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[Display omitted] • Land-use type affects the activity and community of soil autotrophic microbes. • Land-use type influences the fate of CO 2 assimilated by soil autotrophic microbes. • HCO 3 –-utilizing microbes determine variation of autotrophic community in karst soil. CO 2 fixation by soil autotrophic microbes could make considerable contribution to soil organic matter (SOM). However, the effects of different land-use types on the activity and community of soil autotrophic microbes are still unclear. In this study, the autotrophic carbon fixation (ACF) rates of karst soils with four land-use types were assessed by 13CO 2 labelling. After 21 days incubation under alternate 12 h light with darkness, the ACF rate was 0.18 mg C·kg-1·day-1 in bare land soil, approximately 0.39 mg C·kg-1·day-1 in crop land and shrub land soils, while sharply increased to 1.54 mg C·kg−1·day−1 in grass land soil. The 13C nuclear magnetic resonance analysis of SOM showed that the fixed 13CO 2 was mainly incorporated into carbonyl carbon in grass land soil, but largely incorporated into recalcitrant aromatic carbon in other land-use types, especially in bare land. Longer incubation time (21 days versus 7 days) greatly elevated ACF rate only in grass land soil (by about 4 times) under continuous light conditions, suggesting that longer incubation in light conditions largely stimulated microbial phototrophic CO 2 fixation in grass land soil. Metagenomic analysis showed that the soil autotrophic microbial communities were prominently different among the four land-use types, and the autotrophic populations using bicarbonate as inorganic carbon species were most important for explaining the variations of autotrophic communities among the land-use types. Additionally, total nitrogen and soil organic carbon contents could be the key controlling factors of both ACF rates and structure of autotrophic microbial communities. Eventually, the change of soil properties caused by land-use type substantially affected the activity and community of soil autotrophic microbes, as well as the fate and stability of the assimilated CO 2. [ABSTRACT FROM AUTHOR]

The Prairie Pothole Region of North America is characterized by millions of depressional wetlands, which provide critical habitats for globally significant populations of migratory waterfowl and other wildlife species. Due to their relatively small size and shallow depth, these wetlands are highly sensitive to climate variability and anthropogenic changes, exhibiting inter- and intra-annual inundation dynamics. Moderate-resolution satellite imagery (e.g., Landsat, Sentinel) alone cannot be used to effectively delineate these small depressional wetlands. By integrating fine spatial resolution Light Detection and Ranging (LiDAR) data and multi-temporal (2009–2017) aerial images, we developed a fully automated approach to delineate wetland inundation extent at watershed scales using Google Earth Engine. Machine learning algorithms were used to classify aerial imagery with additional spectral indices to extract potential wetland inundation areas, which were further refined using LiDAR-derived landform depressions. The wetland delineation results were then compared to the U.S. Fish and Wildlife Service National Wetlands Inventory (NWI) geospatial dataset and existing global-scale surface water products to evaluate the performance of the proposed method. We tested the workflow on 26 watersheds with a total area of 16,576 km2 in the Prairie Pothole Region. The results showed that the proposed method can not only delineate current wetland inundation status but also demonstrate wetland hydrological dynamics, such as wetland coalescence through fill-spill hydrological processes. Our automated algorithm provides a practical, reproducible, and scalable framework, which can be easily adapted to delineate wetland inundation dynamics at broad geographic scales. • A fully automated algorithm was developed to map wetland inundation dynamics. • Multiple wetland inundation maps (1-m) were produced for the Prairie Pothole Region. • Mapped wetlands show high accuracy when compared to existing surface water products. • The algorithm is scalable for mapping wetland inundation at large geographic scales. [ABSTRACT FROM AUTHOR]

In terrain analysis and hydrological modeling, surface depressions (or sinks) in a digital elevation model (DEM) are commonly treated as artifacts and thus filled and removed to create a depressionless DEM. Various algorithms have been developed to identify and fill depressions in DEMs during the past decades. However, few studies have attempted to delineate and quantify the nested hierarchy of actual depressions, which can provide crucial information for characterizing surface hydrologic connectivity and simulating the fill‐merge‐spill hydrological process. In this paper, we present an innovative and efficient algorithm for delineating and quantifying nested depressions in DEMs using the level‐set method based on graph theory. The proposed level‐set method emulates water level decreasing from the spill point along the depression boundary to the lowest point at the bottom of a depression. By tracing the dynamic topological changes (i.e., depression splitting/merging) within a compound depression, the level‐set method can construct topological graphs and derive geometric properties of the nested depressions. The experimental results of two fine‐resolution Light Detection and Ranging‐derived DEMs show that the raster‐based level‐set algorithm is much more efficient (~150 times faster) than the vector‐based contour tree method. The proposed level‐set algorithm has great potential for being applied to large‐scale ecohydrological analysis and watershed modeling. Research Impact Statement: An efficient level‐set method for delineating and characterizing nested depressions in digital elevation models (DEMs) for terrain analysis and hydrological modeling. [ABSTRACT FROM AUTHOR]

