A Comparative Analysis of Forest Fire Risk Zone Mapping Methods with Expert Knowledge Abstract Despite repeated occurrences of forest fire, very less scientific studies have been reported in the Indian context especially in Kudremukh region to mitigate and suppress the fire. The objective of this article was to pool the expert knowledge on forest fire triggering factors from officials working in wildlife division in the Western Ghats of India through a questionnaire and to validate the risk zones obtained from three popular fire risk zone mapping methods namely logistic regression, multi-criteria decision analysis, and weighted overlay. Based on the earlier studies and expert knowledge, fire ignition parameters considered are elevation, slope, and aspect, proximity to roads, water bodies and area of human activities, normalized difference vegetation index (NDVI), land surface temperature (LST), and vegetation type. The regression model was based on previous fire occurrences and the other two based on expert’s opinion. The three models were validated and compared using past fire occurrence events. The logistic regression model gave 88.89% of accuracy and that of multi-criteria decision analysis with 74.6% accuracy, and that of weighted overlay method with an accuracy of 68.24% for the specific study area. The logistic regression model is useful in the presence of historical data, whereas expert knowledge is helpful for mapping risk zones using multi-criteria decision analysis and weighted overlay analysis when historical data are scarce or not available for mapping risk zones. The obtained risk maps can be used for deciding watchtower locations, installation of sensors, cameras, etc. In every forest division, it is recommended to prepare a standard questionnaire form and document their experiences on forest fire in the region under their supervision before they are getting transferred to another location.

Image Fusion of SAR and Optical Images for Identifying Antarctic Ice Features Abstract Remote sensing data plays an important role in extracting thematic information from various sensors having different spectral, spatial and temporal resolutions. The present study aims at fusion of Radar Imaging Satellite-1 Fine Resolution Stripmap-1 and ResourceSAT-2 Linear Imaging Self Scanning Scanner-4 (LISS-4) images over Indian Antarctic Research Station Maitri and its surroundings to generate a better product which contains the characteristics of both the spectral information from LISS-4 and the spatial details of SAR. Different pixel-based fusion techniques such as Brovey Transform, Principal Component Analysis (PCA), Intensity Hue Saturation and Wavelet Principal Component Analysis (W-PCA) have been used in the present study. These image fusion techniques have been applied for the whole scene as well as for individual surface features like melt ponds, crevasses, freshwater lake, blue ice, oasis and lake ice for better discrimination of features. Quality assessment is performed by evaluating the performance of these algorithms using visual, spatial (High Pass Correlation Coefficient and Entropy) and spectral (Root Mean Square Error, Correlation Coefficient, ERGAS and Universal Quality Index) parameters. It is found that the identification of certain features such as crevasses, blue ice, melt ponds and lake ice has been improved with fused images compared to the original multi-spectral and SAR images. PCA and W-PCA fusion techniques offer better performance as compared to the rest of the techniques.

Spatial–Spectral Jointed Stacked Auto-Encoder-Based Deep Learning for Oil Slick Extraction from Hyperspectral Images Abstract Hyperspectral remote sensing provides an outstanding tool in oil slick detection and classification, for its advantages in abundant spectral information. Many classification methods have been proposed and tested for oil spill extraction using hyperspectral images. However, the deep learning method was hardly researched to classify oil slicks using hyperspectral images. In this work, we proposed a spatial–spectral jointed stacked auto-encoder (SSAE) to extract and classify oil slicks on the sea surface. The traditional machine learning methods, support vector machine, back-propagation neural network and stacked auto-encoder were also adopted. The experimental results reveal that our proposed SSAE model can remarkably outperform the other models, especially for the thick oil films. The results of this work could provide an alternative method to extract oil slicks on hyperspectral remote sensing images.

Estimation of Structural Diversity in Urban Forests Based on Spectral and Textural Properties Derived from Digital Aerial Images Abstract Urban forests generally have a heterogeneous structure consisting of small vegetation patches. High spatial resolution digital aerial images are still a primary data source for urban forest inventories. In the present study, the estimation possibilities of the structural diversity of urban forests were evaluated using image properties extracted from digital aerial images. Firstly, relationships between structural diversity indices and image properties were determined using the correlation analysis. It was found out that structural diversity indices were significantly correlated with spectral and textural properties. The strongest relationship was calculated between the normalized difference vegetation index and species-based Shannon–Wiener diversity index\(\left( {H_{\text{s}}^{\prime } } \right)\) (r = 0.599, p < 0.01). The relationship between textural properties and structural diversity indices was slightly lower compared to spectral properties. The strongest relationship between textural properties and structural diversity indices was calculated between the Entropy values derived from DVI and \(H_{\text{s}}^{\prime }\) (r = 0.478, p < 0.01). Afterward, each used diversity index was modeled as a function of the textural and spectral properties of digital aerial images. Univariate and multivariate linear regression models were used for this purpose. While the adjusted coefficient of determination \(\left( {R_{\text{adj}}^{2} } \right)\) of univariate regression models varies between 0.07 and 0.37, the \(R_{\text{adj}}^{2}\) values of a multivariate model vary between 0.13 and 0.57. Among the developed models, only the estimation models of tree size diversity \(\left( {H_{\text{h}}^{\prime } } \right)\) and tree species diversity \(\left( {H_{\text{s}}^{\prime } } \right)\) provided an estimation accuracy that could be used in practice.

