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Abstract: Abstract Rubber (Hevea brasiliensis (Wild. Ex Adr. De Juss.) Muell. Arg.) is emerging as a fast-expanding plantation crop in India and Southeast Asia. Traditionally, aboveground biomass (AGB) is estimated from forest type or crown density stratification by the Forest Survey of India (FSI) and does not explicitly account for standing age. The present study estimates the AGB and carbon (C) stock of natural rubber (NR) plantations in Tripura, India, which were estimated to cover 93 thousand hectares (kha) in 2021 using remote sensing. A multi-year satellite data-based rubber plantation age-class map was used with measured AGB to generate age-based rubber AGB and C-stock maps with 5-year interval age classes. The total carbon stored for all age group rubber plantations was found to be 2.8 Tg. State-level forest cover and type statistics from the Forest Survey of India (FSI) biannual reports, i.e. India State of Forest Report, were used to understand the dynamics of the forest over the past two decades. This study indicates that the expansion of rubber plantations was accompanied by a loss in natural vegetation and a reduction in standing pools. While India is committed to reducing carbon emissions, and NR plantations have the potential to be an important source of C-stocks at the state and national levels, results indicate that this study site has undergone significant changes in natural forest cover and type. The developed approach may be utilized in practical applications for accurate C-stock accounting in other managed forests. PubDate: 2023-09-15
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Abstract: Abstract Dams are constructed for drinking water, irrigation purposes and to generate hydropower, but it has a great impact on river geomorphology as it affects runoff and sediment loads. The Chandil dam located near the steel city Jamshedpur in India with an area of 17,603 m2, and a storage capacity of 1963 hm3 is the lifeline of nearly 1.3 million people and is crucial for a country like India witnessing an increase in water stress. Evidence of erosional proxies is visible around Chandil Dam. Very few studies can be found that focus on soil loss due to massive erosion–deposition processes in and around Chandil Dam. Therefore, in this study, GIS and remote sensing techniques have been integrated with the Universal Soil Loss Equation model to estimate soil loss and to prepare a catchment area treatment plan for Chandil Dam. Results show that sub-catchments 3 and 4 witness a higher degree of erosion with a total soil loss of 1,094,928.69 and 960,252.23 t/year, respectively. The catchment's projected average yearly soil loss is 14.21 t/ha/year. Some of the recommended measures for erosion control that we recommend include shelter belts, erosion control fences, contour furrows, sandbags, guard walls/caged rip rap, diversion channels and rock chutes due to their ability to mitigate the impact of wind on soil erosion and moisture loss. We anticipate that the findings will benefit various Water Resource Departments across India as the methodology is replicable, and recommendations can be improvised locally. PubDate: 2023-09-08
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Abstract: Abstract Extracting road maps from high-resolution remotely sensed imagery has many practical applications: improving connectivity in remote areas, monitoring urban expansion, and providing aid to disaster-prone regions. Despite the abundance of satellite and aerial imagery, substantial obstacles such as opacity, shadows, inter-class similarity, and missing roadways persist, necessitating immediate attention from researchers. This paper proposes a memory-efficient end-to-end convolution neural network-based architecture called ConnectNet that exploits the powerful features of the Res2Net model. Multi-scale features within every residual block are captured by the hierarchical design, enhancing the proposed model's representation ability. A new block, Stacked Feature Fusion, is proposed having dilated convolution layers of different rates stacked with the squeeze and excitation blocks. Both long-range and narrow-range dependencies are captured by this block, minimizing the boundary and edge loss issue. A new loss function, collective loss, is introduced that combines dice coefficient, binary cross-entropy, and Lovasz sigmoid loss functions which further improves the convergence time and resolves the class imbalance issue. Extensive experiments have been conducted to demonstrate and compare the proposed model's results with other road extraction methods on two publicly available Massachusetts Road Dataset and SpaceNet 3 Road Network Detection Dataset. The quantitative and visual results show that the proposed model outperforms state-of-the-art methods by a significantly large margin. PubDate: 2023-09-08
