Classification of polarimetric synthetic aperture radar (PolSAR) images is an active research area with a major role in environmental applications. The traditional Machine Learning (ML) methods proposed in this domain generally focus on utilizing highly discriminative features to improve the classification performance, but this task is complicated by the well-known "curse of dimensionality" phenomena

ABSTRACT The satellite-based nighttime lights (NTL) data from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS), available in the public domain from 1992 to 2013, are extensively used for socio-economic studies. The improved NTL products from the Visible Infrared Imaging Radiometer Suite’s Day/Night Band (VIIRS-DNB), on-board the Suomi National Polar-Orbiting Partnership

ABSTRACT The life cycle of clouds consists of mainly into three phases, namely developing, mature, and dissipating phases. The information about the vertical structure of the precipitation during different phases of development will improve their representation in the cloud models. Whether specific regimes over Peru favor the formation or decay of the cloud systems and how their intensity varies during

ABSTRACT Wetlands across Canada have been, and continue to be, lost or altered under the influence of both anthropogenic and natural activities. The ability to assess the rate of change to wetland habitats and related spatial pattern dynamics is of importance for effective and meaningful management and protection, particularly under the current context of climate change. The availability of cloud-based

ABSTRACT Extraction of urban objects, and analysis of two- and three-dimensional (2D and 3D) morphological parameters, as well as 2D and 3D landscape metrics in urban environments are valuable for updating GIS databases and for civic applications, urban planning, disaster risk assessment, and climate and sustainability studies. However, few studies have reported the extraction of 3D buildings at different

ABSTRACT Surface soil moisture (SSM) is an important parameter for many applications. Soil Moisture Active Passive (SMAP) satellite mission provides an SSM map at global scale. But its spatial resolution (36 km) is a big restriction for agricultural and hydrological studies at the catchment scale. Therefore, the present study was conducted to disaggregate the passive SMAP soil moisture data using the

ABSTRACT Urban heat islands (UHIs) are a key topic in urban climate studies. However, systematic criteria for UHI comparisons were lacking prior to 2012, when the concept of Local Climate Zones (LCZs) was introduced. By relating remotely sensed land surface temperature (LST) to LCZs, we explored the applicability of LCZs in surface urban heat island (SUHI) investigations and compared the LST variation

ABSTRACT Time-series reconstruction for cloud/shadow-covered optical satellite images has great significance for enhancing the data availability and temporal change analysis. In this study, we proposed a superpixel-based prediction transformation-fusion (SPTF) time-series reconstruction method for cloud/shadow-covered optical images. Central to this approach is the incorporation between intrinsic tendency

ABSTRACT Accurate regional identification of cropland quantities and spatial distributions is important for cropland monitoring, food security, and sustainable regional development. Various countries and organizations have produced series of land-cover products. However, variability among remote sensors, land-cover classification schemes, and classification methods has resulted in discrepancies. In

ABSTRACT We investigated the dynamics of savanna clearing and land degradation in the MATOPIBA region, the newest agricultural frontier over the Cerrado in Brazil. For geospatial analysis of the “big data,” we used algorithms available on the Google Earth Engine (GEE) platform along with different satellite products. For the detection of vegetation clearing in the 1985–2018 period, we applied the LandTrendr

ABSTRACT Incorporating spatial nonstationarity in urban models is essential to accurately capture its spatiotemporal dynamics. Spatially-varying coefficient methods, e.g. geographically weighted regression (GWR) and the Bayesian spatially-varying coefficient (BSVC) model, can reflect spatial nonstationarity. However, GWR possess weak ability eliminating the negative effects of non-constant variance

ABSTRACT Monitoring of snow cover variability is crucial because it is closely linked with mountain water resources, ecosystems, and climate change. For this, moderate-resolution imaging spectroradiometer (MODIS) daily snow cover products (SCPs, version 6) were used over the Chenab river basin (CRB) during 2001–2017. In these data, cloud cover is a significant problem that produces a discontinuity

