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This research paper investigates urban sprawl in Thiruvananthapuram Urban Agglomeration (UA) and attempts to delineate Urban Growth Boundary (UGB) for promoting urban sustenance. A 112% rise in the spatial expanse of Thiruvananthapuram UA from 256.22 km2 in 2001 to 542.57 km2 in 2011 might induce urban sprawl in the peripheral areas. The Landsat satellite imagery for the years 1987, 1997, 2007, and 2017 were extracted to examine the spatiotemporal urban growth pattern. Shannon’s entropy index was employed to detect urban sprawl in Thiruvananthapuram UA. The UGB delineation process involved future urban growth prediction using the MOLUSCE (Modules for Land Use Change Simulations) plug-in of QGIS software. ANN-MLP (Artificial Neural Network-Multi Layer Perceptron) and CA (Cellular Automata) model was preferred in MOLUSCE to predict future urban growth for the year 2027. Thereafter, hexagons of one square kilometer were used to demarcate the Contiguous built-up Growth Boundary (CGB), and later, sub-administrative units were selected to delineate UGB. The results revealed a rise in the built-up areas from 36.04 km2 in 1987 to 140.69 km2 in 2017. Shannon’s entropy index indicated the prevalence of urban sprawl in Thiruvananthapuram UA. The future growth prediction by 2027 exhibited a further rise in built-up areas to 173.31 km2. The total area within CGB is 213.58 km2, while UGB accounted for 355.59 km2, which included 16 sub-administrative units. This study exhibited a unique methodology to delineate the urban growth boundary, which optimizes the future land requirements in developing nations.

Shallow water bathymetry is highly significant to regional and national economic development. It is also fundamentally important to coastal benthic environments. Thus, numerous different types of algorithms have been explored to identify one that accurately determines shallow water depth. Among the algorithms that employ data from passive sensors, Ma Sheng’s algorithm, which uses the Pearson correlation coefficient (CC) and the similarity coefficient (SC), shows good performance. After analyzing the bathymetry retrieval theory and algorithms included in Ma Sheng’s model, we determined that $$[\mathrm{ln}\left(nSC\right)-\mathrm{ln}\left(nCC\right)]$$ / $$[\mathrm{ln}\left(nSC\right)+\mathrm{l}\mathrm{n}(nCC)]$$ has a good relationship with the field data. Based on that finding, we established a normalized algorithm for estimating shallow water depth from hyperspectral data. Finally, the Ma Sheng’s algorithm and our new, normalized, algorithm were compared through analysis of Hyperion satellite imagery against in situ, measured bathymetry over the coastal regions of Saipan and Landfall Islands. Bathymetry retrieved using our method showed a root-mean-square error of 1.937 for Saipan Island and 2.37 for Landfall Island, both of which are comparable to results from Ma Sheng’s method. Moreover, the validation results demonstrate that our method exhibits good performance and is an acceptable alternative algorithm for bathymetry retrieval.

Very high resolution (VHR) satellite imagery and image processing algorithms allow for the development of remote sensing applications including multi-temporal classification, tracking of specific targets, multimedia data integration, ecosystem processes analysis, and land cover/land use (LULC) mapping. Classification algorithms are the primary source to generate LCLU maps. Since texture information is essential to generate LULC maps from VHR images, the object-based classification methods should be used instead of pixel-based methods. Also, in urban mapping, it is vital to select the appropriate classifier according to the type of land covers. Recently, in addition to machine learning algorithms, deep learning methods have also been used to classify VHR images. In this study, we compare the accuracy of convolutional neural network (CNN) algorithm with some machine learning methods, for classification of Pleiades satellite image with 50 cm spatial resolution. The results showed CNN algorithm has the highest classification accuracy when the training samples are increased. However, the difference between the classification accuracy of the CNN and relevance vector machine (RVM) models is not that significant so that one could use a more straightforward method with less training data rather than a complicated one with large volumes of data.

The detection of strategically important geospatial objects in remote sensing images is one of the important tasks for interpreting satellite images. Identification and mapping of objects such as tank, airplane, roads, buildings, etc. from remotely sensed images has gained much research and exploration in both defense and civilian applications. Detection and identification of buildings become more challenging when analyzed in forest neighborhood environment. The presence of dense trees in the surroundings increases the complexity and the rates of both false positives and false negatives. In the current study, an integration of image pan-sharpening (Gram-Schmidt) and sub-pixel image classification (Linear Mixture Model) algorithm is used for identification and mapping of building objects in two high resolution remote sensing satellite imagery (IKONOS and Worldview-2). Building objects having large, medium and small geometric size are selected from both the satellite imagery in strategically challenged forest neighborhood. The capability of pan-sharpening and sub-pixel classification techniques are jointly used for detection of building objects. The accuracy of target detection has been evaluated qualitatively (visual interpretation) and quantitatively (statistical analysis). Results on selected datasets show that the proposed combination of pan-sharpening and sub-pixel classification offers higher correctness in identification of building than other contemporary techniques. Although, significant improvement is noticed in results obtained for both the satellite imagery. Irrespective of having lower spatial resolution, after pan-sharpening, IKONOS datasets are found to be slightly more accurate in building identification in comparison to Worldview-2 datasets.

The rapid growth and advancement in the application of remote sensing (RS) and geographic information systems (GIS) in several application domains helped many researchers to analyze a wide range of information from satellite imagery. Settlement information that includes building foot prints is a very essential parameter for different applications such as urban planning, environmental planning and disaster management. VGG-16 (Visual Geometry Group) conventional neural network (CNN) model is a popular model used to detect and classify the input images. In this study, a new approach was proposed by integration of VGG-16-CNN model with spectral and textural information of satellite images for identification of building foot prints. The model was trained and implemented to identify the building footprints using Worldview-3 high-resolution satellite image over part of Mumbai city of Maharashtra state, India. Classification accuracy in the proposed approach is observed to be nearly 94% as compared to 82% in case of single-shot detector (SSD) algorithm alone. Metric parameters such as F1 score of 0.957, intersection over union (IoU) of 94.86% and total error rate of 8.133% also indicated better performance of the proposed approach. Particularly, the approach is highly beneficial for urban development authorities as they need to monitor the large number of vacant lands spread across urban areas.