Drumlins are ubiquitous landforms in previously glaciated regions, formed through a series of complex subglacial processes operating underneath the paleo-ice sheets. Accurate delineation and characterization of drumlins are essential for understanding the formation mechanism of drumlins as well as the flow behaviors and basal conditions of paleo-ice sheets. Automated mapping of drumlins is particularly important for examining the distribution patterns of drumlins across large spatial scales. This paper presents an automated vector-based approach to mapping drumlins from high-resolution light detection and ranging (LiDAR) data. The rationale is to extract a set of concentric contours by building localized contour trees and establishing topological relationships. This automated method can overcome the shortcomings of previously manual and automated methods for mapping drumlins, for instance, the azimuthal biases during the generation of shaded relief images. A case study was carried out over a portion of the New York Drumlin Field. Overall 1181 drumlins were identified from the LiDAR-derived DEM across the study region, which had been underestimated in previous literature. The delineation results were visually and statistically compared to the manual digitization results. The morphology of drumlins was characterized by quantifying the length, width, elongation ratio, height, area, and volume. Statistical and spatial analyses were conducted to examine the distribution pattern and spatial variability of drumlin size and form. The drumlins and the morphologic characteristics exhibit significant spatial clustering rather than randomly distributed patterns. The form of drumlins varies from ovoid to spindle shapes towards the downstream direction of paleo ice flows, along with the decrease in width, area, and volume. This observation is in line with previous studies, which may be explained by the variations in sediment thickness and/or the velocity increases of ice flows towards ice front. [ABSTRACT FROM AUTHOR]

[Display omitted] • PSB addition notably increased available phosphorus from 0.83 to 1.23 g kg−1. • PSB addition significantly increased abundances of phosphorus-cycling genes. • PSB inoculation notably affected compost bacterial community composition. • Stochastic and homogenizing processes affected more on bacterial community assembly. • Rare bacteria potentially mediated organic phosphorus mineralization. Understanding ecological roles of phosphate-solubilizing bacteria (PSB) is important to optimize composting systems. Illumina MiSeq sequencing, gene quantitation, and statistical analyses were employed to explore ecological mechanisms underlying available phosphorus (AP) facilitation during composting with the inoculation of PSB Pseudomonas sp. WWJ-22. Results displayed that the inoculation of PSB significantly increased AP from 0.83 to 1.23 g kg−1, and notably increased abundances of phosphorus-cycling genes as well as numbers of PSB mineralizing phytate and lecithin. The PSB addition significantly affected compost bacterial community composition, and phosphorus factions and phosphorus-cycling genes independently explained 25.4 % and 25.0 % bacterial compositional dissimilarity. Stochastic and homogenizing processes affected more on bacterial community assembly, and rare bacteria potentially mediated organic phosphorus mineralization. These results emphasized that phosphorus fractions, PSB number, phosphorus-cycling gene abundance, and bacterial community composition contributed differently to phosphorus availability. Findings highlight ecological roles of exogenous PSB during chicken manure composting. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and machine learning (ML) have been applied to solve various remote sensing problems. To fully leverage the power of AI and ML to tackle impactful remote sensing problems, it is essential to enable researchers and practitioners to understand how AI and ML models actually work and thus to improve the model performance strategically. Accurate and timely land cover maps are essential components for informed land management decision making. To address the ever-increasing need for high spatial and temporal resolution maps, this paper developed an interactive and open-source online tool, in Python, to help interpret and improve the ML models used for land cover mapping with Google Earth Engine (GEE). The tool integrates the workflow of both land cover classification and land cover change dynamics, which requires the generation of a time series of land cover maps. Three feature importance metrics are reported, including impurity-based, permutation-based, and SHAP (Shapley additive explanations) value-based feature importance. Two case studies are presented to showcase the tool's capability and ease of use, enabling a globally accessible and free convergent application of remote sensing technologies. This tool may inspire researchers to facilitate explainable AI (XAI)-empowered remote sensing applications with GEE. [ABSTRACT FROM AUTHOR]