ABSTRACT The dynamic nature of continental water body covers is a main indicator of the status of this ecological capital and its trends on the land surface. With synthetic aperture radar (SAR), various water coverage processes can be identified using satellite data. This study proposes the use of a basin-level analysis with SAR images at a medium spatial resolution to perform a regional spatial-temporal

ABSTRACT Understanding the impacts of flight configuration and post-mission data processing techniques on unmanned aircraft system (UAS) photogrammetric data quality is essential for employing this popular technique in coastal wetland ecosystems. In this study, we systematically evaluated the effects of flight configuration (flying altitude, image overlap, and lighting conditions) on UAS photogrammetric

ABSTRACT The trend in spatial data accuracy has gone from meters to decimeters and even centimeter levels in the last two decades. In large part, the centimeter(s) level spatial accuracies of geospatial products result from the use of LiDAR data and both low altitude manned and unmanned aerial systems for imagery and for topographic and surface models. All these sources are dependent on high precision/accuracy

ABSTRACT There is fine-scale spatial heterogeneity in key vegetation properties including leaf-area index (LAI) and biomass in treeless northern peatlands, and hyperspectral drone data with high spatial and spectral resolution could detect the spatial patterns with high accuracy. However, the advantage of hyperspectral drone data has not been tested in a multi-source remote sensing approach (i.e. inclusion

ABSTRACT Landsat-8 derived Land Surface Temperature (LST) is used to measure Surface Urban Heat Island (SUHI) patterns and intensity in Worcester, MA, USA. Additionally, near-surface air temperature variability is measured using in-situ sensors to further contextualize the urban-to-rural land-cover driven thermal patterns in the study area. Despite the widespread applicability of thermal data, many

ABSTRACT Inner Mongolia is an important ecological zone of northern China and 67% of its land area is grassland. This ecologically fragile region has experienced significant vegetation degradation during the last decades. Although the spatial extents and rates of vegetation change have previously been characterized through various remote sensing and GIS studies, the underlying driving factors of vegetation

ABSTRACT Information on urban settlements is crucial for sustainability planning and management. While remote sensing has been used to derive such information, its applicability can be compromised due to the complexity in the urban environment. In this study, we developed a remote sensing method to map land cover types in a large Latin-American city, which is well known for its mushrooming unplanned

ABSTRACT The climate in southern Iceland has warmed over the last 70 years, resulting in accelerated glacier dynamics at the Solheimajoküll glacier. In this study, we compare glacier terminus locations from 1973 to 2018, to changes in climate across the study area, and we derive ice-surface velocities (2015–2018) from satellite remote-sensing imagery (Sentinel-1) using the offset-tracking method. There

ABSTRACT Visibility determination is a key requirement in a wide range of national and urban applications, such as national security, landscape management, and urban design. Mobile LiDAR point clouds can depict the urban built environment with a high level of details and accuracy. However, few three-dimensional visibility approaches have been developed for the street-level point-cloud data. Accordingly

ABSTRACT Several machine learning regression models have been advanced for the estimation of crop biophysical parameters with optical satellite imagery. However, literature on the comparative performances of such models is still limited in range and scope, especially under multiple data sources, despite the potential of multi-source imagery to improving crop monitoring in cloudy areas. To fill in this

Climate change is today one of the biggest issues for farmers. The increasing number of natural disasters and change of seasonal trends is making insurance companies more interested in new technolo...