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Abstract: Abstract Deep learning frameworks have been applied to hyperspectral image classification (HIC) problems more and more in recent years, with excellent results. Existing network models, on the other hand, have a higher model complexity and take more time. The link between local spatial features is often overlooked in traditional HIC algorithms. This article presents a novel Lightweight Cascaded Deep Convolutional Neural Network (LC-DCNN) that describes the spatial as well as spectral characteristics of the hyperspectral images. The performance of the proposed LC-DCNN is validated for different spectral band reductions techniques to minimize the computational complexity of HIC such as Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA). The effectiveness of the proposed algorithm is validated on the Indian Pines and Salinas dataset for the vegetation and agriculture detection field based on accuracy, recall, precision and F1-score. The proposed approach provides 99% and 99.63% accuracy over Indian Pines and Salinas datasets, respectively, over the traditional state of arts employed previously for the HIC. The performance of the suggested LC-DCNN decreases for a larger number of classes. In the future, the performance can be improved for the real time dataset by considering a larger dataset consisting of larger objects. PubDate: 2023-09-08
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Abstract: Abstract Poyang Lake is one of the largest freshwater lakes in China and the only two remaining lakes in the middle reaches of the Yangtze River. It is an internationally important wetland with important ecological value. In recent years, due to the influence of natural and human factors, the ecology of Poyang Lake International Wetland has been greatly damaged. Using relevant research models and methods to study and predict land-use change can provide a scientific basis for relevant departments to manage land and has important demonstration significance for biodiversity conservation and ecological function restoration of wetlands with important ecological value. Based on Landsat remote sensing images from 1986 to 2020, land-use information data were obtained by supervised classification interpretation, and five land-use type maps with an interval of about 8 years were generated by ENVI and ArcGIS software. The land-use change of Poyang Lake wetland was analyzed by using land-use transfer matrix and land-use dynamic attitude. The land-use distribution in 2030 was predicted by the FLUS model. Based on the gray correlation model, the driving force of land-use change was discussed. The results show that the wetland land-use types in Poyang Lake will change greatly from 1986 to 2020: construction land, mudflat, paddy field, and dry land changed significantly and showed an increasing trend. The area of water and grassland decreased on the whole, and the transferred area was large, which was mainly transferred to construction land, paddy field, and dry land. The area of woodland increased slowly, but the change range was not large from the perspective of dynamic attitude. The change of wetland area of reed flat decreased first and then increased, and the overall land-use change was relatively gentle. Through the FLUS model prediction, it is found that the water area of the study area will be greatly reduced in 2030, the area of woodland and reed flat will be greatly increased, and the ecological situation will gradually improve. According to the gray correlation analysis, the total population and annual precipitation are the main driving factors of land-use change in the Poyang Lake wetland. This study studies the land-use change and driving force from the regional wetland scale, which can provide a theoretical basis for the future international important wetland protection and land resource management of Poyang Lake. At the same time, it provides a reference for further regional wetland habitat protection and ecological network construction. PubDate: 2023-09-05
