanization. The advanced techniques of the GRASS GIS of satellite image processing and analysis enabled us to identify and visualize changes in vegetation patterns. The technical excellence of the GRASS GIS in image processing and analysis was demonstrated in the scripts used in this study.

Application of Vegetation Indices for Detection and Monitoring Oil Spills in Ahoada West Local Government Area of Rivers State, Nigeria

The study evaluated the environmental effects of an oil spill in Joinkrama 4 and Akimima Ahoada West LGA, Rivers State, Nigeria, using various vegetation indices. Location data for the spill were obtained from the Nigeria Oil Spill Detection and Response Agency, and Landsat imagery was acquired from the United States Geological Survey. Three soil samples were collected from the affected area, and their analysis included measuring total petroleum hydrocarbons (TPH), total hydrocarbons (THC), and polycyclic aromatic hydrocarbons (PAH). The obtained data were processed with ArcGIS software, utilizing different vegetation indices such as the Normalized Difference Vegetation Index (NDVI), Atmospheric Resistant Vegetation Index (ARVI), Soil Adjusted Vegetation Index (SAVI), Green Short Wave Infrared (GSWIR), and Green Near Infrared (GNIR). Statistical analysis was performed using SPSS and Microsoft Excel. The results consistently indicated a negative impact on the environment resulting from the oil spill. A comparison of spectral reflectance values between the oil spill site and the non-oil spill site showed lower values at the oil spill site across all vegetation indices (NDVI 0.0665-0.2622, ARVI –0.0495-0.1268, SAVI 0.0333-0.1311, GSWIR –0.183-0.0517, GNIR –0.0104-–0.1980), indicating damage to vegetation. Additionally, the study examined the correlation between vegetation indices and environmental parameters associated with the oil spill, revealing significant relationships with TPH, THC, and PAH. A t-test with a significance level of p < 0.05 indicated significantly higher vegetation index values at the non-oil spill site compared to the oil spill site, suggesting a potential disparity in vegetation health between the two areas. Hence, this study emphasizes the harmful effect of oil spills on vegetation and highlights the importance of utilizing vegetation indices and spectral reflectance analysis to detect and monitor the impact of oil spills on vegetation.

Comparative Analysis of Remote Sensing and Geo-Statistical Techniques to Quantify Forest Biomass

Accurately characterizing carbon stock is vital for reporting carbon emissions from forest ecosystems. We studied the estimation of biomass using Sentinel-2 remote sensing data in moist temperate forests in the Galies region of Abbottabad Pakistan. Above-ground biomass (AGB), estimated from 60 field plots, was correlated with vegetation indices obtained from Sentinel-2 image-to-map AGB using regression models. Furthermore, additional explanatory variables were also associated with AGB in the geo-statistical technique, and kriging interpolation was used to predict AGB. The results illustrate that the atmospherically resistant vegetation index (ARVI) is the best index (R2 =0.67) for estimating AGB. In spectral reflectance, Band 1(Coastal Aerosol 443 nm) performs better than other bands. Multiple linear regression models calibrated with ARVI, NNIR and NDVI yielded better results (R2 = 0.46) with the lowest RMSE (48.53) and MAE (38.42) and were therefore considered better for biomass estimation. On the other hand, in the geo-statistical technique, distance to settlements, ARVI and annual precipitation were significantly correlated with biomass compared to others. In the stepwise regression method, the forward selection resulted in a very significant value (less than 0.000) for ARVI. Therefore, it can be considered best for prediction and used to interpolate AGB through kriging. Compared to the geo-statistical technique, the remote sensing-based models performed relatively well. Regarding potential sites for REDD+ implementation, temporal analysis of Landsat images showed a decrease in forest area from 8896.23 ha in 1988 to 7692.03 ha in 2018. Therefore, this study concludes that the state-of-the-art open-source sensor, the Sentinel-2 data, has significant potential for forest biomass and carbon stock estimation and can be used for robust regional AGB estimation with acceptable accuracy and frequent availability.

A COMPARATIVE STUDY OF MACHINE LEARNING CLASSIFIERS FOR CROP TYPE MAPPING USING VEGETATION INDICES

Abstract. Timely and accurate mapping of crops is crucial for agriculture management, policy-making, and food security. Due to the differences in the product calendars of various crops, it is possible to classify them by investigating the remote sensing Vegetation Indices (VIs) during crop growth season. This study developed a VI-based mapping approach to specifying crop types based on phenological and spectral metrics derived from the sentinel-2 images. We used six spectral VIs (ARVI, CVI, EVI, LAI, GLI, and NDVI) in three supervised machine learning methods, including Random Forest (RF), GBoost (GB), and K-Nearest Neighborhood (KNN) for crop mapping. Field data consisting of wheat, barley, canola, vegetables, and a bare land class, were collected as the testing and training data set. The classification results were evaluated through test samples showing high overall accuracy (OA) and satisfactory class accuracies for the most dominant crop types across different fields despite the variability of planting and harvesting dates. Among the VIs utilized to crop mapping, the Atmospherically Resistant Vegetation Index (ARVI) in all three classification methods achieved better results. The overall accuracy of RF, GB, and KNN models with the ARVI index was 95%, 88%, and 90%, respectively.