Mapping the salt-affected soils (SAS) through spectral indices derived from satellite imagery is essential for its monitoring at a frequent interval and for the management. Several salinity indices have been developed based on the combination of spectral bands for predicting soil salinity in the arid environment. Present study attempted to identify and map the salt-affected soils (SAS) in inland plains of semi-arid agro-ecological region of Tamil Nadu, India using satellite image (Resourcesat-1(IRS-P6) LISS IV). Vertisols and Inceptisols are the major soil orders cultivated under paddy, sugarcane and pulses. An integrated approach of spectral bands and indices along with analytical data of samples collected from the field was used to develop multiple regression equations for prediction. The best linear regression model was selected based on adjusted R2 and MSE values for the prediction of pH, EC, and ESP. The variation observed between salt-affected soils predicted by multiple regression equations and measured data were 15, 10, and 5 percent for pH, EC, and ESP, respectively, due to weak variation in soil properties, sample density, better vegetation cover by management practices.

Gully erosion and its complex and variable characteristics have led to challenging and many problems for creation of erosion maps with the most commonly used methods of satellite imagery analysis. The fuzzy algorithm-based object-based image analysis (OBIA) method is one of the most effective methods for classifying satellite imagery, which aims to integrate the spectral satellite imagery and provides the necessary facilities for the use of environmental and spatial information as well as a geometric feature of land surface phenomena. The purpose of this study was to apply and evaluate the implementation of different classification algorithms and fuzzy functions membership detecting and mapping of gully erosion. For this purpose, satellite images of Sentinel-2 were employed and accordingly the soil erosion map of Lighvan watershed was developed within the semi-automated approach by applying fuzzy-OBIA features and techniques. The obtained results from the accuracy assessment of the methods indicated that the accuracy of gully maps obtained by fuzzy-OBIA features including: length/width, asymmetry, density, and shape index algorithms with the respective kappa coefficient of 0.78, 0.91, 0.85, and 0.89 is higher than other algorithms. Also, the results obtained from the study of the degree of membership of fuzzy functions indicate the high role of this factor in the accuracy of the results, so that the highest classification accuracy (kappa coefficient greater than 0.91) was related to the length/width algorithm, in which the fuzzy functions B, D, H and L with the highest degree of membership (0.685, 1, 0.972 and 1, respectively) were employed. Results of current research are great of important for decision makers and authorities by means of providing detailed information regarding the soil erosion in the study area as well as GIS sciences society for applying and introducing the most efficient methods and techniques.

An accurate detection of agricultural fields is often needed for agricultural-related applications, such as subsidies monitoring, field-based crop yield estimation and agricultural statistics extraction. High-resolution space images have become the fundamental source to extract agricultural field boundaries. Manual boundary delineation is not practical. In this study, we present an approach to detect agricultural fields from satellite images on the basis of agricultural field blocks. An agricultural field block consists of one or more fields that are owned by the farmers. The approach combines the Otsu’s thresholding algorithm and marker-controlled watershed (MCW)-based segmentation. First, the well-separated field segments within a field block being considered are detected through recursive processing of the Otsu’s thresholding algorithm. Then, these distinct field segments are used to generate a marker image, and further extraction of individual fields is carried out through a marker-controlled watershed (MCW)-based segmentation. The approach was tested using 10-m resolution Satellite Pour l’Observation de la Terre (SPOT)-5 multi-spectral (XS) image, 4-m resolution IKONOS XS image, 2.40-m resolution QuickBird XS image, and 0.60-m resolution QuickBird pan-sharpened (PS) image. The results were evaluated using the reference field boundary dataset. The achieved overall accuracies were 89.7, 83.2, 81.0, and 77.4% for the IKONOS XS, QuickBird XS, SPOT-5 XS, and QuickBird PS images, respectively. The results are promising and indicate that the approach can be used for the extraction of agricultural fields from space imagery.

Crop acreage estimation is an essential component for forecasting crop production. Menthol mint acreage estimation is a necessity as the crop production data change every year due to fluctuations in the market prices of menthol mint oil; hence, the rate available to farmers also changes every year. These acreage estimation studies would be helpful in reducing the uncertainties of menthol mint production as lower price results in low production and high price results in higher production next year. Thus, it in turn would help in stabilizing the market prices. Nowadays, remote sensing technologies due to their availability and adaptability are being widely used for crop acreage estimation nationally and internationally. This study focuses on menthol mint crop acreage estimation in the Barabanki district of Uttar Pradesh, India, using 2017 Sentinel-2A satellite data. Adaptive maximum likelihood classification algorithm was applied after intensive ground survey to obtain reliable menthol mint crop acreage estimation for talukwise statistics. Results have shown that menthol mint was extensively cultivated in the Fatehpur and Barabanki taluks as compared to the Haidergarh and Ram Sanehi Ghat taluks of Barabanki district. Menthol mint crop acreage estimation in the study area was estimated to be about 58,284.70 ha with (89.13% and 87.23%; users and producer’s accuracy) with overall accuracy (90.67%) and kappa value (0.844). In this study, early and late menthol mint crop acreage estimation was also attempted and it was found that about 26,123.50 ha and 29,911.40 ha were the area of early and late menthol mint, respectively. This method can be useful for localized-level crop acreage estimation from early to mature stage of menthol mint during its growing season.

Bengaluru has a unique place due to its pleasant weather and green cover. Off late, the city has witnessed phenomenal urban growth, especially during last few decades. This study was envisaged to assess the decadal changes in vegetation cover of Bengaluru using multispectral IRS satellite imagery of 2006 and 2019 within Bruhat Bengaluru Mahanagara Palike (BBMP) area. Satellite datasets were analysed to assess changes in vegetation cover during the last decade. Vegetation (tree cover) within BBMP area was estimated to be 26.1% (18,572.2 ha) in 2019 as compared to 30.1% (21,414.9 ha) in 2006, indicating an overall reduction of 4%. Overall classification accuracy was estimated to be 92.1% for 2006 and 95.9% for 2019. Based on the analysis, a vegetation change map highlighting positive change areas, negative change areas and no change areas within vegetation categories were prepared for 198 BBMP wards. A total of 730 GT sample points were also collected along with geo-tagged field photographs, which covered diverse niche areas with different vegetation components and density classes, as well as non-vegetated urban and other areas. Recommendations have been given to sustain and improve existing vegetation cover.

Unmanned aerial vehicle (UAV)-based multispectral remote sensing has shown a tremendous potential normalized difference vegetation index (NDVI) for precision agriculture. In this study, data captured from a UAV equipped with a Multispectral Mica Sense Red Edge camera used as ground-truth information to calibrate Sentinel-2 imagery. UAV-based NDVI allowed crop estimation at 10-cm pixel resolution by discriminating no-green vegetation pixels. The reflectance value and NDVI of the crops at different stages were derived from both UAV and Sentinel-2 images. The UAV Multispectral mapping method used in this study provided advanced information about the physical conditions of the study area (Roorkee) and improved land feature delineation. The result shows that UAV data produced more accurate reflectance values than Sentinel-2 imagery. However, the accuracy of the vegetation index is not wholly dependent on the accuracy of the reflectance. The UAV-derived NDVI has relatively low sensitivity to the vegetation coverage and insignificantly affected by environmental factors compared to NDVI derived from Sentinel-2 image.