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Abstract: Abstract Unmanned aerial vehicles (UAV) imagery has proved to be useful in the efficient protection and management of mangrove forests. However, there have been few attempts to show that UAV-RGB images may be used for mapping trees at the species level. Our objective, in this study, is to identify two mangrove species using object-based classification. Height information was used to segment trees to obtain maximum spectral purity in each segment for classification using the Canopy Height Model (CHM) in Sirik mangrove forest (Azini Creek) located in southern Iran. The object-based classification (using a random forest algorithm) of UAV imagery with dominant mangrove features (i.e., Rhizophora mucronata, Avicennia marina, ground/sand, and water) achieved an overall accuracy (OA) of 98% and Kappa coefficient of 0.97. The results showed that the overall accuracy and Kappa were upgraded from 94 to 98% and 0.91–0.97, respectively. The water and ground classes were identified with a producer’s accuracy of 100%. The random forest algorithm accuracy for both of the trees was more than 90% (produce accuracy 95 and 98% and user accuracy 98 and 97% for R. mucronata and A. marina, respectively). The results demonstrated proof for the potential and usefulness of spectral data, i.e., UAV–RGB derived orthomosaic, and structural, i.e., CHM data for mangrove trees identification. PubDate: 2023-09-05
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Abstract: Abstract The distribution direction of aerial objects is arbitrary compared to objects in natural images. However, the existing detectors identify and locate the targets by relying on the shared features, which leads to the contradiction of regression and classification tasks. To be specific, the classifier suppresses rotation-sensitive features, while the regressor relies on rotation-variable features. To address the above contradictions, a Spatial Dual Network (SD-Net) is proposed, which consists of two modules: Polarization Dual Pyramid Module (PDPM) and Spatial Coordinate Attention Module (SCAM). In the SCAM module, to be able to capture channel-related features and global spatial features in different directions, an attention module is built with different convolution kernels that slide in both horizontal and vertical directions. In addition, the polarization function in the Polarization Dual Pyramid Module can split features into features suitable for classification and regression tasks for use in the classifier and regressor of the network, enabling more refined detection. The experimental results on three remote sensing datasets (i.e., DOTA, UCAS-AOD, and HRSC2016) demonstrate that the proposed method achieves higher performance on detection tasks while maintaining high efficiency. PubDate: 2023-09-03
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Abstract: Abstract Compression of high-resolution space video is one of the important concerns in many remote sensing application areas. As high-resolution video carries a lot of valuable information (which is required for detailed analysis), therefore compression of these videos is a challenging task. Many existing video compression algorithms are not applicable for high-resolution remote sensing and medical video due to too much information loss. Here, a fast, optical processing (phase grating)-based video compression technique has been discussed. The selected video is divided into frames initially. Each video frame is further divided into three planes—red, green, and blue which are to be modulated using phase grating. As a result of the modulation, three spectrums are generated for each frame. Only one spectrum is taken into consideration and sequentially placed on the canvas. At the receiving end, by applying inverse Fourier transform at selected spots of the canvas, planes are extracted and frames are reconstructed using the extracted planes. For quality checking, Peak Signal to Noise Ratio (PSNR) and correlation coefficient (to measure the closeness with original images) methods are used. The entire process is completed in 0.838 s (for 60 fps space video); hence, it can be expressed as a fast process. So, compared with a few existing video compression algorithms, this system has advantages like low noise affection, fast processing, etc. PubDate: 2023-08-28
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Abstract: Abstract Fault activity property is one of the main reasons caused by the motion of crustal fault, and the research of fault activity characteristics has become an important direction in the study of earthquake prediction, which is an important source of new theory and new method in earthquake prediction science. The disastrous May 12, 2008, Mw7.9 Wenchuan earthquake in the Longmenshan fault zone (LFZ) took the local population as well as scientists by surprise. To analyze the temporal and spatial deformation characteristics of the Mao county–Wenchuan fault, the Beichuan-Yingxiu fault, and Jiangyou-Guan County in the central south segment of the LFZ after the Wenchuan earthquake, the SBAS-InSAR method was adopted to derive surface deformation rate with 20 Envisat SAR images acquired between August 6, 2007, and