Hurricanes and tropical storms are severe threats to coastal properties, settlements, and infrastructure. Airborne light detection and ranging (lidar) surveys conducted before and after storm events allow detailed analysis of coastal geomorphologic and sediment volumetric changes and have been proved very useful in the study of coastal changes. Traditionally, most studies use the pixel-based differencing method to quantify the spatial extent and magnitude of coastal changes based on sequential lidar surveys. This research presents a graph theory-based approach and associated software tools for representing and quantifying storm-induced damages to buildings, beaches and sand dunes, coastal vegetation canopy, and infrastructure. Generation of elevation difference grids, construction of local contour trees, and derivation of semantic properties are key components of the new algorithm for change object detection and extraction. An ontology and taxonomy are proposed to classify change objects into different types of coastal damages in terms of their semantic properties. This method has been successfully applied to assess damages of Hurricane Ike to the Bolivar Peninsula on the Texas Gulf Coast based on pre- and post-storm airborne lidar data and colour infrared aerial photographs. [ABSTRACT FROM PUBLISHER]

Sinkhole mapping is critical for understanding hydrological processes and mitigating geological hazards in karst landscapes. Current methods for identifying sinkholes are primarily based on visual interpretation of low-resolution topographic maps and aerial photographs with subsequent field verification, which is labor-intensive and time-consuming. The increasing availability of high-resolution LiDAR-derived digital elevation data allows for an entirely new level of detailed delineation and analyses of small-scale geomorphologic features and landscape structures at fine scales. In this paper, we present a localized contour tree method for automated extraction of sinkholes in karst landscapes. One significant advantage of our automated approach for sinkhole extraction is that it may reduce inconsistencies and alleviate repeatability concerns associated with visual interpretation methods. In addition, the proposed method has contributed to improving the sinkhole inventory in several ways: (1) detection of non-inventoried sinkholes; (2) identification of previously inventoried sinkholes that have been filled; (3) delineation of sinkhole boundaries; and (4) characterization of sinkhole morphometric properties. We applied the method to Fillmore County in southeastern Minnesota, USA, and identified three times as many sinkholes as the existing database for the same area. The results suggest that previous visual interpretation method might significantly underestimate the number of potential sinkholes in the region. Our method holds great potential for creating and updating sinkhole inventory databases at a regional scale in a timely manner. [ABSTRACT FROM AUTHOR]

Soil conservation ecosystem services (SCESs) are related to food production, safety of residents, water quality, and public health. However, the traditional assessment framework only focuses on a single dimension of SCESs, and the efficiency and development trends are often overlooked. A multidimensional assessment framework was established to assess SCESs in terms of the total output, efficiency, development trend temporally and spatially. Additionally, the traditional perspective of influencing mechanism analysis was expanded to include spatial heterogeneity. The influencing strength and distance were quantitatively revealed at pixel resolution. The results indicate that (1) the SCESs in Minnesota were generally improved from 1998 to 2018. (2) 70% and 78% of the counties experienced low-output and low-efficiency problems, respectively. (3) The Twin Cities constituted the degradation cores of SCESs in Minnesota. (4) The cropland and forestland proportions and rainfall were the top three influencing factors, causing SCES changes of 0.9959%, 0.4937%, and 0.1536%, respectively, for every 1% change in these factors. (5) According to the influence scopes, the soil organic carbon (SOC) content, normalized difference vegetation index (NDVI), and soil silt content were classified as long-distance factors, the soil sand content, slope and soil clay content were categorized as moderate-distance factors, and the remaining variables were identified as short-distance factors. (6) The influencing factors imposed both positive and negative influences on the SCESs, and attenuation or alteration of the adverse influences on SCESs through specific human interventions is a very promising approach. • A multidimensional assessment framework was proposed. • The SCESs in Minnesota has been severe with an upward development. • Degradations were classified into five types at county level and concentrated at "Twin Cities". • The proportions of cropland, forestland, and rainfall were the strongest influencing strength factors. • The influencing factors are classified as long-distance, moderate-distance, and short-distance factors. [ABSTRACT FROM AUTHOR]