ABSTRACT Groundwater potential mapping (GWPM) in the coastal zone is crucial for the planning and development of society and the environment. The current study is aimed to map the groundwater potential zones of Sindhudurg coastal stretch on the west coast of India, using three machine learning models: random forest (RF), boosted regression tree (BRT), and the ensemble of RF and support vector machine

ABSTRACT In this paper, we propose a method to regenerate Rational Polynomial Coefficients (RPCs) using KOMPSAT-3A imagery and to reduce the geolocation error using minimum ground control points (GCPs). To estimate the new RPCs, the physical sensor model fitted to KOMPSAT-3A imagery was utilized and virtual GCPs over the study area were created. The size of the virtual grid used was 20x20x20. To remove

The 17 Sustainable Development Goals (SDGs) were adopted by the United Nations General Assembly on September 25th, 2015with the intention of being achieved by 2030. The SDGs were designed to be a “blueprint to achieve a better and more sustainable future for all.” While some people argue that the SDGs are too broad and include some contradicting issues between development and environmental protection

ABSTRACT Globally, drought constitutes a serious threat to food and water security. The complexity and multivariate nature of drought challenges its assessment, especially at local scales. The study aimed to assess spatiotemporal patterns of crop condition and drought impact at the spatial scale of field management units with a combined use of time-series from optical (Landsat, MODIS, Sentinel-2) and

ABSTRACT Researchers, policy makers, and farmers currently rely on remote sensing technology to monitor crops. Although data processing methods can be different among different remote sensing methods, little work has been done on studying these differences. In order for potential users to have confidence in remote sensing products, an analysis of mapping accuracies and their associated uncertainties

ABSTRACT Remote sensing images have long been recognized as useful for the detection of building damages, mainly due to their wide coverage, revisit capabilities and high spatial resolution. The majority of contributions aimed at identifying debris and rubble piles, as the main focus is to assess collapsed and partially collapsed structures. However, these approaches might not be optimal for the image

ABSTRACT Surface roughness of sea ice is primary information for understanding sea ice dynamics and air–ice–ocean interactions. Synthetic aperture radar (SAR) is a powerful tool for investigating sea ice surface roughness owing to the high sensitivity of its signal to surface structures. In this study, we explored the surface roughness signatures of the summer Arctic snow-covered first-year sea ice

ABSTRACT The reliable and robust monitoring of air temperature distribution is essential for urban thermal environmental analysis. In this study, a stacking ensemble model consisting of multi-linear regression (MLR), support vector regression (SVR), and random forest (RF) optimized by the SVR is proposed to interpolate the daily maximum air temperature (Tmax) during summertime in a mega urban area

ABSTRACT High-Resolution Topography (HRT) data sets are becoming increasingly available, improving our ability and opportunities to monitor geomorphic changes through multi-temporal Digital Terrain Models (DTMs). The use of repeated topographic surveys enables inferring the sediment dynamics of hazardous geomorphic processes such as floods, debris flows, and landslides, and allows us to derive important

ABSTRACT Spatial heterogeneity represents a general characteristic of the inequitable distributions of spatial issues. The spatial stratified heterogeneity analysis investigates the heterogeneity among various strata of explanatory variables by comparing the spatial variance within strata and that between strata. The geographical detector model is a widely used technique for spatial stratified heterogeneity

ABSTRACT To assess the effects of the Grain for Green Program (GGP) on soil erosion is essential to support better land management policies in the Chinese Loess Plateau. Studies on the evaluation of the effects of the GGP on soil erosion have garnered heightened attention. However, few studies examined the efficiency of GGP on soil erosion control through spatial relationship analysis. Thus, this study

ABSTRACT Monitoring of destructive invasive weeds such as those from the genus Striga requires accurate, near real-time predictions and integrated assessment techniques to enable better surveillance and consistent assessment initiatives. Thus, in this study, we predicted the potential ecological niche of Striga (Striga asiatica) weed in Zimbabwe, to identify and understand its propagation and map potentially

ABSTRACT Tree species distribution mapping using remotely sensed data has long been an important research area. However, previous studies have rarely established a comprehensive and efficient classification procedure to obtain an accurate result. This study proposes a hierarchical classification procedure with optimized node variables and thresholds to classify tree species based on high spatial resolution

ABSTRACT Impervious surface area (ISA) data are required for such studies as urban environmental modeling, hydrological modeling, and socioeconomic analysis, but updating these datasets in a large area remains a challenge due to the complex urban landscapes consisting of different materials and colors with various spatial patterns. This research explores the integration of multi-source remotely sensed