PENDUGAAN POTENSI KARBON HUTAN DENGAN SISTEM PENGINDRAAN JAUH

The relationship between remote sensing and biomass is that remote sensing is one of the best approaches for regional biomass estimation when forest stand data in the field are difficult to obtain. One used images satellite estimate forest carbon and biomass is the Landsat 8 OLI (Operational Land Imager) image with resolution of 30 meters. Study estimate potential tree carbon stored forest by using remote sensing system using Landsat 8 OLI satellite imagery. Regression analysis of actual biomass calculation using allometric equations developed lowland heterogeneous tropical forests with vegetation index NDVI (Normalized Difference Vegetation Index) and ARVI (Atmospherically Resistant Vegetation Index). Determination best of model by calculating the lowest standard error of estimation of each regression model by calculating carbon 50% from above-ground biomass. Estimation carbon potential in study area using linear regression model on the transformation the ARVI vegetation index (Y = 443.24 ln(X) + 576.93). The amount of biomass that The highest forest in the study area is 225.37 Ton/ha with the highest carbon potential of 112.69 Tons/ha. The forest carbon potential value obtained is classified into 5 classes using the Natural Breaks (Jenks) method facilitate at process estimating forest carbon potential so knowing distribution carbon of forest.

Spatial analysis of soil quality using geospatial techniques in Botanic Garden of Indian Republic, Noida, Uttar Pradesh, India

Ex-situ conservation places such as botanical gardens require sufficient soil quality to support introduced species from various phytogeographical zones. The soil quality of the Botanic Garden of Indian Republic (BGIR), Noida, Uttar Pradesh, was evaluated to quantify soil nutrients. The dependency of one nutrient on the other nutrients was investigated using Pearson correlation and Multilinear regression analysis (MLRA). At the 0.05 level of significance, the nutrients Log10S and Log10EC (r = 0.97), N and OC (r = 0.98), Mn and OC (r = 0.97), Mn and N (r = 0.92), Ca and pH (r = − 0.91), Cu and Fe (r = 0.94) were found to be associated. Correspondence Analysis (C.A.) has been performed to find the association of soil elements with the soil type of study site. The spatial indices like NDVI (Normalized Difference Vegetation Index), EVI2 (Enhanced Vegetation Index), ARVI (Atmospherically Resistant Vegetation Index), NPCRI (Normalized Pigment Chlorophyll Index), RDVI (Renormalized Difference Vegetation Index) have shown significant correlation with the Log10S, Mg, Log10Zn, B and Fe respectively (with respective Pearson correlation coefficient r = 0.88, r = − 0.90, r = − 0.93, r = 0.91, r = 0.92 at P < 0.05). ARVI, along with other indices SCI (Soil Composition Index), NDMI (Normalized Difference Moisture Index), and MSAVI (Modified Soil Adjusted Vegetation Index), are also the predictor variables for Log10Zn (r = − 0.89, r = − 0.88 r = 0.92 at P < 0.05 respectively). MAVI2 (Moisture Adjusted Vegetation Index) positively correlates with OC, Mn content (r = 0.91, r = 0.93 respectively). MSAVI is negatively interrelated with Ca (r = − 0.89), SCI is negatively interrelated with Log10 K (r = − 0.98), BSI (Bare Soil Index) is positively associated with pH (r = 0.91), and negatively with Ca (r = − 0.93). At the same time, other indices like SAVI (Soil Adjusted Vegetation Index), SATVI (Soil Adjusted Total Vegetation Index), NDWI (Normalized Difference Water Index), and DVI (Difference Vegetation Index) have failed to explain the presence of soil nutrients based on spectral reflectance. This study is important for understanding the changing nutrient status of soil at the conservation site for successfully establishing plants from different phytogeographical zones.

