ABSTRACT The Aral Sea, once the fourth largest freshwater lake on Earth, has lost circa 90% of its original water surface in 1960. Maps of different land cover categories provide a suitable baseline to plan and implement effective measures to combat ongoing desertification, such as reforestation of dried out Aral Sea soils. In this study, we used satellite-based remote sensing data and applied a machine

ABSTRACT Distribution modeling methods are used to provide occurrence probability surfaces for modeled targets. While most often used for modeling species, distribution modeling methods can also be applied to vegetation types. However, surfaces provided by distribution modeling need to be transformed into classified wall-to-wall maps of vegetation types to be useful for practical purposes, such as

ABSTRACT Recent improvements in the spatial, temporal, and spectral resolution of satellite images necessitate (semi-)automated classification and information extraction approaches. Therefore, we developed an integrated fuzzy object-based image analysis and deep learning (FOBIA-DL) approach for monitoring the land use/cover (LULC) and respective changes and compared it to three machine learning (ML)

ABSTRACT Wetlands are among the most important, yet in danger ecosystems and play a vital role for the well-being of humans as well as flora and fauna. Over the past few years, state-of-the-art deep learning (DL) tools have gained attention for wetland classification within the remote sensing community. However, the DL methods could have complex structure and their efficiency greatly depends on the

ABSTRACT Land covers provide essential information for understanding and detecting ecosystem, resources, and environmental dynamics. However, they are generally mapped at coarser temporal scales to study the inter-annual changes, while scant attention has been paid to map intra-annual land cover dynamics at finer temporal scales. Moreover, existing studies are still limited in intra-annual land cover

ABSTRACT Uncontrolled extraction of water from groundwater aquifers causes groundwater depletion, which in turn triggers the formation of sinkholes in many parts of the world. Monitoring and detection of these geomorphologic features are of utmost importance and priority for the decision-makers to minimize significant environmental as well as socio-economic implications of land deformation. In this

ABSTRACT Remote sensing has long been used to study wildlife; however, manual methods of detecting wildlife in aerial imagery are often time-consuming and prone to human error, and newer computer vision techniques have not yet been extensively applied to wildlife surveys. We used the object-based image analysis (OBIA) software eCognition to detect laughing gull (Leucophaeus atricilla) nests in Jamaica

ABSTRACT The Mount Elgon ecosystem (MEE), an important hydrological and socio-economic area in East Africa, has exhibited significant landscape changes. These are driven by both natural factors and human activities. Yet, the vulnerability of this ecosystem is poorly understood. This study characterizes ecological and environmental (eco-environmental) vulnerability for the MEE using freely available

ABSTRACT Monitoring of the vegetation change trajectory in mining areas is crucial to understand ecosystem degradation induced by mining activities and to evaluate the effectiveness of ecological restoration. Beijing, the capital of China, is rich in mineral resources and has had many small-scale mine sites. In this study, we aimed to propose a new method to automatically characterize vegetation degradation

ABSTRACT Fog is a phenomenon that occurs very close to the ground or sea level, and when detected by satellite, it is difficult to distinguish it from the low-level cloud. Logistic regression can help identify the false detection of the low-level cloud as fog and improve the accuracy of fog detection. In this study, a Korean fog detection algorithm was developed by using a machine learning-based logistic

ABSTRACT MODIS sensors are widely used in a broad range of environmental studies, many of which involve joint analysis of multiple MODIS spectral bands acquired at disparate spatial resolutions. To extract land surface information from multi-resolution MODIS spectral bands, existing studies often downscale lower resolution (LR) bands to match the higher resolution (HR) bands based on simple interpolation

ABSTRACT Poplars are one of the most widespread fast-growing tree species used for forest plantations. Owing to their distinct features (fast growth and short rotation) and the dependency on the timber price market, poplar plantations are characterized by large inter-annual fluctuations in their extent and distribution. Therefore, monitoring poplar plantations requires a frequent update of information

ABSTRACT InSAR processing is vastly used for land deformation monitoring from the space. Machine learning methods are known as strong tools for data modeling as well as predicting. In this study, we are going to model and predict the future behavior of land subsidence by InSAR processing and leveraging deep learning methods over the lands in the vicinity of Lake Urmia (located in the northwest of Iran)

ABSTRACT Air pollution is a significant urban issue, with practical applications for pollution control, urban environmental management planning, and urban construction. However, owing to the complexity and differences in spatiotemporal changes for various types of pollution, it is challenging to establish a framework that can capture the spatiotemporal correlations of different types of air pollution

ABSTRACT Wind and soil confrontation under specific atmospheric conditions leads to the horrendous phenomenon of dust storms, particularly in arid lands. The negative impact of storms on the health and life of living beings is evident, resulting in many losses in the economic, social, and environmental sectors. Dust storm detection is the most important topic in dust studies, where many algorithms