Surface depressions are abundant in topographically complex landscapes, and they exert significant influences on hydrological, ecological, and biogeochemical processes at local and regional scales. The increasing availability of high-resolution topographical data makes it possible to resolve small surface depressions. By analogy with the reasoning process of a human interpreter to visually recognize surface depressions from a topographic map, we developed a localized contour tree method that is able to fully exploit high-resolution topographical data for detecting, delineating, and characterizing surface depressions across scales with a multitude of geometric and topological properties. In this research, we introduce a new concept ‘pour contour’ and a graph theory-based contour tree representation for the first time to tackle the surface depression detection and delineation problem. Beyond the depression detection and filling addressed in the previous raster-based methods, our localized contour tree method derives the location, perimeter, surface area, depth, spill elevation, storage volume, shape index, and other geometric properties for all individual surface depressions, as well as the nested topological structures for complex surface depressions. The combination of various geometric properties and nested topological descriptions provides comprehensive and essential information about surface depressions across scales for various environmental applications, such as fine-scale ecohydrological modeling, limnological analyses, and wetland studies. Our application example demonstrated that our localized contour tree method is functionally effective and computationally efficient. [ABSTRACT FROM PUBLISHER]

Non-floodplain wetlands – those located outside the floodplains – have emerged as integral components to watershed resilience, contributing hydrologic and biogeochemical functions affecting watershed-scale flooding extent, drought magnitude, and water-quality maintenance. However, the absence of a global dataset of non-floodplain wetlands limits their necessary incorporation into water quality and quantity management decisions and affects wetland-focused wildlife habitat conservation outcomes. We addressed this critical need by developing a publicly available "Global NFW" (Non-Floodplain Wetland) dataset, comprised of a global river–floodplain map at 90 m resolution coupled with a global ensemble wetland map incorporating multiple wetland-focused data layers. The floodplain, wetland, and non-floodplain wetland spatial data developed here were successfully validated within 21 large and heterogenous basins across the conterminous United States. We identified nearly 33 million potential non-floodplain wetlands with an estimated global extent of over 16×106 km 2. Non-floodplain wetland pixels comprised 53 % of globally identified wetland pixels, meaning the majority of the globe's wetlands likely occur external to river floodplains and coastal habitats. The identified global NFWs were typically small (median 0.039 km 2), with a global median size ranging from 0.018–0.138 km 2. This novel geospatial Global NFW static dataset advances wetland conservation and resource-management goals while providing a foundation for global non-floodplain wetland functional assessments, facilitating non-floodplain wetland inclusion in hydrological, biogeochemical, and biological model development. The data are freely available through the United States Environmental Protection Agency's Environmental Dataset Gateway (https://gaftp.epa.gov/EPADataCommons/ORD/Global%5fNonFloodplain%5fWetlands/ , last access: 24 May 2023) and through 10.23719/1528331 (Lane et al., 2023a). [ABSTRACT FROM AUTHOR]

Detailed and precise information on urban building patterns is essential for urban design, landscape evaluation, social analyses and urban environmental studies. Although a broad range of studies on the extraction of urban building patterns has been conducted, few studies simultaneously considered the spatial proximity relations and morphological properties at a building-unit level. In this study, we present a simple and novel graph-theoretic approach, Extended Minimum Spanning Tree (EMST), to describe and characterize local building patterns at building-unit level for large urban areas. Building objects with abundant two-dimensional and three-dimensional building characteristics are first delineated and derived from building footprint data and high-resolution Light Detection and Ranging data. Then, we propose the EMST approach to represent and describe both the spatial proximity relations and building characteristics. Furthermore, the EMST groups the building objects into different locally connected subsets by applying the Gestalt theory-based graph partition method. Based on the graph partition results, our EMST method then assesses the characteristics of each building to discover local patterns by employing the spatial autocorrelation analysis and homogeneity index. We apply the proposed method to the Staten Island in New York City and successfully extracted and differentiated various local building patterns in the study area. The results demonstrate that the EMST is an effective data structure for understanding local building patterns from both geographic and perceptual perspectives. Our method holds great potential for identifying local urban patterns and provides comprehensive and essential information for urban planning and management. [ABSTRACT FROM PUBLISHER]