Heritage sites are vulnerable to damage due to social, anthropological and environmental factors. Major Earthquakes are followed by damage to cultural heritage buildings. The assessment of such building damage is a critical problem. Earth Observation data, owing to its property of being non-contact, cost effective, synoptic view and high repeatability, has a significant role to play in estimation of damage in the earthquake affected areas. Currently, several and varied types of remote sensing data have become available, and therefore, appropriate methods for rapid assessment and analysis of the data need to be developed. Rapid damage assessment is critical to minimize loss in terms of life and property. In case of cultural monuments, rapid assessment can minimize damage and help in the conservation of monument. This research focuses on evolving a robust method for rapid identification and extraction of damaged heritage building structures, especially those affected by disasters such as earthquakes. In this study, we propose to examine the utility of advance texture algorithms such as Gabor, fractal and semi-variogram for rapid damage detection in heritage building structures. The methodology attempts to automatically highlight damaged portions of the structure through a knowledge driven rule set. The technique was able to extract the damaged area from the heritage building structure with the use of high-resolution space borne data. It is observed that feature extraction algorithms based on fractal and variogram provide better results than the Gabor based textures and are very useful in the case of high-resolution satellite imagery. Both the methods are able to extract damaged features in both shadowed and non-shadowed regions of the image. Hence the problems posed by shadowed dead grounds on EO data can be effectively resolved. However, it is also observed that the advance texture feature extraction algorithms are useful only in case of high-spatial resolution dataset and has limited use for rapid damage assessment from medium and low resolution datasets.

A large proportion of the world's population lives in coastal areas. These zones play a vital role in the socioeconomic aspects of coastal communities. The evolution of shoreline along the zones is of great importance to scientists, and engineers as well as coastal management. This research aims to assess and understand the dynamics of shoreline along the Red Sea coast between Al Lith and Ras Mahāsin, using medium-resolution satellite imagery over 34 years (1984–2018) as well as to predict futuristic changes in the shoreline position until 2038. The tasseled cap transformation tools in ArcGIS 10.2 are used to extract shoreline position for seventh intervals time. These shorelines are analyzed by the digital shoreline analysis system in four statistical functions, namely (EPR, LRR, NSM, and LMS). The endpoint rate is used to predict futuristic shoreline positions. The results reveal that the evolutions of shoreline in the form of erosion and accretion patterns, as surface area exceed 12.3 and 0.89 km2, respectively. The evolution rates were classified based on LRR into five classes: [i.e., − 11.84 to − 7.85 (very high erosion), − 7.85 to − 4.17 (moderate erosion), − 4.17 to 0.00 (low erosion), 0.00 to 1.28 (low accretion), and 1.280 to 14.44 (high accretion)]. These changes are attributed to the impact of the extreme wave action and the littoral drifts of sediments by longshore currents. The prediction model reveals that a large portion of the coastal zone is vulnerable to a high rate of shoreline disintegration.

Soil resource characterization of Kumaon hills of Himalayas was carried out through soil survey and established soil–physiography relationship after identification and delineation of six physiographic units in Khulgad watershed of Almora district, Uttarakhand. The satellite image characteristics identified through the visual/digital interpretation of IRS-1D LISS-III fused with PAN data on 1:12,500 scale were utilized in the present study. Based on the characteristics of soils developed on different physiographic units, the soils were evaluated for land capability classification and suitability for major crops in the region for sustainable development of the study area. GIS technique was used to develop decision trees employing various soil–site parameters for assessing crop suitability. The results revealed that deep to moderately deep soils with high clay content on the stable terraces and valley lands mostly appearing as dark blue to blackish blue in satellite imagery grouped under land capability classes II and III were interpreted as suitable for wheat, rice, finger millet and mustard crops. Moderately deep to deep soils occurring on moderately steep hill slopes and upper terraces were suitable for potato, mustard and finger millets. Soils on degraded hills with terraced tops having forest in patches (showed mix tone of grey and pink in imagery) categorized into class IV were found to be marginally suitable for wheat, mustard and potato with constraints of soil and topography, while soils on upper hill slopes, degraded hill tops/slopes with thin forest/grazing land and rocky ridges with dark to light blue, white and greyish mixed tone and dark blue with pale pink mixed tone in the imagery placed under land capability classes VI to VII were presently not suitable for crops cultivation and moderately suitable for grassland pasture or silvipasture purposes. The extent of suitability of soils for various crops has also been highlighted using GIS software and the maps were prepared. GIS is found to be helpful in processing spatial and non-spatial remote sensing data and conventional data of highly folded and faulted chain of Kumaon Himalayas to evaluate soil–site parameters for assessing crop suitability for optimum utilization of land.

The use of satellite imagery to monitor flood areas is essential to determine the damage and prevent related problems in the future. This paper examines thresholding and unsupervised classification for flood mapping using Sentinel-1 SAR image. Thresholding helps us to determine over-detection and under-detection regions in the flooded areas, and so, gamma distribution is used to select the thresholds. Also, the relevance vector machine (RVM) and the object-based classification method have been used for classification. The RVM algorithm obtained better results with overall accuracy = 0.89 and k = 0.95, while for the object-based classification method, these values were 0.87 and 0.91, respectively. According to the results, over- and under-detection occurred in flat areas and man-made structures, respectively. The results demonstrate a great potential of radar imagery for operational detection and delimitation of water in flood risk areas. The automation of satellite radar data processing operation has been tested, and it shows a potential for optimising the system of monitoring and early detection of flood risk.

Rooftops are essential features, extracted from satellite images for their significance in applications such as update of urban geodatabase, risk assessment and rescue map. In this work, a methodology (MBION-SVM) which integrates morphological, spectral, shape and geometrical features with SVM classifier to classify the objects within the satellite image into building rooftops and non-rooftops has been proposed. The probable buildings are detected using Morphological Building Index (MBI). The mislabeled rooftops are eliminated by combining Otsu thresholding and Normalized Difference Vegetation Index (NDVI). Geometrical features computed from identified building rooftops are used to train a support vector machine (SVM), and self-correction is performed for removing any mislabeled rooftops and to provide the data on surface area of the perfect rooftops. Here, we have used Very High Resolution (VHR) images of Worldview-2 and Sentinal-2. We have analyzed the performance of the proposed building extraction approach with classification algorithms such as linear discriminant analysis, logistic regression and SVM. Since the proposed method gives an accuracy around 99%, precision of 89%, a perfect recall of 1 and a F-score of 88%, it can be effectively utilized to extract the buildings from VHR images for any appropriate application.