July 26, 2010. Our analysis shows that the overall movement speed of LFZ increased significantly, reaching about − 40 mm/year, which shows a dextral compression strike-slip. From west to east, the velocity changes of each section are different, and the movement of the front-range fault is dominant in the middle and south sections of Longmenshan, which is close to the epicenter. The reason may be related to the fact that the middle and south section of Longmenshan is the epicenter of the earthquake. The southern and mid-southern sections of the LFZ change from west to east, and the direction of profile movement increases gradually. In the middle and north segment of the fault zone between the two, the variation characteristics are not obvious. To a certain extent, it indicates that the fault is characterized by dextral strike-slip compression in the southern segment and the mid-southern segment. The difference in the profile movement direction in the LFZ may be related to the stress release of the southern segment of the LFZ after the earthquake while the movement of the northern segment of the LFZ was blocked. The research results will reveal the mechanism of earthquake pregnancy and earthquake generation of LFZ, enrich the knowledge of the impact on the aftershock distribution of the Wenchuan earthquake, and promote the development of earthquake prediction research. PubDate: 2023-08-17
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Abstract: Abstract Aboveground biomass (AGB) estimation is crucial for assessing forest productivity, carbon sequestration, and functional diversity. Integrating optical remote sensing data with field inventory to estimate AGB saturates at high biomass. However, transformed optical imagery is effective in tropical forest AGB estimation, especially for eliminating saturation effects. Recent AGB mapping research uses diverse predictor data fusion and advanced machine learning models. In this research, we extracted texture parameters using the Gray-level co-occurrence Matrix from the first two principal components of Sentinel-2's multispectral bands. We also calculated the Normalized Difference Vegetation Index, Visible Atmospheric Resistance Index, and Leaf Area Index for the analysis. Elevation, slope, and aspect data from SRTM DEM and GEDI-Landsat tree height product were used as ancillary datasets due to their significant impact on AGB. Neighborhood statistics (3 × 3 pixels) of predictor variables were calculated to account surrounding contributions of the focal plot. A total of 60 plots of 0.1 ha were established across the landscape where 70% and 30% of randomly selected plots were used for random forest model development and validation respectively. The final model explained AGB variability significantly (correlation coefficients = 0.72, root mean square error (RMSE) = 69.18 Mg/ha, mean absolute error (MAE) = 58.22 Mg/ha) with an uncertainty observation of 41.3 percent relative RMSE (rRMSE). The study concluded that combinations of texture and spectral variables derived from Sentinel-2 optical imagery along with physical variables are found effective in AGB mapping. The method and obtained results were promising and appeals to its replicability for building a generalized AGB model. PubDate: 2023-08-14
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Abstract: Abstract Forest aboveground biomass (AGB), an essential climate variable, is a major priority for the delivery of usable products from multi-sensor remote sensing data. Recent AGB global products such as ESA-CCI and GlobBiomass could provide critical inputs for carbon sequestration, emission and climate change studies. While these have been developed and tested with global field datasets, very little use of Indian field measurements and validation with Indian observations has been reported. In this study, a database of field measurements was created, of 1 ha (135 plots), clustered plots of 0.1 ha (101 plots) and 582 plot AGB of 0.1–0.04 ha from the published literature and used for validating ESA-CCI 2018 & 2010 and Santoro-2010 (Santoro et al., Earth System Science Data 13:3927–3950, 2021) datasets. Validation of mean AGB for larger areas such as regional and national estimates was carried out with field-based national forest inventory results of Forest Survey of India (FSI), which indicated an RMSE of 13.47 Mg/ha at zone level and a bias of 48.82 Mg/ha for AGB density and 983.96 Mt in AGB pool at national level. The plot-level comparison at 1 ha plots had RMSE of 215 Mg/ha. However, data from smaller plots did not show any correlation with the AGB product. In general, all products exhibited saturation and were unable to capture AGB of plots above 250 Mg/ha. The large area mean AGB was underestimated when compared with national forest inventory results. Expanding the Indian datasets for use in the development and validation of AGB models, updating the global datasets with Indian observations through new data integration approaches is suggested. PubDate: 2023-08-14