Wetland ecosystems straddle both terrestrial and aquatic habitats, performing many ecological functions directly and indirectly benefitting humans. However, global wetland losses are substantial. Satellite remote sensing and classification informs wise wetland management and monitoring. Both pixel- and object-based classification approaches using parametric and non-parametric algorithms may be effectively used in describing wetland structure and habitat, but which approach should one select? We conducted both pixel- and object-based image analyses (OBIA) using parametric (Iterative Self-Organizing Data Analysis Technique, ISODATA, and maximum likelihood, ML) and non-parametric (random forest, RF) approaches in the Barguzin Valley, a large wetland (~500 km2) in the Lake Baikal, Russia, drainage basin. Four Quickbird multispectral bands plus various spatial and spectral metrics (e.g., texture, Non-Differentiated Vegetation Index, slope, aspect, etc.) were analyzed using field-based regions of interest sampled to characterize an initial 18 ISODATA-based classes. Parsimoniously using a three-layer stack (Quickbird band 3, water ratio index (WRI), and mean texture) in the analyses resulted in the highest accuracy, 87.9% with pixel-based RF, followed by OBIA RF (segmentation scale 5, 84.6% overall accuracy), followed by pixel-based ML (83.9% overall accuracy). Increasing the predictors from three to five by adding Quickbird bands 2 and 4 decreased the pixel-based overall accuracy while increasing the OBIA RF accuracy to 90.4%. However, McNemar's chi-square test confirmed no statistically significant difference in overall accuracy among the classifiers (pixel-based ML, RF, or object-based RF) for either the three- or five-layer analyses. Although potentially useful in some circumstances, the OBIA approach requires substantial resources and user input (such as segmentation scale selection—which was found to substantially affect overall accuracy). Hence, we conclude that pixel-based RF approaches are likely satisfactory for classifying wetland-dominated landscapes. [ABSTRACT FROM AUTHOR]

The brightness of nighttime lights (NTL) has been proven to be strongly related to population density and thus has been widely used for population estimation from national to county levels. However, the limited availability of fine-grained census data makes an accurate assessment of the pixel-level relationship between NTL intensity and population a challenge. Consequently, pixel-level population estimation bias based on NTL intensity has been rarely investigated. Using fine-grained census data, we quantitively evaluated the correlation between urban population and NTL intensity over the core areas in Shanghai city, China. We also proposed a simple index called building volume adjusted nighttime light index (BVANI) for better characterizition of the relationship between urban population and NTL intensity. Our results found that pixel-level NTL intensity has a minimal correlation with population and the exclusive use of NTL intensity will not improve our ability to model population. While the assessment of BVANI shows that BVANI has an inverse relationship with pupation and the relationship between BVANI and population follows a power-law distribution. The relationship strength with population can be significantly improved by using BVANI with a correlation coefficient of 0.60. We believe that BVANI can be used as an important modeling factor for mapping fine-scale urban populations. • NTL intensity has a minimal correlation with population at pixel level. • An index called building volume adjusted nighttime light index (BVANI) was proposed. • The relationship between BVANI and population follows a power-law distribution. • BVANI reduces pixel-level population estimation biases significantly. [ABSTRACT FROM AUTHOR]

Individual tree crown delineation is of great importance for forest inventory and management. The increasing availability of high-resolution airborne light detection and ranging (LiDAR) data makes it possible to delineate the crown structure of individual trees and deduce their geometric properties with high accuracy. In this study, we developed an automated segmentation method that is able to fully utilize high-resolution LiDAR data for detecting, extracting, and characterizing individual tree crowns with a multitude of geometric and topological properties. The proposed approach captures topological structure of forest and quantifies topological relationships of tree crowns by using a graph theory-based localized contour tree method, and finally segments individual tree crowns by analogy of recognizing hills from a topographic map. This approach consists of five key technical components: (1) derivation of canopy height model from airborne LiDAR data; (2) generation of contours based on the canopy height model; (3) extraction of hierarchical structures of tree crowns using the localized contour tree method; (4) delineation of individual tree crowns by segmenting hierarchical crown structure; and (5) calculation of geometric and topological properties of individual trees. We applied our new method to the Medicine Bow National Forest in the southwest of Laramie, Wyoming and the HJ Andrews Experimental Forest in the central portion of the Cascade Range of Oregon, U.S. The results reveal that the overall accuracy of individual tree crown delineation for the two study areas achieved 94.21% and 75.07%, respectively. Our method holds great potential for segmenting individual tree crowns under various forest conditions. Furthermore, the geometric and topological attributes derived from our method provide comprehensive and essential information for forest management. [ABSTRACT FROM AUTHOR]