ABSTRACT The Forel–Ule index (FUI) is one of the indicators used to assess the trophic state of inland waters, which can be objectively determined based on satellite-retrieved corrected hue angles (α). However, the functions for α correction have been proposed based on a dataset from Dutch and the North Sea, which might be inadequate for the precise FUI-derived trophic state. This study proposed a

ABSTRACT Reliable spatiotemporal crop data are vital for sustainable agricultural management. However, efficient algorithms that can be automatically applied to large regions are scarce, especially for cash crops, since it is hard to distinguish their uniqueness merely from temporal profiles of traditional vegetation indices. The efficiency of knowledge-based temporal features and red-edge pigment

ABSTRACT Recently, measurement of sea ice thickness (SIT) has received increasing attention due to the importance of thinning ice in the context of global warming. Although altimeter sensors onboard satellite missions enable continuous SIT measurements over larger areas compared to in situ observations, these sensors are inadequate for mapping daily Arctic SIT because of their small footprints. We

ABSTRACT A modeling framework that can quantify the impact of multiple potential drivers on coastal wetland loss while estimating its uncertainties is needed to better conserve and restore these valuable ecosystems. We developed a Bayesian multi-level modeling framework to predict the areal wetland loss in the northern Gulf of Mexico, driven by relative sea-level rise (RSLR), vegetation productivity

ABSTRACT Seasonal alpine snow contributes significantly to the water resource. It plays a crucial role in regulating the environmental feedback and from the perspective of socio-economic sustainability in the alpine regions. While most nations are pursuing renewable energy sources, hydropower generated from snowmelt runoff is one of the primary sources. Additionally, alpine regions with snow cover

ABSTRACT Accurate information on cropland evapotranspiration (ET) can facilitate effective agricultural management. However, the application of existing physical models over broad regions may be impeded due to the need for difficult to acquire information about environmental factors that constrain ET. The recently developed near-infrared reflectance of vegetation (NIRv), which can reasonably characterize

ABSTRACT Mapping the distribution and type of land use and land cover (LULC) is essential for watershed management. The Tigris-Euphrates basin is a transboundary region in the Middle East shared between six countries, but a recent fine-scale LULC map of the area is lacking. Using Landsat-8 time series, a 30-m resolution LULC map was produced for the Tigris-Euphrates basin. In total, 1184 Landsat scenes

ABSTRACT Climate and land-use change profoundly affect plant species distribution (SD) and composition, and the impact of these processes is expected to increase in the coming years. As a proxy of global changes, knowledge of SD and diversity along climatic gradients is essential to determine the efforts needed for species conservation. Plant spectral diversity is an emerging approach used as a proxy

ABSTRACT The soil roughness and two-way vegetation transmissivity parameters affect the radar backscattered signal; therefore, the results of the soil moisture retrieval from Synthetic Aperture Radar (SAR) images. In this paper, the Improved Water Cloud Model was extended to specify the effects of the above parameters. The surface roughness parameter values were calculated for each ground measurement

ABSTRACT Southern China is a major rice-producing region where mixed rice cropping systems (single cropping rice (SCR) and double-cropping rice (DCR)) are widely practiced. An accurate estimation of the spatiotemporal changes in cropping systems is important for understanding the dynamic of paddy rice system and for developing suitable agricultural policies. However, few studies focus on mapping long-term

ABSTRACT Eutrophication is the leading cause of freshwater impairment. Of China’s major cities, Wuhan has the largest coverage of the lake area, while lake eutrophication is increasing. Thus, the objective of this study was to monitor and assess lake eutrophication in Wuhan. For this purpose, the lake eutrophication was quantified using the Trophic Level Index (TLI). The TLI was modeled by building

ABSTRACT Urban tree species classification is a challenging task due to spectral and spatial diversity within an urban environment. Unmanned aerial vehicle (UAV) platforms and small-sensor technology are rapidly evolving, presenting the opportunity for a comprehensive multi-sensor remote sensing approach for urban tree classification. The objectives of this paper were to develop a multi-sensor data

ABSTRACT The study of trajectories of people, vehicles, and animals has many applications. Clustering is one of the ways to extract movement behaviors in trajectories. Due to the complex behavioral nature of trajectories, different geometric criteria, such as distance, shape, sinuosity, complexity, and orientation, are used for trajectory (dis)similarity calculation depending on the types of data.

