Showing papers in "Remote Sensing Applications: Society and Environment in 2021"

TL;DR: The post-classification change detection method using maximum likelihood classifier (MLC) supervised classification is applicable in all cases and is the most commonly used technique from the past till present that has achieved high accuracy in all regions comparatively to other techniques.

TL;DR: In this paper, the authors used cellular automata and ANN to estimate future land use/land cover (LULC) and seasonal (summer and winter) LST scenarios in one of the fastest-growing megacities and the business capital of Bangladesh, named as Chattogram.

TL;DR: In this article, the authors compared remote sensing ecological index (RSEI) and EI with the most popular pressure-state-response (PSR) method based on a set of remote sensing and statistical indexes through a weight system in Samara region Russia.

TL;DR: In this paper, the authors presented a comprehensive evaluation of the urban drainage system (UDS) of Gurugram City, India by utilizing the high-resolution remotely sensed datasets viz., IMERG (half-hourly precipitation data from 2000 to 2019), ALOS PALSAR (Digital Elevation Model) and Sentinel-2 (land use/land cover).

TL;DR: In this paper, the authors proposed an interactive decision-making approach under multi-criteria decision analysis for mapping the flood susceptible areas within the Dodoma region, i.e. elevation, slope, geology, drainage density, flow accumulation, land-use/cover, and soil.

TL;DR: In this paper, the authors developed yield prediction models based on a machine-learning approach using satellite-based time-series images and validated them using regression analysis and an artificial neural network (ANN) approach.

TL;DR: In this paper, the authors developed an alternative approach for mapping burned areas in the Cerrado biome in Brazil, using Landsat imagery and Deep Learning algorithm, implemented on the Google Earth Engine and on Google Cloud Storage platform.

TL;DR: A novel integration of support vector machine, Markov chain and cellular automata for urban change modelling that implies a substantial fit between the predicted and reference data, which proves the robustness of this method for modelling urban change.

TL;DR: In this paper, the authors examined the relationship between land use/land cover (LULC) and land surface temperatures (LST) using remote sensing data over three major urban agglomerations UAs.

TL;DR: In this paper, the fragmentation effect on ecosystem services value (ESV) is quantitatively explored and the impact of land use land cover (LULC) change on ecosystem service value is explored.

TL;DR: In this paper, the influence of land-specific carbon emissions (LCEs) on land surface temperature (LST) dynamics in urban and suburban areas of Khulna city during 1998-2018 was examined through linear regression in the GIS environment.

TL;DR: In this article, a study of water bodies in Sirmaur District, Uttarakhand, India with the special focus on Renuka wetland was carried using the online interactive cloud-based planetary processing open source platform, Google Earth Engine (GEE).

TL;DR: In this paper, the authors assess the quality of the PRODES Cerrado data, which is being generated according to the well-known and consolidated PRODESA Amazonia methodology.

TL;DR: In this article, a lineament derivation environment through the integration of edge detection and line-linking algorithms is presented, where the authors show that the used optical sensors are less efficient than DEMs having the same spatial resolution.

TL;DR: In this article, the authors have attempted a study to model and predict the plausible future urban growth using a Cellular-Automata and Markov Chain Model (CA-MCM) and also used three map validation procedure.

TL;DR: In this article, a methodology to apply an automatic land use and land cover classification on the Sao Francisco Verdadeiro River hydrographic basin, western region of Parana state, with Landsat-8 images in the Google Earth Engine geospatial processing platform is presented.

TL;DR: In this paper, the authors used a transdisciplinary approach, combining Google Earth Engine's processing power, freely available Sentinel imagery (fusion of Sentinel-1 and -2), expert engagement (including researchers, managers and decision makers), drone technology and field trips, to provide an accurate and up-to-date understanding of the occurrence and density of invasive alien trees in an important water tower for the southwestern Cape of South Africa at a 20m resolution.

TL;DR: In this paper, the potential of Sentinel-2 imageries in spatio-temporal assessment of Rice residue burnt areas and generated pollutants in Kurukshetra, districts of Haryana, India.

TL;DR: In this article, the spatial pattern, dynamics and goodness of urban growth in Mangaluru city were studied in the context of its implications for future sustainable development using the techniques of remote sensing, GIS and statistical models.

TL;DR: In this article, the authors developed a new damaged area assessment (DAA) method to measure the area of each damage type class (DTC) for cyclone-prone agricultural lands using Landsat 8 OLI and TIRS datasets.

TL;DR: In this article, a study was conducted to estimate changes in vegetation and land surface temperature from Landsat TM (Thematic Mapper) of 1985 and Landsat 8 OLI (Operational Land Imager) of 2015 for Addis Ababa City, Ethiopia.

TL;DR: In this article, the authors evaluated annual soil loss using RUSLE Model in Telkwonz Watershed for prioritization and conservation measures in South Gondar Zone, Amhara Region, Ethiopia.

TL;DR: This study scrutinizes the efficacy of Artificial Neural Network, Maximum Likelihood Classifier (MLC) and Support Vector Machine (SVM) over hybrid datasets including optical, radar, DEMs and their derivatives and shows that SVM and MLC are much better than ANN.

TL;DR: In this article, the authors aimed at identifying groundwater potential recharge zones (GPRZ) of semi-arid midlands Manyara fractured aquifer using Geographic Information System (GIS) and Multi-criteria Decision Analysis (MCDA) based on Analytic Hierarchy Process (AHP) technique.

TL;DR: The broadcast specification model Soil and Water Assessment Tool (SWAT) has been tested on monthly basis for evaluating surface runoff from a small gauging station of Govindpur in NH5 road located along the Budhabalanga river basin this paper.

TL;DR: This research proposes the detection of CPIS using instance segmentation from multi-temporal SAR images that are cloud-free, and developed a CPIS database for the Cerrado biome based on visual interpretation, totaling 3675 instances in the Common Objects in Context (COCO) annotation format.

TL;DR: In this paper, four supervised machine learning algorithms (Support Vector Regression, Random Forest, Linear and Polynomial Regression) were used to forecast vegetation indices for 2019 and scored on their performance.

TL;DR: In this article, the authors used satellite remote sensing-based vegetation indices at the optimum harvesting time before flash flooding to develop rice yield prediction models for rice production, which were validated using both parametric (simple and multiple) and nonparametric (artificial neural network, ANN) regression analyses.

TL;DR: This study introduced a simple, scalable, and robust wetland classification by applying unsupervised (K-means cluster – KMC) and supervised (Support vector machine classification – SVMc) ML algorithms.

TL;DR: In this paper, a comparative performance of linear regression, polynomial regression and generalized additive model (GAM) was explored for canopy cover estimation in the dry deciduous forest of West Bengal.