Multispectral imagery for detection and monitoring of vegetation affected by oil spills and migration pattern in Niger Delta Region, Nigeria

Oil spills in the Niger Delta area can be detected and monitored using this novel technique. Landsat 5 and 8 images were used to assess various vegetation stress, such as Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index, Atmospheric Resistant Vegetation Index, Green Near Infrared and Green Short-wave Infrared, from the spill site in 2019, non-spill site (1992 pre-oil spill) and 2020 post-oil spill events, respectively. There is a substantial difference (p-value &lt;0.005) in the vegetation conditions at the Spill Site and the non-spill Site in 2020 in terms of NDWI, SAVI, ARVI2 and G-NIR and G-SWIR. NDVI, SAVI, ARVI2, G-NIR, G-SWIR, and G-SWIR. There is a very significant difference in vegetation conditions between the pre-spill event and the post-spill event in 2020 (p-value &lt;0.005). The oil spills' migration patterns and flow directions indicate from north to south along the runoff water using SRTM data. The sentinel 1 data revealed visualization of the flooded areas, including water surfaces that are stable around oil pipelines and the surroundings, using calibration threshold and the RGB band method to distinguish flooded areas from permanent water bodies. This was used to map areas affected by the oil spill on land and water bodies for proper environmental assessment before and during the flood of the oil spill environment. Multi spectral imagery is therefore a veritable tool for detection, response and monitoring of oil spills from pipelines.

Prediction of regional vegetation cover using spatial image features and semantic segmentation

Vegetation is an essential part of our ecosystem; it also determines health of our planet. According to “The State of the World’s Forests 2020” only 31% of the global land area is covered with vegetation. This paper presents a superior way of representing vegetation cover in a region by utilizing Atmospherically Resistant Vegetation Index (ARVI) as vital features out of multispectral satellite images. These images comprise Green, Blue, Red and Near Infrared bands data which was further utilized by U-Net to efficiently segment satellite images. In remote sensing greenery of environment is traditionally determined using NDVI, one of the critical imperfections with this method is that it is liable to compute inaccurate values as a consequence of variations in soil, air moisture and shadowing affected by varying incidence angle of sunlight. On the other hand, ARVI is immune to such flaws and integration with deep learning provides a better solution for segmenting regional vegetation and predicting its coverage up to some extent. This method can be employed for predicting vegetation in any region along with assisting in events such as repopulating trees and urban planning while conserving beauty of our nature.&#x0D;

Assessment of Land Cover Changes in Litumbandyosi-Gesimasowa Game Reserve using Remote Sensing and GIS

As countries in Sub-Saharan Africa strive to reduce deforestation in Miombo woodlands, it is essential to use the appropriate, reliable, and cost-effective tools for assessing land cover changes. This study employed Remote Sensing and GIS techniques to assess land use and its changes in the Litumbandyosi-Gesimasowa Game Reserve between 1990 and 2020. The tools employed were GEE and Collect Earth. The study employed Sentinel-2 and Landsat-5 TM imagery and also incorporated the Atmospheric Resistant Vegetation Index (ARVI) for improving classification by overcoming the effects of Non-Photosynthetic Vegetation (NPV) and phenology. The study produced highly accurate land cover maps, with an overall accuracy of 99.53% and a kappa coefficient of 98.11% in 1990, 99.84% and a kappa coefficient of 98.69% in 2011, and 92.10% and an 89.62% kappa coefficient in 2020. The findings of the post-classification revealed an alarming change in land cover over the last 30 years, with heavy forestland decreasing by 10.77%, shrubland increasing by 12.19%, and grassland increasing by 13.35%. Furthermore, farmland expanded by 4.58%, barren land grew by 5.82%, and wetlands grew by 0.74%. Significant agents of change have been identified as forest fires, overgrazing, crop farming, and mining.

Applicability of different vegetation indices for pasture biomass estimation in the north-central region of Mongolia

A dependable pasture biomass estimation is critical to prevent the pasture shortages for decision-makers at all levels in Mongolia. Remote sensing technology is expected to be capable of deriving such estimates. Therefore, in this study, we produced and compared linear regression models between above-ground pasture biomass and seven vegetation indices using close-range spectral measurements in the forest-steppe zone, which is one of six vegetation zones of Mongolia. The results indicated that the atmospherically resistant vegetation index (ARVI) (R2 = 0.62; p < 0.001) showed the highest fit with above-ground biomass in this particular landscape. The dominant perennial grasses in the sampled areas were Stipa krylovii Roshev. and Artemisia frigida Willd., which are commonly grazed in the summer and winter. Therefore, we concluded that the ARVI is the most suitable candidate for estimating pasture biomass in a landscape similar to that widely found in north-central Mongolia. This research will be applicable for pasture monitoring and natural resource studies in Mongolia.