The increasing population growth has caused a phenomenon called urban sprawl in most cities in the world. Urban sprawl has imposed adverse economic and environmental impacts on cities. Therefore, study, measurement and recognition of this phenomenon are essential. The purpose of this study is the identification and measurement of urban sprawl intensity in the Urmia city (capital of West Azerbaijan Province of Iran). It causes urban planners and managers to better plan for the future growth of the Urmia and achieve sustainable urban development with greater awareness and understanding. The present research is a theoretical–applied research purpose and is descriptive–analytic in terms of research nature. Several methods have been used for measuring urban sprawl. Nowadays, landscape metrics and spatial data are widely used worldwide because they detected urban sprawl with great detail and accuracy. Thus, in the present research, in order to measure urban sprawl in Urmia city, the black-and-white hypothesis and satellite imagery and integration of these data with landscape metrics have been used for the first time in Iran. In this regard, satellite images of 1991, 2001, 2011 and 2016 were collected with the help of Landsat satellite. These images were classified using an artificial neural network in two classes: built and non-built. Then the detection of changes was done using ENVI 5.3 software and also using landscape metrics (fractal dimension index, largest patch index, contiguity index, number of patches index and shape index) by Fragstats 4.2 software in order to measure urban sprawl in Urmia city. The results of this study indicate that urban sprawl in Urmia during the period of 1991–2011 had a decreasing rate and from then until now has been increasing and it will be continued.

This study assessed and mapped the aboveground tree carbon stock using very high-resolution satellite imagery (VHRS)—WorldView-2 in Barkot forest of Uttarakhand, India. The image was pan-sharpened to get the spectrally and spatially good-quality image. High-pass filter technique of pan-sharpening was found to be the best in this study. Object-based image analysis (OBIA) was carried out for image segmentation and classification. Multi-resolution image segmentation yielded 74% accuracy. The segmented image was classified into sal (Shorea robusta), teak (Tectona grandis) and shadow. The classification accuracy was found to be 83%. The relationship between crown projection area (CPA) and carbon was established in the field for both sal and teak trees. Using the relationship between CPA and carbon, the classified CPA map was converted to carbon stock of individual trees. Mean value of carbon stock per tree for sal was found to be 621 kg, whereas for teak it was 703 kg per tree. The study highlighted the utility of OBIA and VHRS imagery for mapping high-resolution carbon stock of forest.

Geospatial data play a key role in the preparation of mine plan, lease boundary fixing and volume calculation of minerals in the mine. The conventional practice proposed by Indian Bureau of Mine is using differential global positioning system (DGPS), CartoSAT/LISS IV satellite imagery and cadastral maps to provide lease boundary fixing, digital elevation model of mine and rectified cadastral map. Most of the mine has steep terrain which is difficult to survey using DGPS/total station, and it involves human risks. The temporal resolution of the satellite, cost and time required to procure the satellite imagery is high. The increasing affordability of civilian unmanned aerial vehicle (UAV), aerial data acquisition is easy in any topography, time-saving and less cost compared to traditional survey technique like total station and DGPS. The main aim of this paper is to incorporate the proposed workflow to generate the high accurate geospatial data for Indian Mines through the integration of UAV and traditional survey technique. Based on the proposed workflow, UAV-based survey conducted on a iron ore mine and the output geodata were generated. The study discussed UAV geodata-based analytical technique to identify the encroachment, slope unstable area of mine, water disposal route and volume of the stockpile. This helps to reduce the violations in terms of quantity and area, provide a better decision support system to the lease of the mine and increase the revenue of the government.

Forest canopy cover represents different characteristics of forest stands. However, especially in semiarid forest, the estimation of canopy cover by field measurements is too expensive. Therefore, it is necessary to develop appropriate techniques to estimate forest canopy cover for forest management in semiarid areas. In this research, a robust procedure to estimate canopy cover using stratification field sampling and AdaBoostM1 machine learning algorithm with Landsat 8 OLI imagery is suggested. Approximately 29,000 ha of semiarid forest (Manesht- and Ghelarang-protected area) in west of Iran was selected as the study area. The unsupervised classification was used on NDVI layer extracted from OLI data, and Neyman method was applied for allocation, in stratified areas. The crown cover was measured in percentage in each plot. In inaccessible plots, the optical satellite imagery of Worldview-2 from Google Earth database was used (0.46 m spatial resolution). For the classification of canopy cover, the AdaBoostM1 algorithm with random forest classifier was trained by 75% split original data, while 25% remaining data were used for accuracy assessment using ROC curve, true positive (TP), false positive (FP), overall accuracy (OA) and kappa coefficient (K). The results showed the overall accuracy and kappa coefficient of 91% and 0.88, respectively. Based on the results, the methodology developed in this study is suitable to estimate canopy cover in semiarid forests.

The study aims to investigate the urban change in the West Bank between the years 1997 to 2016, under complicated geopolitical conditions. High-resolution aerial photographs (1.25 m and 0.1 m) and moderate-resolution satellite imagery (30 m resolution Landsat data) were used in this study. The results of the study showed that the Palestinians suffer from serious limitations imposed by the Israeli occupation due to the urban expansion, especially in the so-called area C, which represents 61% of the West Bank. The Israeli occupation controls this area. On the other hand, the Israeli occupation provides an opportunity to the Israeli settlers to build more houses and buildings for various purposes. The study also shows that most of the Palestinian urban expansion comes at the expense of agricultural lands. The study recommends that the international community has to place more pressure on Israel to give the Palestinians their freedom to plan for their future, and use their lands freely.

The aim of current study is the identification of hydrothermal alteration by remote sensing methods in Tirka area, NE Iran. The field of study with area about 13 km2 (scale: 1:20,000) is part of the Toroud–Chahshirin magmatic belt, which is located in central Iran structural zone. Hydrothermal alteration zones are interpreted based on Advanced Spaceborne Thermal Emission and Reflection Radiometer remote sensing data. In the first stage, atmospheric, topographic and geometric corrections were performed on satellite imagery. Then, different image interpretation techniques consisting of band ratio, principal component analysis, minimum noise fraction, least squares fitting (LS-Fit), spectral angle mapper and spectral feature fitting (SFF) methods were used. Therefore, VNIR bands for iron oxide alteration, SWIR bands for argillic, phyllic and propylitic alterations, and TIR bands for silicification were applied. The results showed iron oxide and propylitic alterations occurred at the SW and the argillic and phyllic alteration zones were existed in the northern and SE parts of the area. Silicification happened sporadically in the region. Lineaments were processed by false color composite, high-pass filters and hill-shade digital elevation model methods, and two NW–SE and NE–SW major trends were revealed. Field observation and laboratory analysis were used for accuracy of satellite studies. Finally, alteration and lineament maps were integrated for the recognition of high-potential mineralization. Petrographic studies showed that SFF and LS-Fit methods are more reliable than other techniques for the determination of hydrothermal alterations.

