Showing papers in "Remote Sensing Applications: Society and Environment in 2022"

TL;DR: In this paper , the authors analyzed the spatial variability in urban heat islands (UHIs) in Pakistan from 2000 to 2019, combining MODIS satellite imagery with data from fifty meteorological stations.

TL;DR: In this article , a literature review was conducted utilizing three bibliographic databases, including Google Scholar, Scopus, Web of Science, and 179 research articles that are relevant to UAV applications in sugarcane and other general information about UAV and sensors collected from the databases mentioned earlier.

TL;DR: In this article , a hybrid 3D/2D CNN method is used together with dimension reduction methods to improve HRSI classification performance, which consists of a combination of 3D CNN, 2D CNN and depthwise separable convolution.

TL;DR: In this article , the integration of remote sensing and social sensing data to derive informed flood extent maps was explored, and state-of-the-art deep learning methods were employed to process heterogeneous data obtained from four case-study areas, including two urban regions from Somalia and India and two coastal regions from Italy and The Bahamas.

TL;DR: In this paper , the authors used four satellite images of Landsat 5 and 8 for LULC mapping of mangrove wetlands and their adjacent areas to evaluate the change dynamics for 1990, 2000, 2010, and 2020.

TL;DR: In this paper , the authors examined the relationship between land cover land use (LULC) and other factors on the urban land surface temperature (LST) in Ilorin from the period of 2002-2020.

TL;DR: In this paper , UAV-based spectral bands, canopy height, vegetation indices (VI), and texture features were generated by gray level co-occurrence matrix (GLCM) and integrated to predict crop grain yield using five machine learning regression models, including Cubist, Extreme Gradient Boosting (XGBoost), Stochastic gradient boosting (GBM), Support vector machine (SVM), and Random Forest (RF).

TL;DR: In this article , the authors investigated the potential to produce reliable maps of PM2.5 surface concentrations for Germany and parts of the surrounding countries using AOD based on observations by three different satellite sensors.

TL;DR: In this paper , a continuous coverage of multi-spectral optical and synthetic aperture radar (SAR) along with sparsely global ecosystem dynamics investigation (GEDI) spaceborne LiDAR data in the machine learning (ML) models for mapping Hcanopy in the mixed tropical forests of Shoolpaneshwar wildlife sanctuary (SWLS), Gujarat, India.

TL;DR: In this article , the authors made a concerted attempt to assess the health conditions of coastal wetland ecosystem in the Sundarban Biosphere Reserve (SBR), India during 1989-2017.

TL;DR: In this article , the variability in sargasso beachcast accumulation in the northern Mexican Caribbean coast in 2018 and 2019 and its relationship with: (1) wind direction and speed, (2) sea surface temperature, and (3) Sargasso biomass measured with MODIS in the southwestern Caribbean Sea and the Mexican Caribbean exclusive economic zone.

TL;DR: In this article , the authors focused on the evaluation of spatio-temporal dynamics of vegetation utilizing the trend and rate of change of annual median NDVI (Normalized Difference Vegetation Index) of EIHR.

TL;DR: In this paper , the authors analyzed spatial correlations of NDVI and MSAVI2 indices in a large urban agglomeration, which was located almost in the center of Poland and the central part of Europe.

TL;DR: In this article , the authors described and analyzed the land cover changes in the entire Río de la Plata Grasslands (RPG) region for the first two decades of the 21st century, especially those related to grasslands loss.

TL;DR: In this paper , the authors used the combination of spectral data from Sentinel-2 and synthetic aperture radar polarization (Pol) features for the monthly mapping of burned areas (BAs) extent using machine learning algorithms, such as random forests (RFs) and extreme gradient boosting (XGB).

TL;DR: In this paper , the authors used the maximum likelihood classification (MLC) tool for the year 1991, 2001, 2011 and 2021 to identify the LULC classes in the study area from 1991 to 2021 with their change dynamics and predict the land cover changes in 2050.

TL;DR: In this article , the authors employed various MODIS satellite products (i.e., land cover, leaf area index (LAI), and gross primary productivity) for a comprehensive study of vegetation dynamics over the Rajmahal Hills (RH) in the state of Jharkhand during 2001-2019.

TL;DR: In this article , the authors used the spatial principal component analysis (SPCA) method to construct EVI by integrating satellite imagery and socio-economic data like Gross Regional Domestic Product (GRDP) per capita and population density.

TL;DR: In this paper , the authors leveraged cloud-computing-based Google Earth Engine and geo-information modelling techniques to provide spatial-temporal insights regarding LULC and LST over the past three decades (1990-2020) in Pakistan.

TL;DR: In this paper , the results of classification accuracy were investigated based on the phenology of agricultural products and using time series of Landsat 8, Sentinel-1 and the digital elevation model (DEM).

TL;DR: In this paper , a novel script model built-in ArcMap for image pre-processing and processing was developed to analyze short-term coastal landform changes on the east coast of Sri Lanka using remote sensing and geographic information system techniques.

TL;DR: In this paper, the authors leveraged cloud-computing-based Google Earth Engine and geo-information modelling techniques to provide spatial-temporal insights regarding LULC and LST over the past three decades (1990-2020) in Pakistan.

TL;DR: In this paper , the performance of Support Vector Machine (SVM), Shannon's Entropy (SE), and Analytical Hierarchy Process (AHP) methods in the preparation of flood susceptibility mapping and assess their contextual suitability.

TL;DR: In this paper , two models are proposed for the estimation of the surface soil moisture, namely a model based on partial least squares regression (PLSR) and radar backscatter, and a model with partial least square regression and Stokes parameters.

TL;DR: In this article , the authors used Landsat time-series with 10-year intervals from 1991 to 2021 to extract information on land use and land cover (LULC) changes in the Ternata oasis over the past thirty years by using maximum likelihood classification (MLC) and the normalized difference vegetation index (NDVI).

TL;DR: In this article , the authors examined the extent to which the Shannon entropy index (H) can be used to ensure urban growth sustainability by measuring the spatial dispersion pattern of a built-up area.

TL;DR: In this paper , the classification performance of three optical satellite data in an area located in northwestern Morocco was evaluated using overall accuracy, Cohen's kappa, and F-score using random forest algorithm.

TL;DR: In this paper , the authors investigated if and by how much higher resolution satellite imagery improves crop yield estimation accuracy at the county level when paired with a high-resolution cropland mask.

TL;DR: SatRed as mentioned in this paper is based on a densely-connected neural network to classify land use and land cover from Sentinel-2 imagery and data acquired in the field and showed a 0.909 ± 0.009% overall accuracy and outperformed the seven most traditional Machine Learning methods, including Random Forest.

TL;DR: In this paper , a Google Earth Engine tool was developed, to facilitate access, processing and estimation of properties of remotely sensed NDVI time series in distinct zones, that corresponds to the fire-affected area, whereas the second one delineates a zone of equal area and with the same land cover characteristics to the burned land as described by the CORINE Level 1 and Level 3 nomenclature.