Synergic use of Sentinel-1 and Sentinel-2 data for automatic detection of earthquake-triggered landscape changes: A case study of the 2016 Kaikoura earthquake (Mw 7.8), New Zealand

Earthquakes can trigger numerous landslides and cause other significant changes in the landscape over large areas. This study presents a new processing scheme combining radar (Copernicus Sentinel-1) and optical satellite data (Copernicus Sentinel-2) to quickly and easily map landscape changes such as landslides, coastal uplift and changes in water bodies caused by a severe event such as an earthquake. The processing scheme has been tested for the 2016 Kaikoura earthquake (Mw 7.8), New Zealand, which impacted vast and mostly inaccessible areas, causing hundreds of landslides. The workflow combines the following change-detection methods: i) Sentinel-1 amplitude change detection ii) Sentinel-2-based detection of non-vegetated areas that occurred after the event using the atmospherically resistant vegetation index (ARVI). To get a more complex view of the surface changes caused by the Kaikoura earthquake, the available online services and tools were further tested (open source via the European Space Agency) allowing automatic detection of vertical displacements and deformations. It was concluded, that the above-mentioned approaches facilitated the assessment of earthquake-triggered changes in a comprehensive manner. The methodology is an example of how to detect earthquake landscape changes in an automatic and rapid manner. The new processing scheme for the synergic use of Sentinel-1 and Sentinel-2 data has high potential to be used for operational and scientific purposes, since it relies on globally available, free data and provides high spatial and temporal resolution. The results can be obtained and made available only a few days after an event, therefore providing significant insights into earthquake impact assessment and may also be helpful for prioritizing field work.

Above ground biomass carbon assessment using field, satellite data and model based integrated approach to predict the carbon sequestration potential of major land use sector of Arunachal Himalaya, India

The land-use sector needs special importance owing to its ability to store and emit carbon back to the atmosphere. Land-use changes and their correlations could elucidate conditions that put forests at risk of conversion to other land uses. The impact of rapid forest cover change tends to reduce the percentage of forest cover, thereby reducing the potential for carbon storage in woody biomass. The study was conducted in the state of Arunachal Pradesh to estimate the above-ground biomass, carbon pool and sequestration potential of major land-use sectors. The above-ground biomass of selected land-uses viz. dense forest, moderately dense forest, open forest, plantations, Jhum >5 years, Jhum <5 years and current jhum are 332.28 t ha−1, 246.63 t ha−1, 145.36 t ha−1, 179.31 t ha−1, 149.63 t ha−1, 55.40 t ha−1, 16.84 t ha−1 respectively. The developed model is derived from Soil Adjusted Vegetation Index (SAVI) and Atmospherically Resistant Vegetation Index (ARVI) for prediction of above-ground biomass (AGB) (R 2 = 0.85, p < 0.05). The resultant R 2 value of 85% predicts that 79% of accuracy could be assumed by the model. The RMSE of the model was 53.21 t ha−1, having no multicollinearity problem keeping tolerance (0.49) and VIF (2.80). The spatial AGB density map was predicted using a step-wise linear regression model which used AGB, SAVI and ARVI of the corresponding sample plot location. The implication of the land-use change revealed that about 84% carbon will be lost from dense forest once it is converted to Jhum (Jhum is the alternate name of Shifting cultivation in India) followed by Jhum <5 years (65%). It is pertinent to mention here that, the present analysis will help the policymakers in visualizing a proper developmental goal to regulate land-use changes for achieving the higher carbon stock and maintaining balance in the global climate scenario.

Modelling vegetation health and stress using hypersepctral remote sensing data

Vegetation health mapping is extremely helpful for distinguishing nuisance and scourge conditions and it is additionally valuable in surveying zones of lumber gather. Decline in forest health occurs due to many reasons, including land-use intensification, forest disturbances and bad management practices at various scale that can be assessed and monitored using geoinformatics. Among various remote sensing (RS) techniques, the use of hyperspectral band information suits more for vegetation stress and health analysis and possesses better accuracy. Besides, RS gives lot of opportunity to monitor and manage natural resources at multi-temporal, multi-spectral and multi-spatial level. In this study, we evaluated the applications of hyperspectral RS for forest health assessment and mapping in terms of vegetation health analysis, stress and fire fuel vegetation analysis and linked these to forest management. We analysed the vegetation health of the Lonar forest (Maharashtra) using hyperspectral imagery using ENVI software for the management of forest and its conservation in terms of healthy and stressed vegetation using different indices. The background studies suggest that a forest displaying low pressure conditions comprise sound vegetation, although a forest under high pressure conditions gives the indication of dry and kicking the bucket plant material, exceptionally thick and inadequate overhang and wasteful light use. We examined different vegetation models to compare and identify the index which resulted showing the best accuracy. We executed six Vegetation Index models (VIM) i.e. Enhanced Vegetation Index (EVI), Sum Green Index (SGI), Moisture Stress Index (MSI), Structure Insensitive Pigment Index (SIPI), Atmospherically Resistant Vegetation Index (ARVI) and Normalized Difference Vegetation Index (NDVI) for evaluation of the timberland strength of the woods locale. The study suggest that each vegetation model index has its specific expression for healthy and stressed vegetation and its own suitability for specific uses and also some limiting factors. We argue on the importance to select an appropriate vegetation model to evaluate vegetation health and stress that must be based on environmental conditions, forest composition, leaf type and other biophysical factors.