Earth is a limited and non-renewable natural resource that is directly affected by the population growth pressures. In order to make the optimal use of land, it is necessary to be aware of bad land use/land cover (LULC) changes and the ways in which human beings make use of the land, which is possible by detecting LULC change. In this study, remote sensing (RS)/geographic information systems (GIS) were used, and Landsat 5 and Landsat 8 images from the years 1986, 2000, and 2016 were analyzed for changes. The aim was using multilayer perceptron (MLP) neural network and systematic points statistical analysis (SPSA) for predicting the trend of LULC changes in RS/GIS. The satellite images of three different years were classified into five classes. Variables such as proximity to the road network were considered as effective parameters in growth and development. The SPSA with scattering point trends and points kernel shape also showed the effect of changes on each factor and urban zone. According to the results, during the 30 years, 10.6% of agricultural lands were destroyed and urban areas increased by 23.4%. Agricultural lands and open lands have changed more than other LULCs and have become urban areas with the highest rates of change in the southern parts of the river on the southern and northern margin of the city. These results were shown some layers had more effective on changes, and some region according to desirable for urban developments had more changes that should be considered in urban planning.

Water scarcity is a major problem for villagers’ survival as it determines the population density and affects the migration pattern in Bara region. In the present study, groundwater resource endowment is studied and correlated with the distributional pattern of population, settlements and economic activity for analysing regional development in the study area. The study has been carried out using geospatial platforms, i.e. Erdas Imagine 2014 and ArcGis 10.2.2 software. Sentinel-2 satellite imagery and Cartosat-1 DEM data were the major data sources for extracting factor layers. Geomorphology and lineament maps of NRSC, District Resource Map of GSI, topographic maps and Google Earth images along with field surveys were ancillary database. Saaty’s 9-point rating scale of analytical hierarchy process was used to extract the GWPI by integrating factor layers of geomorphology, lineament density, slope, geology, rainfall, drainage density and land use land cover according to their relative influence. Final map shows different zones of groundwater prospects in the study region, which is validated from aquifer thickness data. Result shows that 39.19% (291.41 km2) of the total area (743.64 km2) is classified as high-to-excellent GWP, whereas 27.96% (207.89 km2) of the area is under very poor-to-poor GWP. Areas having poor-to-poorest groundwater storage impact on population distribution, as 14% of the total population is lying over these zones. The descriptive statistical analysis showed that CGWPI and built-up area are significantly correlated (F = 18.024 > 4.41*, t = − 4.245 > 2.101, R2 = 50.03%). CGWPI is also correlated significantly with population density (F = 18.855 > 4.41*, t = -4.342 > 2.101, R2 = 50.16%). However, the relationship is not very high on linear regression model as expected since only 50% variations in population distribution can be attributed to CGWPI, for example, the Shankargarh town having the highest population density and the second highest built-up percentage in the whole study area in spite of being endowed with the lowest groundwater potential.

The timely precise information of land use land cover (LULC) in the canal command area can help in managing irrigation water according to the crop water requirement. A study was conducted to map the LULC of irrigated command area of three distributaries in the lower Chenab canal system, Pakistan, namely Mungi, Killianwala and Khurrianwala for the estimation of canal water deficit (CWD). Multispectral images of LANDSAT-7 were used for Rabi season of 2009–2010 and 2010–2011. Normalized difference vegetation index-based unsupervised classification was performed for the formation of LULC of the commands area. During the initial classification, totally nine clusters were created with maximum likelihood. These clusters were then merged into final four classes on the basis of field knowledge. Accuracy assessment was performed using error matrix; producer and user accuracies were estimated for each class with overall accuracy of 84% and 86% for the Rabi season 2009–2010 and 2010–2011, respectively. For the assessment of the irrigation water demand, potential evapotranspiration was estimated using the Penman–Monteith equation. Crop water requirement was estimated based on the 10-day Kc value of the mapped crop from the LULC. Irrigation water demand for cropped area was estimated from the LULC and crop evapotranspiration. Canal water deficit was estimated from the available canal water supply and irrigation water requirement in the Rabi season. During the Rabi seasons, average CWD was 64%, 72% and 32% (2009–2010) and 33%, 46% and 36% (2010–2011) for Khurrianwala, Killianwala and Mungi distributary, respectively.

This paper presents the application of NDVI technique for extracting net sown cropped area of major crops cultivated during the winter season (October–March) and calculation of crop diversity index at Nyaya Panchayat level in Bara Tehsil of Allahabad district. Time series NDVI imagery is generated from the processing of Sentinel-2A MSI L1C product. NDVI composite index that contains intersection of time series NDVI threshold based on crop ground truth data is used to extract pure cropped area. Further, the formula given by Gibbs and Martin (Am Sociol Rev 27, 1962) is used to calculate crop diversity index, which uses only net sown area of crops for measuring diversification of crops at the regional level. Derived net sown area from Sentinel-L2A imagery is simulated with reported data of net sown area compiled by the government at the block level. Further, the net sown area is mapped at village level and result analyzes at Nyaya Panchayat level. The result shows that proportion of cultivated area under each crop is changed at Nyaya Panchayat level with change in topography, soil types, irrigation facilities, and distance from major crop market and smooth road infrastructure under the same geo-climatic and socio-economic conditions.

Narcotic plants contain substances that cause unusual excitation and subsequent depression of the central nervous system. Many narcotic plants contain substances that have medicinal properties and are used primarily as pain relievers. Opium poppy (Papaver somniferum) is one of the most cultivated narcotic plants. The planning of Opium poppy growing is controlled by United Nations and Drugs and Medicines Control Program. This study was conducted to establish a basis for determining the traceability of poppy cultivating areas using remote sensing in the large plain. A very high-resolution QuickBird-2 satellite image was used in the study. The software of ERDAS Imagine and eCognition Developer was performed for image processing and classification. A variety of classification methods were performed on the satellite image. ArcGIS software was used for accuracy assessment, ground control, and map production of the poppy cultivating area. The accuracy of classification methods was compared. The producer–user accuracy was estimated as 97.99% using spectral difference sub-segmentation process of multiresolution segmentation algorithm in object-based classifications method. This algorithm can be a practical approach for determining of opium poppy parcels in the large plain.