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Abstract: Abstract A proper estimate of glacier stored water is helpful to assess the long-term availability of water in any river basin. A method based on the laminar flow and volume–area scaling is used to estimate glacier stored water in the Chenab basin. Here, we used Landsat 8 images from 2015 to 2019 for the estimation of glacier surface velocity. The laminar flow technique needs surface velocity and slope of the glaciers, and other parameters which are assumed to be constants. The surface velocity of 223 glaciers was assessed by using the sub-pixel correlation technique, applied to Landsat 8 images. The slope was estimated using ASTER DEM. We calculated the average surface velocity and thickness as 11.12 ± 0.05 m a−1 and 54.56 ± 7.4 m, respectively, and the maximum ice thickness as 470 ± 63.9 m. Moreover, we have developed a volume–area scaling equation using laminar flow estimates and applied it to the remaining 1945 glaciers. The glacier-stored water estimated for 2168 Chenab glaciers covering 2519 ± 125.8 km2 area has been estimated as 145.61 ± 26.2 Gt. Our investigation provides decent estimates of glacier stored water, which helps to advance hydrological studies, thereby developing innovative mitigation strategies. PubDate: 2023-08-10
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Abstract: Abstract Tiruppur is one of India’s major textile cities in Tamil Nadu, India, and it is seeing fast urbanization. Tiruppur, like other cities throughout the world, confronts urbanization issues. The prediction of urban expansion supports planners in identifying the urbanization trend and thereby planning appropriately. There are several approaches for forecasting urbanization trends, and this study attempts to forecast the urbanization trend using logistic regression (LR) and frequency ratio (FR) to create the Urban Growth Probability Index (UGPI) map for the year 2001. The FR and LR models can be used to predict the urbanization trend for the year 2021. For the current study, elevation, slope, aspect, the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Built-up Index (NDBI), distance from roads, rivers, lakes, the Land Use Land Cover (LULC) map for the years 1991 and 2001, and the population density map for the years 1991 and 2001 are regarded as independent variables and urban growth (UG) from 1991 to 2001 as the dependent variable. The UGPI map of the LR and FR models clearly showed population density, NDVI, NDBI, and LULC as isolated factors that have a strong correlation with urban development, and by changing the factors to the model, prediction of urban growth for any year is possible. The results clearly conclude that the urban sprawl is more towards the north and north-western regions due to the presence of more commercial and industrial centres, whereas the southern region shows very less urban sprawl due to the absence of industrial centres and witness more agricultural activities. It is now easier for planners to predict where the real growth in the study area will occur based on the results of the current work in the application of FR and LR models for UGPI attempts to determine the variables that are connected directly. PubDate: 2023-08-01
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Abstract: Abstract Large scale biomass burning like forest fires and crop residue burning can significantly impact the physical environment, including land cover, land use, ecology, habitats, and climate change. We investigated the effect of fire counts on surface Black Carbon mass concentration (BCC) and Tropospheric Columnar NO2 (TCN) over the North Eastern Region (NER) of India in the domain: 20° N–30° N and 88° E–98° E for 15 years from 2006 to 2020 using MODIS, MERRA-2 and OMI data. Significant fire counts are recorded in January, February, March, and April. An average of 65,000 fire counts is recorded in March and April during the 15 years of study over the domain. TCN is high in Mizoram, Manipur, and Nagaland, followed by Assam, Tripura, and Meghalaya in March and April, which varies from 18.79 × 1014 to 29.08 × 1014 cm−2 in March, 10.76 × 1014–15.81 × 1014 cm−2 in January and February, and 12.67 × 1014–14.2 × 1014 cm−2 in April. Spatially averaged BC varies from 1.80 to 2.76 µg m−3 in January and February and 1.82–2.36 µg m−3 in March. BCC is high in Mizoram, Tripura, Manipur, Nagaland, and Brahmaputra valley of Assam than in the rest of the NER. PubDate: 2023-08-01
