Journal of the Indian Society of Remote Sensing最新文献

The Western Himalayas receive significant snowfall, and it's essential to monitor the snow cover for various purposes like managing water resources, conducting hydrological research, and predicting avalanches using remote sensing techniques. With the pan-sharpening of different satellite sensor images, the essential features can be obtained to understand the snow cover dynamics. The resampling algorithms play a very important role in the pan-sharpening procedure to highlight the important features of the pan-sharpened dataset. However, the performance of various resampling algorithms for pan-sharpening is very rarely validated with optical and microwave datasets. In this article, the spatial and spectral analysis of different resampling algorithms, i.e., Nearest Neighbour (NN), Bilinear (BI), and Cubic Convolution (CC) resampling, have been performed using the fusion of optical and microwave satellite images. In this study, two datasets, i.e., MODIS (optical) and SCATSAT-1 (microwave) were used over the western Himalayas (i.e., Ladakh, Jammu and Kashmir, Himachal Pradesh, and Uttrakhand). To evaluate the performance of each resampled pan-sharpened dataset, the output is classified using a Support Vector Machine (SVM) classifier and a Spectral Angle Mapper (SAM) classifier. Root Mean Square Error values were computed to quantify the level of agreement between the pan-sharpened datasets and the ground truth data. The result of statistical analysis showed that NN-based pan-sharpened performed better than BI-based and CC-based pan-sharpened classified images with both classifiers i.e., SVM and SAM. This study is important in terms of the effective utilization of the resampling techniques along with pan-sharpening algorithms.

Local climate zone (LCZ) map, an outcome of a supervised classification procedure using satellite imagery, can be generated at different landscape resolutions. Because of the large spatial extent, huge-sized satellite imagery, and fine granularity, it is difficult to analyze supervised LCZ (pixel-classified satellite image) outcome creating a scope for some post-classification tasks. In this paper, we have proposed an entropy-based directional edge algorithm for locating LCZ boundaries, named as DEALB, which creates homogeneous LCZ regions and delineates their boundaries. In DEALB, an image is initially partitioned into superpixels using directional edges considered at different angles (0°, 90°, 45°, and 135°) within a specified spatial scale. Next, similar but spatially cohesive superpixels are clustered to form large homogeneous regions. Spatial cohesiveness, which is a crucial characteristic to be considered in landscape clustering, is implemented by using the breadth-first search and deque data structure. Further, to validate the correctness and pureness of boundaries in the absence of any ground truth image, we have proposed the concept of boundary purity index focusing on spatial contrast inside and outside of LCZ regions. We have demonstrated the algorithm on LCZ classified results for heterogeneous landscape of the city Nagpur in India that has been found useful by domain experts.

With economic development, the emergence of cities, and the growth of transportation, air quality has become a significant concern. This leads to ecological imbalance and threatens the health of millions of people. The city of Oran, due to its importance and economic growth in recent decades, is no exception. The primary objective of this study is to conduct a comprehensive analysis of pollutant trends for the period 2019–2022 and their relationships with surface temperatures. The data for four pollutants from Sentinel-5P, namely ozone (O₃), carbon monoxide (CO), nitrogen dioxide (NO₂), and sulfur dioxide (SO₂), were obtained and processed using the Google Earth Engine (GEE) platform. Given the variability in emissions from year to year, the results from the Air Pollution Index (API) indicate a trend toward moderate to high pollutant concentrations (> 1.54e-2 mol/m²), with the city being the primary source of pollutants. In 2019, 61% of the study area was heavily polluted, with concentrations exceeding 1.54e-2 mol/m². This percentage was 45% in 2020, 58% in 2021, and 44% in 2022, while concentrations below 1.54e-2 mol/m² represented 15%, 26%, 17%, and 30%, respectively. Except for SO₂ (> 40 µg/m³), the levels of NO₂ (~ 10 µg/m³) and CO (< 4 mg/m³) align with the levels recommended by the World Health Organization (WHO). Furthermore, a comparison of the results with surface temperatures shows that, except for O₃, no significant correlation exists. In the context of sustainable development, these findings represent a proactive strategy for understanding pollutant concentrations and formulating effective policies to improve air quality in the city of Oran.