The backscattering coefficient (bb) has been obtained either in situ observations/measurements or semi-analytical/analytical and empirical algorithms that depend on the relationship between ‘bb’ and the remote sensing reflectance (Rrs). Several models were developed to estimate backscattering coefficient from the satellite imagery. The present study aims to assess the accuracy of bio-optical models and infer the best suitable model for Ocean Colour Monitoring (OCM-2) and for the upcoming OCM-3 sensors of Indian Space Research Organization. For this purpose, the bio-optical algorithms/models such as quasi-analytical algorithm (QAA), QAA version 5, Generalized Inherent Optical Properties (GIOP) and optimization of semi-analytical models in case 1 waters of the Bay of Bengal (BoB) were considered. For this analysis, three satellite sensors Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua, Visible Infrared Imaging Radiometer Suite (VIIRS), and OCM-2 datasets has been used to assess the model’s accuracy. The results showed that the optimization technique performs better (bias = 0.002, 0.179 and root-mean-square error (RMSE) = 8%, 20%) for MODIS-A and OCM-2, respectively, whereas GIOP performs better (bias = 0.08, RMSE = 14%) for VIIRS in case 1 waters of the BoB. From the statistical analysis of each model for all stations, it is recommended to use the optimization technique rather than GIOP technique for estimation of satellite based ‘bb’ in Indian waters.

Agricultural field maps are significant sources of data to achieve precision farming. The present research is a step toward generating land use/land cover maps automatically. The primary goal of this research was to develop an area-based model from nonparametric active contour models for agriculture land boundary extraction from IRS P5 satellite images. After investigating two well-known models created from nonparametric active contours, named local binary fitting and multi-phase, the local binary fitting model was selected to develop and enhance. Land boundary detection was improved by adding two texture layers to the input images and the development of the external energy function. The local binary fitting model was advanced as a multi-phase model in order to identify several regions in an image. Also, dull image boundaries were better extracted by changing the sigma parameter and regularization term. Evaluation of the proposed method yielded to the overall accuracy, user accuracy, producer accuracy, and kappa coefficient of 89.53%, 65.93%, 86.13%, and 86.52%, respectively.

By using satellite imagery, the recognition and evaluation of various phenomena and extraction of information necessary for the planning of land resources or other purposes are easily accomplished. The purpose of this study is to compare the efficiency of seven commonly used methods of monitored classification of satellite data to evaluate land use changes using TM and OLI Landsat, IRS, Spot5 and Quick Bird bands as well as different color combinations of these images to detect agricultural land, residential areas and aquatic areas using object-oriented processing. Digital processing of satellite images was carried out in 1998 and 2016 using advanced methods. Training samples were extracted in five user classes by eCognition software using segmentation scale optimization, different color combinations and coefficients of shape and compression. The appropriate segmentation scale for arable land, human complications and the blue areas were, respectively, 50, 8 and 10. Then each image was classified separately using seven methods and extracted samples, and efficiency of each classification method was obtained by calculating two general health and Kappa coefficients. The results show that the accuracy of each classification method and the neural network with a total accuracy of 94.475 and Kappa coefficient of 92.095 were selected as the most accurate classification method. These results show that the sampling of educational samples with proper precision of the classes in the images and dependency probability of each satellite images pixel can be useful in classifying group available in helpful area.

In recent years, the significant increase in research on spatial information is observed. Classification or clustering is one of the well-known methods in spatial data analysis. Traditionally, classifiers are generally based on per-pixel approaches and are not utilizing the spatial information within pixel, called mixels which is an important source of information to image classification. There are two foremost reasons behind the existence of mixels: (a) coarse or low spatial resolution of sensor and (b) topographic effects that recorded on optical satellite imagery due to differential terrain illuminations over rugged areas such as Himalayas. In the present study, different classification algorithms have been implemented to drive the impact of topography on them. Among various available, three algorithms for the mapping of snow cover region over north Indian Himalayas (India) are compared: (a) maximum likelihood classification (MLC) as supervised classifier; (b) k-mean clustering as unsupervised classifier; and (c) linear spectral mixing model (LSMM) as soft classifier. These algorithms have been implemented on AWiFS multispectral data, and analysis was carried out. The classification accuracy is estimated by the error matrices, and LSMM achieved higher accuracy (84.5–88.5%) as compared to MLC (81–84%) and k-mean (74–81%). The results highlight that topographically derived classifiers achieved better accuracy in mapping as compared to simple classifiers. The study has many applications in snow hydrology, glaciology and climatology of mountain topography.

The practice of shifting cultivation has been increased due to an increase in population and is affecting the global issues, such as land use/land cover, biodiversity loss and environmental degradation, etc. In this study, the area of shifting cultivation lands in 1996, 2001, 2006, 2011, and 2016 were analyzed in West Karbi Anglong district of Assam by using time series imagery of Landsat Thematic Mapper, Enhanced Thematic Mapper Plus, and Operational Land Imagery with 30 m. The results showed that, from 1996 to 2016 the area under shifting cultivation has been increased from 149 to 215 km2, and was mainly distributed at elevations of 250–500 and 500–750 m and on slopes of

The devastating earthquake that struck Nepal in 25 April (Mw7.8) and 12 May (Mw7.3) 2015 have triggered numerous landslides and reactivated existing landslides extending over large areas. These two shocks mostly affected the northwest and northeast region of Kathmandu which accelerated large region along with southward up thrusting in the region. To detect the event of the landslides in time and space satellite images have been analysed using space remote sensing techniques like pseudo colour transformation technique and was employed to pre and post-earthquake images of the event to bring out only landslide affected areas from the image by masking the remaining part, where field based survey of the landslides provided information on real nature of it, slope and rock type. Surprisingly the observed mass movements are mostly belong to debris flow and rock fall types beginning from the top part of the ridge slope with south-westerly faces i.e. sun facing. Further, satellite images of different dates could be gathered which facilitated analysis on development of landslide with time. This study also revealed that the NE–SW trending geological faults have controlled the landslide occurrence especially in Ramche area and along the Bagmati river valley near Shital-chowk village on Kathmandu-Hetauda road.

Located on the southeast edge of the Qinghai-Tibetan plateau, Aba County is the core area of the Returning Grazing Land to Grassland Project (RGLGP) on the plateau. For the purpose of monitoring the grassland change before and after the grassland protection project in Aba County, Landsat images acquired in 1996, 2003 and 2009 were analyzed. Using Spectral Mixture Analysis (SMA) model, sub-pixel fractions of land cover components were obtained: bright vegetation (BV), dark vegetation (DV), bright soil (BS), dark soil (DS) and water. Fraction images present the distribution and proportions of typical land cover components in this study. Fractions BV and BS were chosen as two indicators for grassland degradation. Thereafter, Change Vector Analysis (CVA) model was applied on the two indicators. After the performance of the CVA model, change results which consisted of both grassland degradation and vegetation re-growth were obtained, showing the change patterns of grassland degradation and vegetation re-growing in Aba County between two gaps: from 1996 to 2003 (before the RGLGP) and from 2003 to 2009 (after the RGLGP). The change patterns of grassland degradation and vegetation re-growing can effectively assist in the development of environmental restoration measures and in the RGLGP plans for the Qinghai-Tibetan plateau.