Watershed resilience is the ability of a watershed to maintain its characteristic system state while concurrently resisting, adapting to, and reorganizing after hydrological (for example, drought, flooding) or biogeochemical (for example, excessive nutrient) disturbances. Vulnerable waters include non-floodplain wetlands and headwater streams, abundant watershed components representing the most distal extent of the freshwater aquatic network. Vulnerable waters are hydrologically dynamic and biogeochemically reactive aquatic systems, storing, processing, and releasing water and entrained (that is, dissolved and particulate) materials along expanding and contracting aquatic networks. The hydrological and biogeochemical functions emerging from these processes affect the magnitude, frequency, timing, duration, storage, and rate of change of material and energy fluxes among watershed components and to downstream waters, thereby maintaining watershed states and imparting watershed resilience. We present here a conceptual framework for understanding how vulnerable waters confer watershed resilience. We demonstrate how individual and cumulative vulnerable-water modifications (for example, reduced extent, altered connectivity) affect watershed-scale hydrological and biogeochemical disturbance response and recovery, which decreases watershed resilience and can trigger transitions across thresholds to alternative watershed states (for example, states conducive to increased flood frequency or nutrient concentrations). We subsequently describe how resilient watersheds require spatial heterogeneity and temporal variability in hydrological and biogeochemical interactions between terrestrial systems and down-gradient waters, which necessitates attention to the conservation and restoration of vulnerable waters and their downstream connectivity gradients. To conclude, we provide actionable principles for resilient watersheds and articulate research needs to further watershed resilience science and vulnerable-water management. [ABSTRACT FROM AUTHOR]

Remote sensing (RS) plays an important role gathering data in many critical domains (e.g., global climate change, risk assessment and vulnerability reduction of natural hazards, resilience of ecosystems, and urban planning). Retrieving, managing, and analyzing large amounts of RS imagery poses substantial challenges. Google Earth Engine (GEE) provides a scalable, cloud-based, geospatial retrieval and processing platform. GEE also provides access to the vast majority of freely available, public, multi-temporal RS data and offers free cloud-based computational power for geospatial data analysis. Artificial intelligence (AI) methods are a critical enabling technology to automating the interpretation of RS imagery, particularly on object-based domains, so the integration of AI methods into GEE represents a promising path towards operationalizing automated RS-based monitoring programs. In this article, we provide a systematic review of relevant literature to identify recent research that incorporates AI methods in GEE. We then discuss some of the major challenges of integrating GEE and AI and identify several priorities for future research. We developed an interactive web application designed to allow readers to intuitively and dynamically review the publications included in this literature review. [ABSTRACT FROM AUTHOR]

Water features (e.g., water quantity and water quality) are one of the most important environmental factors essential to improving climate-change resilience. Remote sensing (RS) technologies empowered by artificial intelligence (AI) have become one of the most demanded strategies to automating water information extraction and thus intelligent monitoring. In this article, we provide a systematic review of the literature that incorporates artificial intelligence and computer vision methods in the water resources sector with a focus on intelligent water body extraction and water quality detection and monitoring through remote sensing. Based on this review, the main challenges of leveraging AI and RS for intelligent water information extraction are discussed, and research priorities are identified. An interactive web application designed to allow readers to intuitively and dynamically review the relevant literature was also developed. [ABSTRACT FROM AUTHOR]

Freshwater is becoming scarce worldwide with the rapidly growing population, developing industries, burgeoning agriculture, and increasing consumption. Assessment of ecosystem services has been regarded as a promising way to reconcile the increasing demand and depleting natural resources. In this paper, we proposed a multidimensional assessment framework for evaluating water provisioning ecosystem services by integrating multi-source remote sensing products. We applied the multidimensional framework to assess lake water ecosystem services in the state of Minnesota, US. We found that: (1) the water provisioning ecosystem services degraded during 1998–2018 from three assessment perspectives; (2) the output, efficiency, and trend indices have stable distribution and various spatial clustering patterns from 1998 to 2018; (3) high-level efficiency depends on high-level output, and low-level output relates to low-level efficiency; (4) Western Minnesota, including Northwest, West Central, and Southwest, degraded more severely than other zones in water provisioning services; (5) human activities impact water provisioning services in Minnesota more than climate changes. These findings can benefit policymakers by identifying the priorities for better protection, conservation, and restoration of lake ecosystems. Our multidimensional assessment framework can be adapted to evaluate ecosystem services in other regions. [ABSTRACT FROM AUTHOR]