ABSTRACT Monitoring of inland water quality is of significant importance due to the increase in water quality related issues, especially within the Midwestern United States. Traditional monitoring techniques, although highly accurate, are vastly insufficient in terms of spatial and temporal coverage. Using a virtual constellation by harmonizing Landsat-8 and Sentinel-2 data a high temporal frequency

ABSTRACT Sustainable intensification of existing cropland is one of the most viable options for meeting the escalating food demands of the ever-increasing population in the world. Accurate geospatial data about the potential single-crop (rice-fallows) areas is vital for policymakers to target the agro-technologies for enhancing crop productivity and intensification. Therefore, the study aimed to evaluate

ABSTRACT Forest fires can change forest structure and composition, and low-density Airborne Laser Scanning (ALS) can be a valuable tool for evaluating post-fire vegetation response. The aim of this study is to analyze the structural diversity differences in Mediterranean Pinus halepensis Mill. forests affected by wildfires on different dates from 1986 to 2009. Several types of ALS metrics, such as

ABSTRACT Spatial variation of Urban Land Surface Temperature (ULST) is a complex function of environmental, climatic, and anthropogenic factors. It thus requires specific techniques to quantify this phenomenon and its influencing factors. In this study, four models, Random Forest (RF), Generalized Additive Model (GAM), Boosted Regression Tree (BRT), and Support Vector Machine (SVM), are calibrated

ABSTRACT Climatic factors such as rainfall and temperature play a vital role in the growth characteristics of vegetation. While the relationship between climate and vegetation growth can be accurately predicted in instances where vegetation is homogenous, this becomes complex to determine in heterogeneous vegetation environments. The aim of this paper was to study the relationship between remotely-sensed

ABSTRACT Commercial forest plantations are increasing globally, absorbing a large amount of carbon valuable for climate change mitigation. Whereas most carbon assimilation studies have mainly focused on natural forests, understanding the spatial distribution of carbon in commercial forests is central to determining their role in the global carbon cycle. Forest soils are the largest carbon reservoir;

Air quality has been a serious societal issue over the globe due to continued urbanization and ongoing climate change. Air quality affects human health and various ecosystems, such as farmlands, forests, livestock, and wildlife. Millions of people die worldwide every year due to exposure to air pollution. Climate change tends to increase the risk of wildfire leading to severe air pollution. Global

ABSTRACT Urban functional zones (UFZs) are important for urban sustainability and urban planning and management, but UFZ maps are rarely available and up-to-date in developing countries due to frequent economic and human activities and rapid changes in UFZs. Current methods have focused on mapping UFZs in a small area with either remote sensing images or open social data, but large-scale UFZ mapping

ABSTRACT The temporal resolution of vegetation indices (VIs) determines the details of seasonal variation in vegetation dynamics observed by remote sensing, but little has been known about how the temporal resolution of VIs affects the retrieval of land surface phenology (LSP) of grasslands. This study evaluated the impact of temporal resolution of MODIS NDVI, EVI, and per-pixel green chromatic coordinate

ABSTRACT Rapid economic growth, a high degree of urbanization and the proximity of a large number of desert and semidesert landscapes can have a significant impact on the atmosphere of adjacent territories, leading to high levels of atmospheric pollution. Therefore, identifying possible sources of atmospheric pollution is one of the main tasks. In this study, we carried out an analysis of spatial and

ABSTRACT The physical processes associated with the constituents of the troposphere, such as aerosols have an immediate impact on human health. This study employs a novel method to calibrate Aerosol Optical Depth (AOD) obtained from the MODerate resolution Imaging Spectrometer (MODIS – Terra satellite) for estimating surface PM2.5 concentration. The Combined Deep Blue Deep Target daily product from