Bank Line Migration and Its Impact on Land Use and Land Cover Change: A Case Study in Jangipur Subdivision of Murshidabad District, West Bengal Abstract This study is to evaluate the impact of bank lines shifting on land use and land cover changes. It is also to understand the coherence between changes in land use and land cover and annual rate of river bank failure. To fulfill the purpose of the present study, 21 river bank erosion-affected mouzas along the banks of river Ganga-Bhagirathi in Jangipur subdivision of Murshidabad district, West Bengal, have been selected. The whole work has been done with the help of RS and GIS techniques. Field visit has been arranged to verify the ground truth basically. The result of the study shows that an area with active bank erosion problem is characterized by remarkable land use land cover changes. In the study area, total erosion is 7.93 square kilometer (km2) where the total deposition is 3.244 km2 along the river banks from the year of 1980 to 2010. It can be said that bank failure directly affects the land use and land cover of the area under the river channel. During 1980–1990, negative and significant correlation is observed between the annual rate of bank erosion and change in area under sparse vegetation (r = − 0.810). Again, positive and significant correlation is observed between the annual rate of bank erosion and change in area of water body (r = 0.60). Same scenario is also observed in the respective years of 1990–2000 and 2000–2010. Ganga-Bhagirathi bank erosion has an adverse impact on the dwellers of the flood plain.

Analysis of Parabolic Dune Morphometry and Its Migration in Thar Desert Area, India, using High-Resolution Satellite Data and Temporal DEM Abstract Thar Desert region is mainly comprised of sand dunes and sand sheets which are distributed in more than 80 per cent geographical area of western Rajasthan. Among the different types of sand dunes, nearly 50 per cent area of western Rajasthan is covered by parabolic dunes. However, morphodynamics of parabolic dunes in the Thar Desert is not fully understood. For this purpose, high-resolution satellite data of IRS RS2 LISS-IV (5.8 m resolution) and Cartosat-1 DEM (10 m resolution) have been used for delineation of parabolic dunes with the derivation of morphometric parameters (dune length, dune area, volume of sand accumulation and dune height) in western Rajasthan. Scatter plots of these morphometric parameters show a linear relationship with correlation coefficient (0.59–0.80). Interpolated maps show an increased rate of wind speed during 1980–2014. Although several measures have been taken for sand dune stabilisation, differential sand migration has been estimated for selected parabolic dunes using temporal DEMs (SRTM DEM of 2000 and Carto DEM of 2009). The analysis shows average dune shifting of 7.11 m/year in Barmer, 5.15 m/year in Jaisalmer and 3.51 m/year in Bikaner districts of western Rajasthan in the Thar Desert area.

Knowledge-Based Identification and Damage Detection of Bridges Spanning Water via High-Spatial-Resolution Optical Remotely Sensed Imagery Abstract Bridges over water are important artificial objects that can be damaged by natural disasters. Accurate identification and damage detection of such bridges through the use of high-spatial-resolution optical remotely sensed imagery are important in emergency rescue and lifeline safety assessment. In this study, we detail a knowledge-based method of identification and damage detection of bridges spanning water using high-spatial-resolution optical remotely sensed imagery. Data on the body of water are extracted to define spatial extent and improve the timeliness of identification and damage detection, the threshold values of the rectangle degree and area are set to remove false bridge targets, and the damaged parts are detected according to the bridge’s rectangular characteristics and the relationship with the body of water. First, the characteristics, such as spectral, geometric, and textural, and spatial relationships of the bridge over water, are analyzed. Second, to limit the spatial extent of bridge identification and improve computational efficiency, data on the body of water are extracted. Third, the post-event bridge is identified from the viewpoint of bridge integrity based on shape and area parameters. Damage detection is then performed according to the bridge’s integrity. Finally, the results are evaluated for both non-positional and positional accuracy. Results of experiments carried out in Huiyang and Wenchuan, China, show that the proposed method, using high-spatial-resolution optical remotely sensed imagery, is effective for identification and damage detection of fallen and collapsed bridges spanning water. Therefore, the method is useful in updating the geographic database of bridges and assessing damage to them caused by natural disasters.