Excessive runoff and high soil erosion rate are the critical problems in the Himalayan terrain, mainly due to rugged topography and high intensity rains. Accurate quantification of runoff and erosion is thus of paramount importance for taking appropriate measures to sustain the soil productivity in the Himalayan watersheds. Distributed, process-based hydrological and erosion models are ideal for this purpose. However, model parameterization in the rugged, inaccessible and thus generally a data scarce Himalayan watershed is a major challenge. The present study primarily investigates the applicability of kinematic runoff and erosion model (KINEROS2) model in a Himalayan watershed besides exploring the potential of satellite remote sensing and GIS in spatially distributed runoff modeling. The KINEROS2 model, is an event-based, distributed, water and erosion process model. It discretizes the watershed into a mosaic of planes and channels based on topography. The runoff is estimated for each plane which eventually flows to adjacent channel and is then routed to estimate the total runoff at the watershed outlet. Remote sensing is primarily used for model parameterization, i.e., characterizing the individual planes and channels. Optimized digital elevation model and fine-scale land-use/land-cover information are generated using high-resolution panchromatic and multi-spectral optical and microwave satellite imagery. The resulting data on near-surface soil moisture from radar imagery (ENVISAT ASAR) calibrated the initial soil moisture in the model, whose performance is evaluated using root mean square error and Nash–Sutcliffe that reveals that KINEROS2 model works quite well in a small Himalayan watershed. The sensitivity analysis indicates that saturated soil hydraulic conductivity is the most sensitive parameter influencing the runoff compared to Manning’s coefficient and initial soil moisture. The model output is also used for validating the remote sensing and geographical information system (GIS) based hydrologic response units delineated in a previous research study. The study highlights that the coupling of remote sensing and GIS with process models, such as KINEROS2, can provide valuable information in planning sustainable watershed management practices in the Himalayan watersheds.

The urban forest plays an important role in mitigating the urban heat island. However, the cooling effects of different types of urban forest are unclear. In addition, the fairness of the cooling effect of the urban forest has not been discussed. In this study, the land surface temperature (LST) of Changchun City, China was obtained from Landsat ETM+ data and then correlated with detailed urban forest information derived from the high-spatial-resolution Google Maps in order to determine the cooling intensity and cooling distance of different types of urban forest. In addition, the Gini coefficient was used to evaluate the equity of cooling services provided by the urban forest. The results indicated that (1) the total area of urban forest in Changchun City is 106.69 km2 and is composed of attached forest (AF, 45.83 km2), road forest (RF, 23.87 km2), ecological public welfare forest (EF, 23.24 km2) and landscape forest (LF, 13.75 km2); (2) the cooling effect of different types of urban forest varies. The cooling intensity and cooling distance are 3.2 °C and 125 m (LF), 0.2 °C and 150 m (EF) and 0.6 °C and 5 m (AF), and RF had no cooling effect; and (3) the cooling effect of urban forest benefits approximately 760,000 people in Changchun City, and the Gini coefficient of the cooling services of urban forest was 0.29, indicating that the cooling service was reasonable. Therefore, we demonstrated that ETM+ and Google data are a convenient and affordable approach to study the LST on an urban scale, and the Gini coefficient could be a meaningful indicator to evaluate urban ecological services.

With the advent of high spatial resolution satellite imagery, automatic and semiautomatic building extractions have turned into one of the outstanding research topics in the field of remote sensing and machine vision. To this date, various algorithms have been presented for extracting the buildings from satellite images. Such methods lend their bases to diverse criteria such as radiometric, geometric, edge detection, and shadow. In this paper, a novel object based approach has been proposed for automatic and robust detections as well as extraction of the building in high spatial resolution images. To fulfill this, we simultaneously made use of both stable and variable features. While the former can be derived from inherent characteristics of the buildings, the latter is extracted using a feature analysis tool. In addition, a novel perspective has been recommended to boost the automation degree of the segmentation part in the object based analysis of remote sensing imagery. The proposed method was applied to a QuickBird imagery of an urban area in Isfahan city and the results of the quantitative evaluation demonstrated that the proposed method could yield promising results. Moreover, in another section of this study, for assessing the algorithm transferability, the rule set was implemented to a part of the WorldView image of Yazd city, proving that the proposed approach is capable of transferability in different types of case studies.

We evaluated the relationships among three Landsat Enhanced Thematic Mapper (ETM+) datasets, top-of-atmosphere (TOA) reflectance, surface reflectance climate data records (surface reflectance-CDR) and atmospherically corrected images using Fast Line-of-Sight atmospheric analysis of Spectral Hypercubes model (surface reflectance-FLAASH) and their linkto pecan foliar chlorophyll content(chl-cont). Foliar chlorophyll content as determined with a SPAD meter, and remotely-sensed data were collected from two mature pecan orchards (one grown in a sandy loam and the other in clay loam soil) during the experimental period. Enhanced vegetation index derived from remotely sensed data was correlated to chl-cont. At both orchards, TOA reflectance was significantly lower than surface reflectance within the 550–2400 nm wavelength range. Reflectance from atmospherically corrected images (surface reflectance-CDR and surface reflectance-FLAASH) was similar in the shortwave infrared (SWIR: 1550–1750 and 2080–2350 nm) and statistically different in the visible (350–700 nm). Enhanced vegetation index derived from surface reflectance-CDR and surface reflectance-FLAASH had higher correlation with chl-cont than TOA. Accordingly, surface reflectance is an essential prerequisite for using Landsat ETM+ data and TOA reflectance could lead to miss-/or underestimate chl-cont in pecan orchards. Interestingly, the correlation comparisons (Williams t test) between surface reflectance-CDR and chl-cont was statistically similar to the correlation between chl-cont and commercial atmospheric correction model. Overall, surface reflectance-CDR, which is freely available from the earth explorer portal, is a reliable atmospherically corrected Landsat ETM+ image source to study foliar chlorophyll content in pecan orchards.