ABSTRACT Widespread forest fire events occurred in the foothills of North Western Himalaya during 24 April to 2 May 2016 (Event-1) and 20–30 May 2018 (Event-2). Their impacts were investigated on the distribution of pollutant gases ozone (O3), carbon monoxide (CO), and oxides of nitrogen (NOx) over Uttarakhand using simulations of Weather Research and Forecasting model coupled with chemistry (WRF-Chem)

ABSTRACT The classification of tree species can significantly benefit from high spatial and spectral information acquired by unmanned aerial vehicles (UAVs) associated with advanced classification methods. This study investigated the following topics concerning the classification of 16 tree species in two subtropical forest fragments of Southern Brazil: i) the potential integration of UAV-borne hyperspectral

ABSTRACT The automated classification of ambient air pollutants is an important task in air pollution hazard assessment and life quality research. In the current study, machine learning (ML) algorithms are used to identify the inter-correlation between dominant air pollution index (API) for PM10 percentile values and other major air pollutants in order to detect the vital pollutants’ clusters in ambient

ABSTRACT Temporal trajectories of apparent vegetation abundance based on the multi-decadal Landsat image series provide valuable information on the postfire recovery of chaparral shrublands, which tend to mature within one decade. Signals of change in fractional shrub cover (FSC) extracted from time-sequential Normalized Difference Vegetation Index (NDVI) data can be systematically biased due to spatial

ABSTRACT Particulate matter (PM) is a widely used indicator of air quality. Satellite-derived aerosol products such as aerosol optical depth (AOD) have been a useful source of data for ground-level PM monitoring. However, satellite-based approaches for PM monitoring have limitations such as impacts of cloud cover. Recently, many studies have documented advances in modeling for monitoring PM over the

ABSTRACT Globally, countries have experienced substantial increases in farmland abandonment. Although vegetation phenology is a key factor for the classification of land use, understanding of the phenological change of abandoned farmland is lacking. Using harmonic analysis of NDVI and NDWI extracted from Landsat imagery, this study investigates the distinctive phenological characteristics of abandoned

ABSTRACT Filtering is one of the key steps for Digital Elevation Model (DEM) generation from airborne Light Detection and Ranging (LiDAR) data. Machine-learning-based filters have emerged as a class of filtering algorithms in recent years. Most existing studies mainly focus on feature generation due to limited available features a point cloud possesses. More than 30 features have been described in

ABSTRACT Particulate Matter (PM) emissions originating from mine waste and mine tailings can be hazardous to human health, depending on the ore type and processes used to extract ore. Until now, only a single, simple estimate of the total area of mine waste area across all of Canada has been available for calculating air quality emissions from this source. This single estimate, based on manual satellite

ABSTRACT The South Asia (India, Pakistan, Bangladesh, Nepal, Sri Lanka and Bhutan) has a staggering 900 million people (~43% of the population) who face food insecurity or severe food insecurity as per United Nations, Food and Agriculture Organization’s (FAO) the Food Insecurity Experience Scale (FIES). The existing coarse-resolution (≥250-m) cropland maps lack precision in geo-location of individual

ABSTRACT The landslide, which occurred at Umyeon mountain (Mt. Umyeon) in Seoul, Korea in 2011, was a prime example that raised awareness about the landslide in the highly urbanized area. Although many studies have been done on Umyeon landslide, there is a lack of research that detects the area where the landslide occurred and quantifies the elevation changes through remote sensing data. In this regard

ABSTRACT Optical satellite data is an efficient and complementary method to hydrographic surveys for deriving bathymetry in shallow coastal waters. Empirical approaches (in particular, the models of Stumpf and Lyzenga) provide a practical methodology to derive bathymetric information from remote sensing. Recent studies, however, have focused on enhancing the performance of such empirical approaches

ABSTRACT Atmospheric aerosols can alter the direct and diffuse components of global solar radiation, which further influences terrestrial gross primary productivity (GPP) via photosynthesis. To investigate the impact of aerosols on GPP, GPP is modeled using the Boreal Ecosystem Productivity Simulator (BEPS) under two aerosol scenarios (S1& S2) over cropland and grassland ecosystems in the highly polluted