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Abstract: Abstract Hydro-Estimator (H-E) method-based rainfall products from INSAT-3DR are operationally available at the web portal of the India Meteorological Department (IMD). These high spatial (4 km) and temporal (30 min) resolution products not only play a significant role in monitoring monsoonal rain over India and the surrounding ocean but also show importance in assessing several meteorological events such as tropical cyclones, floods, cloud bursts and thunderstorms. Thus, a comparison of these rain products with in situ observations and other satellite data is important to evaluate their performance. This study details about the validation procedures and discusses the performance of H-E technique-based rain products from INSAT-3DR over India and surrounding oceans during the Indian summer monsoon (June–September) 2020. The performance of products is evaluated using two sets of data (1) in situ network of IMD rain gauges over India and (2) Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) products. Results show that H-E well captures the spatial distribution of rain as depicted by rain gauges and IMERG. H-E shows a probability of detection of > 95% and a false alarm rate of < 25% with in situ rain gauges’ observations on a monthly scale. It provides a correlation of 0.4 and a root mean square error of 54.48 mm/day with respect to IMERG over India and the surrounding ocean. The present study provides a detailed evaluation of the H-E rain for its worthiness for various applications. PubDate: 2023-08-01
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Abstract: Abstract The Tutakdağı (Şebinkarahisar-Giresun) area is located in the southern part of the Eastern Pontides, northeastern Turkey. It contains numerous types of mineralization, examples include massive sulfide, porphyry, skarn, and vein. The Tutakdağı area contains vein-type Pb–Zn deposits hosted by Upper Cretaceous volcanic rocks. Hydrothermal alteration and oxidized weathering products are well exposed in this mineralization area. Due to the inaccessible points, steep slopes, and rugged topography of the area, it is difficult to apply traditional geological field studies. This research addresses the applicability of remote sensing methods to identify and map alteration mineralogy within the area and presents the results of a set of remote sensing investigations, including spectral measurements, band ratios, relative absorption band depth, principal component analysis, and matched filtering techniques on the Landsat-8 OLI and the ASTER data sets. The spectral investigations were performed on the representative altered rock samples and the powder samples obtained during the clay separation processes. The spectra of hydrothermal alteration minerals like kaolinite, halloysite, illite, montmorillonite, kaolin/smectite, illite + smectite, illite + montmorillonite, montmorillonite + smectite, muscovite, chlorite, and calcite and the iron oxide/hydroxide minerals like jarosite, limonite and goethite were identified. A mineral distribution map has been prepared, and all the outputs on these maps contain useful information to track the distributions of hydrothermal alteration minerals, weathering-related iron oxide/hydroxide occurrences surrounding mineralization zones, and the lineament/lineament density. These maps are prepared in a shorter time and with lower costs compared to classical field geological methods and can make a valuable contribution to the exploration of new lead–zinc deposits in the region. PubDate: 2023-08-01
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Abstract: Abstract Hydro-geomorphological maps help to understand the diversity and evolution of landforms and landscapes. The development of geomorphological mapping techniques has been significantly advanced from classical to computer-based digital mapping through methodological development. Manbhum-Singhbhum Plateau is located in the eastern section of the Precambrian-origin Indian Chota Nagpur Plateau. Digital geomorphological mapping with hydrological aspects and investigation of groundwater changes are highly required for the planning and hazard management of any region. In this connection, the main objectives of this study are (1) to classify the landforms by applying the algorithms of Wood & Jennes, 2006, (2) to comprehend the relationship between lithology with structures and landforms through the evolution of time and (3) to investigate groundwater storage changes and recent landcover patterns through google earth engine (GEE) using advanced spaceborne thermal emission and reflection radiometer (ASTER), Terra Climate and Landsat 8 datasets. The result