Integration of satellite remote sensing data and GIS techniques is an applicable approach for landslide mapping and assessment in highly vegetated regions with a tropical climate. In recent years, there have been many severe flooding and landslide events with significant damage to livestock, agricultural crop, homes, and businesses in the Kelantan river basin, Peninsular Malaysia. In this investigation, Landsat-8 and phased array type L-band synthetic aperture radar-2 (PALSAR-2) datasets and analytical hierarchy process (AHP) approach were used to map landslide in Kelantan river basin, Peninsular Malaysia. Landslides were determined by tracking changes in vegetation pixel data using Landsat-8 images that acquired before and after flooding. The PALSAR-2 data were used for comprehensive analysis of major geological structures and detailed characterizations of lineaments in the state of Kelantan. AHP approach was used for landslide susceptibility mapping. Several factors such as slope, aspect, soil, lithology, normalized difference vegetation index, land cover, distance to drainage, precipitation, distance to fault, and distance to the road were extracted from remotely sensed data and fieldwork to apply AHP approach. The excessive rainfall during the flood episode is a paramount factor for numerous landslide occurrences at various magnitudes, therefore, rainfall analysis was carried out based on daily precipitation before and during flood episode in the Kelantan state. The main triggering factors for landslides are mainly due to the extreme precipitation rate during the flooding period, apart from the favorable environmental factors such as removal of vegetation within slope areas, and also landscape development near slopes. Two main outputs of this study were landslide inventory occurrences map during 2014 flooding episode and landslide susceptibility map for entire Kelantan state. Modeled/predicted landslides with a susceptible map generated prior and post-flood episode, confirmed that intense rainfall throughout Kelantan has contributed to produce numerous landslides with various sizes. It is concluded that precipitation is the most influential factor for landslide event. According to the landslide susceptibility map, 65% of the river basin of Kelantan is found to be under the category of low landslide susceptibility zone, while 35% class in a high-altitude segment of the south and south-western part of the Kelantan state located within high susceptibility zone. Further actions and caution need to be remarked by the local related authority of the Kelantan state in very high susceptibility zone to avoid further wealth and people loss in the future. Geo-hazard mitigation programs must be conducted in the landslide recurrence regions for reducing natural catastrophes leading to loss of financial investments and death in the Kelantan river basin. This investigation indicates that integration of Landsat-8 and PALSAR-2 remotely sensed data and GIS techniques is an applicable tool for Landslide mapping and assessment in tropical environments.

An Improved Floor Area Ratio (IFAR) which can describe the intensity of urban land development was proposed and estimated by means of remote sensing technique. The paper intent to conduct a quick and high-precision estimation of the index based on building shadows in high-resolution images. Firstly, the spectral characteristics of shadows were analyzed to establish a ratio band SR which would enhance shadow information. The object-oriented method was used to achieve the extraction of shaded areas. Secondly, GIS method was used to estimate the shadow length and building height. The inspection of the building height between the estimation result and the filed measured data showed an accuracy of 90%. Finally, the gross floor area of each building was calculated by multiplying the storeys with the basement area. Thiessen polygons were built by assuming the centroid of the basement area as a point. The IFAR of each building was the ratio of the gross floor area to the corresponding polygon. The IFAR have more abundant connotations by taking more information into consideration. The remote sensing tool can well extract the proposed index accurately on a very large scale.

Spatial contextual feature plays an important role in high resolution remote sensing image. A new approach based on local variance analysis is introduced to spatial contextual feature extraction for change detection in this paper. In the proposed approach, the change magnitude between the paired central pixels of a local area (e.g., a 3 × 3 sliding window) in multitemporal images depends on surrounding pixels in the difference image, and this change magnitude is quantitatively measured based on the standard deviation of the difference image within the local area. This difference image contextual property may be useful particularly when high-resolution images are used. Finally, the change magnitude image is classified as a binary CD map by using SVM. This proposed approach is applied to SPOT-5 multitemporal datasets and two QuickBird multitemporal images from two case studies, which are quantitatively compared and validated. Experimental results show that the proposed approach is feasible.

The rainfall intensity classification technique using spectral and textural features from MSG/SEVIRI (Meteosat Second Generation/Spinning Enhanced Visible and Infrared) satellite data is proposed in this paper. The study is carried out over north of Algeria. The developed method is based on the artificial neural multilayer perceptron network (MLP). Two MLP algorithms are used: the MLP-S based only on spectral parameters and the MLP-ST that use both spectral and textural features. The MLP model is created with three layers (input, hidden, and output) that consist of 6 output neurons in the output layer that represent the 6 rain intensities classes: very high, moderate to high, moderate, light to moderate, light and no rain and 10 spectral input neurons for the MLP-S and 15 input neurons for MLP-ST, which as ten spectral features that were calculated from MSG thermal infrared brilliance temperature and brilliance temperature difference and as five textural features, and The rainfall intensity areas classified by the proposed technique are validated against ground-based radar data. The rainfall rates used in the training set are derived from Setif radar measurements (Algeria). The results obtained after applying this method show that the introduction of textural parameters as additional information works in improving the classification of different rainfall intensities pixels in the MSG/SEVIRI imagery compared to the techniques based only on spectral information. These results are compared with results obtained with the probability of rainfall intensity (PRI). This comparison revealed a clear outperformance of the MLP algorithms over the PRI algorithms. Best results are provided by the MLP-ST algorithm. The combination of spectral and textural features in the MSG–SEVIRI imagery is important and for the classification of the rainfall intensities to different classes.

WOS: 000406359500003, This research aimed to explore the fusion of multispectral optical SPOT data with microwave L-band ALOS PALSAR and C-band RADARSAT-1 data for a detailed land use/cover mapping to find out the individual contributions of different wavelengths. Many fusion approaches have been implemented and analyzed for various applications using different remote sensing images. However, the fusion methods have conflict in the context of land use/cover (LULC) mapping using optical and synthetic aperture radar (SAR) images together. In this research two SAR images ALOS PALSAR and RADARSAT-1 were fused with SPOT data. Although, both SAR data were gathered in same polarization, and had same ground resolution, they differ in wavelengths. As different data fusion methods, intensity hue saturation (IHS), principal component analysis, discrete wavelet transformation, high pass frequency (HPF), and Ehlers, were performed and compared. For the quality analyses, visual interpretation was applied as a qualitative analysis, and spectral quality metrics of the fused images, such as correlation coefficient (CC) and universal image quality index (UIQI) were applied as a quantitative analysis. Furthermore, multispectral SPOT image and SAR fused images were classified with Maximum Likelihood Classification (MLC) method for the evaluation of their efficiencies. Ehlers gave the best score in the quality analysis and for the accuracy of LULC on LULC mapping of PALSAR and RADARSAT images. The results showed that the HPF method is in the second place with an increased thematic mapping accuracy. IHS had the worse results in all analyses. Overall, it is indicated that Ehlers method is a powerful technique to improve the LULC classification.