Informed decision regarding cultivating the right crop in the right land will guarantee maximum production, which is the need of the hour to meet the world’s burgeoning food demand and to ensure the sustainability of agriculture. The present study aimed to identify the land suitability for major crops in the semi-arid ecosystem of Palani block in Tamil Nadu by integrating the analytical hierarchy process (AHP) and geographic information system (GIS). Soil slope and various soil characteristics influencing crop growth such as soil depth, texture, drainage, gravelliness, pH and organic carbon were considered for assessing the land suitability. Weights and scores were assigned to the selected criteria and their respective sub-criteria based on their relative significance in influencing crop growth. It was found that soil drainage and texture were the most influencing factors for paddy cultivation, with weights of 0.49 and 0.27, respectively. For field beans, coconut, and guava, texture and depth were identified as the major influencing factors with high weightages ranging from 0.26 to 0.40. Results indicate that about 22% (8627 ha) of the study area was highly suitable for field beans, followed by paddy (18%). In contrast, paddy and coconut registered the largest land area under the marginally suitable class and were deemed unsuitable for about 19% and 21% of the land, respectively. For guava and field beans, respectively 37% and 44% of the land were found moderately suitable while 77% and 76.6% of the land were found suitable. Soil texture, soil depth, and drainage were identified as the major impediments to coconut and paddy suitability. An error matrix was generated by comparing the land suitability derived through the AHP–GIS method with the farmers’ opinions on land suitability for different crops. It indicated a high agreement between the suitability classes and farmers’ opinion for field beans, followed by coconut, guava and rice with kappa indices of 0.64, 0.51, 0.49 and 0.40 and overall accuracy of 75%, 65%, 62% and 60%, respectively. The present study not only helps in identifying suitable areas for crop cultivation, but also recommends land management strategies to each land parcel to improve land productivity and sustainability.

Accurate estimation of atmospheric parameters is crucial for accurate atmospheric correction as well as aerosol retrieval. While spectral responsivity within a spectral band is much higher at central wavelength, the sensor is sensitive across the entire bandwidth, thereby increasing the possibility of differences in atmospheric parameters estimated using only the central wavelength as compared to that using the relative spectral response (RSR). In the present study, an attempt has been made to investigate the differences in the values of different atmospheric parameters and the consequent impact it has on surface reflectance (SR) estimation and aerosol optical depth (AOD) retrieval from Operational Land Imager (OLI) sensor onboard Landsat 8 and Multispectral Imager (MSI) onboard Sentinel 2. The SR has been estimated from the top-of-atmosphere signals using the Simplified and Robust Surface Reflectance Estimation Method (SREM). AOD has been thereafter, retrieved using a simplistic physics-based approach in single scattering approximation. The results suggest that though the difference in RSR and central wavelength derived parameters is quite small in absolute terms, the effect of RSR is more evident in case of green spectral band for Landsat 8 and in blue band for Sentinel 2. The difference in retrieved AOD is more pronounced in case of OLI as compared to MSI, with difference being more in green band for the former and in blue band for the latter.

The use of remote sensing technology is essential for identifying and mapping tree species. In species management, remote sensing tools like Unmanned Aerial Vehicles (UAVs) are used because of their short-cycle replication, high-resolution images, and 3D capabilities. The main objectives of this research were to evaluate the ability to use UAV images and the reliability of three Nearest Neighbor (NN), Random Forest (RF), and Decision Tree (DT) algorithms, as well as the ability to differentiate deciduous tree species based on their spectral and structural characteristics. UAV images were obtained and processed, and 3D canopy crown structure features, i.e. DSM, DTM, CHM, and mean slope of the canopy crown, were prepared for object-based classification. The results showed that adding the structural feature of CHM, DSM, and slope, as combined with multispectral bands, could improve the results compared to using only multispectral bands for NN and RF algorithms. However, the DT algorithm provided the highest classification accuracy with an overall accuracy of 69.04% and a Kappa coefficient of 0.595, using spectral characteristics of the main bands, vegetation indices, and texture analysis. In contrast to the DT algorithm, which does not improve classification results by using tree structural properties, CHM shape properties in combination with their spectral properties can improve classification results. Overall, in dense deciduous forests where all trees have normal spectral reflections during the growing season, UAV images and structural features such as mean slope provide valuable information.

With a teeming population and rapid urban expansion, water demands in large cities are increasing multi-fold. Coping up with such large demands, especially during the post-monsoon season, poses a significant challenge to the line departments. The problem is exacerbated by the depleting ground water levels resulting from high withdrawals exceeding the recharge by large proportions. Bengaluru city, India, is experiencing a shortage of water during the post-monsoon of 2023-24. The present study employed the satellite data based geospatial products and hydrological modelling to analyse the water scarcity manifested in Bengaluru and southern districts of Karnataka. Rainfall and runoff analysis revealed the deviations from long-term average conditions, during 2023-24, leading to decreased surface water runoff, lower inflows into reservoirs/water bodies & reduced water availability during 2023-24. Assessment of inflow patterns into key reservoirs underscores the impact of reduced runoff on water storage. Analysis of water spread dynamics and crop water stress using satellite data highlights the anomalies & severity of the scarcity. The findings underscore the need to use in-season satellite data & geospatial inputs for improved water management strategies and sustainable development practices to ensure urban water security.