A WebGIS-Based Study for Managing Mangroves of Godavari Wetland, Andhra Pradesh, India Abstract The mangroves of the Godavari are well known as the first largest mangrove wetland in the state of Andhra Pradesh and the second largest in the Eastern Coast of India. Mangroves of Godavari provide multiple benefits to local communities, but they were threatening this wetland since 1980. The objective of this work is to map and monitor the mangroves and analyze the changes over the period of 74 years and incorporate geospatial data of the study area into a WebGIS platform. This WebGIS is an exceptional and cost-effective solution that would lead a valuable decision-making process for the public, significant versatile asset which would bring the mangroves to improve operations, increase collaboration and promote tourism, research and development through the internet. Topographic maps and satellite images have been used to map mangroves wetland and developed WebGIS by using open source and incorporated geospatial information of mangroves into it. As it acts as a standards-based platform for digital analysis, data management and mapping and monitoring mangroves, resulting stakeholders to access everywhere via web browser at any time. This is an innovative technique and is attempted here for the first time to manage mangroves; it can store a massive volume of geospatial data on the server, which reduces time, manpower and expense and control duplication of data. Apart from this, it has also allowed users for visualizing, editing, manipulating, interacting and disseminating geospatial data. The result revealed that 12 geospatial data from 1938 to 2012 were made available that showed changes in mangroves extent and distribution, and also a gradual increase in mangrove covers has been noticed from 2004 onwards. It may be concluded that a WebGIS-based study on mangrove wetland is useful to manage mangroves by mangrove management authorities and to regulate the mangrove development activities and formulating new policies for the better planning and management in order to guide for the sustainable mangrove of forests.

Performance Evaluation of Three Satellites-Based Precipitation Data Sets Over Iran Abstract The present study aims to evaluate the performance of daily and monthly precipitation data relative to GPM-IMERG, TRMM_3B42 and PERSIANN satellite-based precipitation estimations against historical data for the period 2014–2017 as observed at 70 synoptic stations distributed over Iran. The coefficient of determination (R-squared), root mean square error and the Nash–Sutcliffe model efficiency coefficient were used to evaluate the performance of the used data sets against observed precipitation records at the considered stations. The statistics showed that the considered data sets are generally less successful in estimating daily precipitation at nationwide as the estimation errors were found high at almost all the studied stations. The errors of daily precipitation estimation of GPM-IMERG, TRMM_3B42 and PERSIANN-CDR data sets showed that although there is a considerable similarity between the estimated precipitation by the three data sets, especially between the TRMM_3B42 and GPM-IMERG, the accuracy of GPM-IMERG daily precipitation over Iran is higher than that of TRMM_3B42 and PERSIANN-CDR. The highest R2 value for GPM-IMERG, TRMM_3B42 and PERSIANN-CDR remotely sensed daily precipitation is equal to 0.6, 0.46, and 0.37, respectively. Similarly, on the monthly time scale, the GPM-IMERG, with an average R2 value of 0.83 over the country, performs better than the other two data sets. The TRMM_3B43 with mean nationwide R2 = 0.80 also showed comparative performance with GPM-IMERG, but the PERSIANN-CDR data set with an average R2 value of 0.4 over the stations is not as accurate as the GPM-IMERG and TRMM_3B43.

Dense DSM and DTM Point Cloud Generation Using CARTOSAT-2E Satellite Images for High-Resolution Applications Abstract The primary objective of this study is to provide a methodology to generate a dense point cloud of digital surface model (DSM) and digital terrain model (DTM) from 0.6 m GSD stereo images acquired by CARTOSAT-2E satellite of the Indian Space Research Organization. These products are required for many high-resolution applications such as mapping of watersheds and watercourses; river flood modeling; analysis of flood depth, landslide, forest structure, and individual trees; design of highway and canal alignment. The proposed method consists of several processes such as orienting the stereo images, DEM point cloud extraction using the semi-global matching, and DSM to DTM filtering. The stereo model is built by performing aero triangulation and block adjustment using the ground control points. The semi-global matching algorithm is used on the epipolar images to generate the DSM in the form of dense point cloud corresponding to one height point for each pixel. The planimetric and height accuracies are evaluated using orthoimages and DSM and found to be within 3-pixel (~ 2 m) range. A method for extracting DTM by ground point filtering, to discriminate the probable ground points and the non-ground points, is provided by using discrete cosine transformation interpolation. This robust method uses a weight function to differentiate the noise points from the ground points. The overall classification efficiency kappa (κ) value averages at 0.92 for ground point classification/DTM extraction. The results of benchmarking of the GPS-aided GEO augmented navigation GPS receiver by operating it over IGS station, in static mode for collecting the checkpoints, also are presented.