showed that the maximum area of the entire plateau is covered by high ridges and midslope drainage with shallow valleys (36.98%) followed by canyons with incised streams (17.24%). The smallest area coverage is under the upper slope zones with mesas (1.46%). Local midslope ridges and hills in the plains are covered around 6.47%, whereas plains occupy 11.81% of the Manbhum-Singhbhum Plateau. Results have also been verified by collected waypoints. Furthermore, groundwater storage changes showed a negative trend (− 2.5 to − 3.7 cm) in the entire region, while high ridge areas with steep slopes face severe conditions. This research is most helpful for proper landuse planning and applied geomorphological issues. PubDate: 2023-07-29
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Abstract: Abstract Chilika Lake is an optically complex water body experiencing circulations linked to the tidal influx from Bay of Bengal. These circulations causes seasonal variations in biophysical and chemical parameters of the lake, such as water nutrients, salinity and chlorophyll-a (Chl-a). In this study, bio-optical parameters (Chl-a concentration, absorption by colored dissolved organic matter & detritus and backscattering by suspended particles) of Chilika Lake were estimated using multi-spectral remote sensing data of Oceansat-2 Ocean Color Monitor (OCM-2, spatial resolution \(\sim\) 360 m) and Landsat-8 Operational Land Imager (OLI, spatial resolution \(\sim\) 30 m) sensors. Spectral matching technique was implemented on the satellite derived remote sensing reflectance spectra ( \(R_{\text {rs}}(\lambda )\) ) to estimate the bio-optical parameters. Both OLI and OCM-2 revealed that the southern and central portion of Chilika has a relatively higher phytoplankton productivity (8 mg \(\hbox {m}^{-3}\) \(\le\) Chl-a \(\le\) 15 mg \(\hbox {m}^{-3}\) ) when compared to the northern sector. Also, retrieval from both the satellites could capture the presence of a high sediment load in the northern sector. Application of spectral matching technique requires accurate estimation of \(R_{\text {rs}}(\lambda )\) from a satellite image. For this purpose, atmospheric corrections were implemented on both OLI and OCM-2 level-1b data. Processing of OLI data was done using SWIR (short wave infrared) atmospheric correction algorithm with the help of ACOLITE software. For OCM-2, due to the absence of SWIR bands, we used the standard atmospheric correction algorithm with certain modifications to obtain accurate results over the turbid waters of Chilika Lake. \(R_{\text {rs}}\) from OCM-2 were compared with in situ measured reflectance. Both the shape and magnitude of the spectra compared well, with relative errors ranging from 8.75 to 32.78 %. The modified atmospheric correction algorithm is useful for multi-spectral data that does not have SWIR bands and can be implemented over any inland and coastal water bodies having clear oceanic pixels in its vicinity. PubDate: 2023-07-19
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Abstract: Abstract Wetlands play an important role by providing multidimensional ecosystem services to the society. In recent decades, the world has experienced tremendous urban expansion, posing threats to wetland ecology. To comprehend and evaluate these threats, a study was conducted in the Bhoj wetland of Bhopal, India. In 2002, the Ramsar convention designated the Bhoj wetland, which includes Upper and Lower Lake, as a wetland of international importance. The Upper Lake serves as the city's lifeline and provides 40% of the potable water used in the city. However, this wetland has faced anthropogenic threats in recent decades. The purpose of this study was to map and monitor the vulnerable areas around the Bhoj wetland and assess the changes over a period of 30 years. Remote sensing and geographic information system methods were used in this study to identify changes in land use and land cover (LULC) patterns for the years 1990, 2000, 2013, and 2020. The maximum likelihood classifier was used in supervised classification to create the LULC maps. The result showed that Built-up, Barren land, and Marshy land increased by 16.97 percent, 4.79 percent, and 0.7 percent, respectively, while Agricultural land, Vegetation, and Water bodies decreased by 11.84 percent, 10.5 percent, and 0.13 percent, respectively, from the year 1990–2020. The outcome of this study showed the vulnerable areas in the wetland catchment by change assessment in LULC, drainage density and land fragmentation. This would guide future urban development policy for LULC management, considering the wetland protection goals. PubDate: 2023-07-11 DOI: 10.1007/s12524-023-01728-7
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