Geo-environmental monitoring and 3D finite elements stability analysis for site investigation of underground monuments. Horemheb tomb (KV57), Luxor, Egypt

The Valley of the Kings (KV) is a UNESCO world heritage site with more than thirty opened tombs. Since the first tombs were constructed, at least 24 historical flash flood events has been identified, each of which has been contributed to the destruction and deterioration of the tombs. Recently, most of these tombs have been damaged and inundated after 1994 flood. In order to understand the Geo-environmental impact mainly the past flash floods due to the intensive rainfall storm on the valley of kings and the long-term rock mass behavior under geostatic stresses in selected Horemheb tomb (KV57) and its impact on past failures and current stability, Remote sensing, GIS, LIDAR, 3D finite element stability analysis and rock mass quality assessments had been carried out using advanced methods and codes. Our work provides environmental satellite space views via landviewer Erath Observation System (EOS) Platform with passive and active sensors which include the Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Atmospherically resistant vegetation index (ARVI), Green Chlorophyll Index (GCI), Normalized Burn Ratio (NBR), Normalized Difference Snow Index (NDSI), Light Detection And Ranging (LIDAR) images, Terrain (DEM) Digital Elevation Models, 3D geological maps. In other hand experimental and numerical geotechnical evaluations and modeling of the rock mass of these underground structures and their surroundings have been executed. We estimated the rock mass quality of the different members within the Thebes limestone and Esna shale formations using the mechanical testing and Rock Mass Rating (RMR), rock quality system (Q-system) and Geological Strength Index (GSI) systems. Our recent analyses show that the KV57 rock- cut tomb at Luxor has been cut into poor to very poor quality marl shale masses due to the impact of flash foods. Rock failures of ceilings and pillars were frequently facilitated by local, unfavorably oriented persistent discontinuities, such as tension cracks and faults. Other failures were related to the disintegration of the marl limestone and Esna shale Formations into individual nodules upon weathering. Our data suggest that, in ancient Egypt monumental tomb construction, low-strength rock masses rarely resulted in modifications of the planned tomb design in order to minimise the risk of rock falls and to prevent collapses. The current flood protection measures are not enough. For this two following measures are proposed 1—to rise the current wall by 50 cm. 2—to fill the depression by reshaping bathymetry.

A Validation Experiment of the Reflectance Products of KOMPSAT-3A Based on RadCalNet Data and Its Applicability to Vegetation Indexing

Surface reflectance products obtained through the absolute atmospheric correction of multispectral satellite images are useful for precise scientific applications. For broader applications, the reflectance products computed using high-resolution images need to be validated with field measurement data. This study dealt with 2.2-m resolution Korea Multi-Purpose Satellite (KOMPSAT)-3A images with four multispectral bands, which were used to obtain top-of-atmosphere (TOA) and top-of-canopy (TOC) reflectance products. The open-source Orfeo Toolbox (OTB) extension was used to generate these products. Next, these were subsequently validated by considering three sites (i.e., Railroad Valley Playa, NV, USA (RVUS), Baotou, China (BTCN), and La Crau, France (LCFR)) in RadCalNet, as well as a calibration and validation portal for remote sensing. We conducted the validations comparing satellite image-based reflectance products and field measurement reflectance based on data sets acquired at different times. The experimental results showed that the overall trend of validation accuracy of KOPSAT-3A was well fitted in all the RadCalNet sites and that the accuracy remained quite constant. Reflectance bands showing the minimum and maximum differences between the sets of experimental data are presented in this paper. The vegetation indices (i.e., the atmospherically resistant vegetation index (ARVI) and the structure insensitive pigment index (SIPI)) and three TOC reflectance bands obtained from KOMPSAT-3A were computed as a case study and used to achieve a detailed vegetation interpretation; finally, the correspondent results were compared with those obtained from Landsat-8 images (downloaded from the Google Earth Engine (GEE)). The validation and the application scheme presented in this study can be potentially applied to the generation of analysis ready data from high-resolution satellite sensor images.