This research paper investigates the factors responsible for tropical cyclones’ intensification process, focusing on pre-monsoon cyclones Fani and Yaas. Using the WRF model, the study examines the dynamics and thermodynamics of the two cyclones in three phases: pre-intensification, intensification, and post-intensification. The results are compared with available satellite data sets and it was found that Fani had higher wind speeds and a more structured system with high specific humidity concentrations closer to the storm center than Yaas. Before the RI of Fani, there was a sudden increase in moisture convergence rate, hydrometeors, and diabatic heating, facilitating the formation of cloud ice at the upper level of the troposphere and the release of higher latent heat energy. In contrast, the lack of moisture convergence in Yaas resulted in a weaker updraft and reduced latent heat release. Further analysis suggests that the heating in Fani was more intense and widespread in the inner eyewall region than the core, compared to Yaas. In general, it is also noted that an enhanced bulk kinetic energy (BKE) is a clear indicator for intensification and during the intensification phase, sustained BKE production and moderate dissipation are evident. The latent heating budget parameters indicate a continuous accumulation of moisture with nearly constant moisture consumption during the intensification process. These features are more distinct for Fani compared to the Yaas. The study concludes that during RI, dynamical processes play a dominant role compared to thermodynamical processes.

Landslides are complex geohazards responsible for damage to life, the natural environment, and essential infrastructures like buildings, roads, and transmission lines in mountainous regions. The modeling of topographic input parameters for landslide-related investigations is often based on Digital Elevation Models (DEMs), which serve as a crucial geospatial data source. The present study attempts to analyze the effects of DEMs, obtained from different sources and varying in spatial resolution, on terrain feature estimation and spatial characterization of landslide-affected areas in the Indian Himalayas. Carto-DEM version 3R1 and ALOS PALSAR DEM are used to generate two geodatabases of DEM-derived landslide causative factors, each including digital maps of Elevation, Slope, Aspect, Curvature, Terrain Ruggedness Index, and Distance to Drainage. The generated geodatabases are utilized for conducting a spatial frequency distribution analysis to characterize the selected area into spatial bins with similar topographic characteristics. A comparative study of this analysis reveals that both the DEMs exhibited comparable topographic characteristics on a general level. However, considerable variations are observed when both the geodatabases are scrutinized closely. The results of this study highlight that the quality of the DEM used may affect its usability in a specific investigation and hope to add to the scientific discourse on the effects of DEM on landslide-related studies.

Water induced surface erosion is one of the major factors for causing land degradation leading to massive food insecurity. Therefore, the estimation of water induced erosion risk becomes important mainly in an agrarian watershed. In this 2021 study, water erosion is holistically assessed using erosion models, while the role of topography was analysed through hydro-geomorphometric parameters. Qualitative erosion analysis is performed using the multi-criteria decision-making Analytical Hierarchical Process (MCDM-AHP) tool, while sediment yield is calculated using the RUSLE model. As the empirical formulas are data driven lack of data for smaller region does not provide quite accurate results hence in this experiment a more holistic approach is taken in addressing the erosion through both qualitative and quantitative approach. The morphometric study using a Geographical information system (GIS) showed that the Jumar watershed has rolling and slightly undulating terrain, with mild slope. The hydrological parameters indicated low infiltration capacity of soil and high surface runoff potential indicated that the watershed is at increasing risk (from low to moderate) of gully formation in the lowlands. The analysis of land use land cover (LULC) revealed a significant rise in urbanization, and barren land. Limited vegetation cover was observed during the summer and winter seasons. The RUSLE model showed about 26% of the total geographic area faced erosion between 100–2500 t/ha/year in the watershed. The MCDM-AHP model showed 27.25% of the total geographic area was under moderate to high susceptibility erosion. Both the models showed aggregable results in comparison to each other. It is suggested to plant cover crops during the fallow period and use of biopolymer mulches for land covering. Use of agricultural waste biochar as organic fertilizer is suggested to control erosion and nutrient pollution. This will also improve crop productivity.