ABSTRACT This study develops an aerosol data assimilation and forecast system using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and the three-dimensional variational (3D-VAR) data assimilation method. The system assimilates the aerosol optical depth (AOD) from the Geostationary Ocean Color Imager (GOCI) satellite and surface particulate matter (PM) observations. The

ABSTRACT The parametrization of wood volume equations has traditionally been carried out with destructive samplings, which are highly resource-intensive. These equations must be specifically set up for each species and set of conditions, meaning that, in many cases, they are unfeasible or non-existent. Here, we present a nondestructive and fully automated methodology for the parametrization of merchantable

ABSTRACT Oil spills are one of the most hazardous disasters with significant short- and long-term effects on fragile marine ecosystems. Synthetic Aperture Radar (SAR) has been considered an effective technology for mapping and monitoring oil spills in the marine environment, primarily thanks to its weather-, illumination-, and time-independent capabilities. To cope with serious oil spill threats, researchers

ABSTRACT In this study, we present a new automatic deep learning-based network named Road Vectorization Network (RoadVecNet), which comprises interlinked UNet networks to simultaneously perform road segmentation and road vectorization. Particularly, RoadVecNet contains two UNet networks. The first network with powerful representation capability can obtain more coherent and satisfactory road segmentation

ABSTRACT We investigated the potential of hyperspectral remote sensing to estimate aboveground biomass (AGB) over the Brazilian savannas (Cerrado), the second-largest source of carbon emissions in Brazil. For this purpose, a Hyperion/Earth Observing-1 (EO-1) image was collected in the dry season at the Ecological Station of Águas Emendadas (ESAE). In order to estimate the AGB, we evaluated the performance

ABSTRACT We investigate the temporal dynamics of shifts in phenological responses of a range of key stages of the growing season in New Zealand’s three indigenous grassland types over the last 16 years (2001–2016). A near-daily Normalized Difference Vegetation Index (NDVI) time series from MODerate Resolution Imaging Spectroradiometer (MODIS) was used to extract five annual growth phenology indices

ABSTRACT Overshooting tops (OTs) play a crucial role in carrying tropospheric water vapor to the lower stratosphere. They are closely related to climate change as well as local severe weather conditions, such as lightning, hail, and air turbulence, which implies the importance of their detection and monitoring. While many studies have proposed threshold-based detection models using the spatial characteristics

ABSTRACT Accurate, long time-series, high-resolution mapping of built-up land dynamics is essential for understanding urbanization and its environmental impacts. Despite advances in remote sensing and classification algorithms, built-up land mapping which only uses spectral data and derived indices remains prone to uncertainty. We mapped the extent of built-up land in the North China Plain, one of

ABSTARCT In the Grassland Natural Region (GNR) of southern Alberta, wetlands are relatively small-sized disconnected prairie pothole marshes, swamps, and shallow open water habitats often surrounded by grasslands, parkland forests, agricultural lands, and urban areas. These wetlands are susceptible to climatic variability, resulting in temporally and spatially dynamic habitats that are difficult to

ABSTRACT Surface urban heat island (SUHI) is a major anthropogenic alteration of Earth’s surface and can influence the local thermal environment by altering the surface energy flux balances. Researchers have paid much attention to SUHI studies in the last decades; still, its geospatial variability over a larger area is poorly understood. Detailed research is required to understand the mechanism and

ABSTRACT Cellular automata (CA)-based models have been extensively used in urban expansion modeling because of their simplicity, flexibility and intuitiveness. Previous studies on CA-based urban growth modeling have mainly focused on the process of spatial allocation of increased urban lands; however, the temporal contexts during the simulation have not been properly explored. In this study, we examined

ABSTRACT Remote sensing and geospatial techniques are being used to provide large-scale and regional solutions for achieving the sustainable development goals (SDGs) of the United Nations, including sustainable infrastructure development. Road transportation infrastructure has a significant contribution to the economy, but it also increases environmental pressure. However, little knowledge is available

ABSTRACT Computing trajectory similarity is a fundamental operation in movement analytics, required in search, clustering, and classification of trajectories, for example. Yet the range of different but interrelated trajectory similarity measures can be bewildering for researchers and practitioners alike. This paper describes a systematic comparison and methodical exploration of trajectory similarity

ABSTRACT The regional scale mass change provides a substantial understanding to the influence of climate change on the glacier surface. However, the scarce ground observations and large spatial extent limit the use of field-based mass balance estimation. In this study, the Gravity Recovery and Climate Experiment (GRACE) satellite data were used to estimate the regional glacier mass variation over the

ABSTRACT High spatiotemporal population data are critical for a wide range of applications (e.g. urban planning and management, risk assessment, and epidemic control). However, such data are still not widely available due to the limited knowledge of complex human activities. Here we proposed a spatiotemporal downscaling framework for estimating hourly population dynamics in Beijing by integrating remote

ABSTRACT Substantial studies have been conducted to simulate urban growth in the rapidly growing regions for planning and management. However, the difficulty remains in the establishment of urban growth models designed for megacity regions, particularly due to spatial differentiations in the distribution and driving forces of urban dynamics among sub-regions. In addition, limited studies have examined

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