Night-Time light (NTL) data have been widely used for monitoring the dynamics of human activities and socioeconomics. As a new-generation satellite for acquiring NTL data, the National Oceanic and Atmospheric Administration-20 (NOAA-20) was successfully launched in November 2017. To support broad-scale environmental applications, it is necessary to generate monthly NTL composites of NOAA-20. Taking China as the study area, we produced NOAA-20 monthly NTL composites from April 2019 to December 2019. First, we performed a series of de-noising steps to eliminate NOAA-20 NTL pixels affected by sunlight, moonlight, high scan angles, clouds and further eliminated NTL outliers in time series by using a modified z-score method. Then, we aggregated daily NOAA-20 NTL data to produce monthly NTL composites to improve data coverage and stability. Subsequently, we examined the consistency of monthly composites between NOAA-20 and S-NPP at multiple scales, including provincial, prefectural, and pixel levels. Our results show that the monthly NTL composites of NOAA-20 are in good agreement with that of S-NPP with an R2 ranging from 0.81 to 0.99. Besides, we found that the spatial variation trends of the NOAA-20 monthly NTL composites are similar to that of S-NPP monthly NTL composites. We believe that the monthly NTL composites of NOAA-20 are very close to that of S-NPP and will open up more opportunities for relevant NTL studies. [ABSTRACT FROM AUTHOR]

Fractures are important structural features that affect the stress condition and stability of ice shelves. Previous studies have mainly focused on the measurement of fractures in the horizontal dimension. However, the vertical morphology of fractures could also be potentially important in determining their evolution and role in ice shelf stability. In this regard, the dense and high-resolution surface elevation measurements collected by the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) provide an excellent opportunity for studying fractures in the vertical dimension over a regional scale. Here we developed an object-oriented algorithm to automatically detect and characterize fracture features (including rifts, surface fractures, and the surface expressions of basal fractures) from ICESat-2 data. We successfully applied the algorithm to ICESat-2 L3A Land Ice Height (ATL06) data over the Amery Ice Shelf in East Antarctica, and retrieved the vertical structural information of two major fracture fields. The detected fracture features match well with the visible fracture lines in Landsat-8 satellite imagery, and the edges of fracture features are well-captured from the ATL06 data. We analyzed the fracture patterns in terms of surface depth, width, vertical shape, length, and orientation. We found that the fracture feature depth is a key factor determining the formation of new fractures and the spatial pattern of depth is closely related to rift formation. We anticipate that further application of the object-oriented method over other ice shelves will produce important insights regarding fracture formation and ice shelf vulnerability by combining ICESat-2-derived vertical information with the horizontal structure retrieved from satellite imagery. • ICESat-2 data are used to map and characterize the fractures on Amery Ice Shelf. • An automated object-oriented algorithm is developed to detect fracture features. • Fracture feature depth is critical for fracture evolution and rift formation. [ABSTRACT FROM AUTHOR]

Characterizing landscape patterns and revealing their underlying processes are critical for studying climate change and environmental problems. Previous methods for mapping land cover changes largely focused on the classification of remote sensing images. Therefore, they could not provide information about the evolutionary process of land cover changes. In this paper, we developed a spatiotemporal structural graph (STSG) technique for a comprehensive analysis of land cover changes. First, a land cover neighborhood graph was generated for each snapshot to quantify the spatial relationship between adjacent land cover objects. Then, an object-based temporal tracking algorithm was designed to monitor the temporal changes between land cover objects over time. Finally, land cover evolutionary trajectories, pixel-level land cover change trajectories, and node-wise connectivity changes over time were characterized. We applied the proposed method to analyze land cover changes in Suffolk County, New York from 1996 to 2010. The results demonstrated that STSG can not only characterize and visualize detailed land cover changes spatially but also maintain the temporal sequence and relations of land cover objects in an integrated space-time environment. The proposed STSG provides a useful framework for analyzing land cover changes and can be adapted to characterize and quantify other spatiotemporal phenomena. [ABSTRACT FROM AUTHOR]

• The confluence process of river overflow during floods participates in the eco-hydrological simulation. • Using actual river length, bend radius equivalent and overflow data helps improve the steppe river confluence process. • Precipitation, ecological status, and climate change significantly interact with OFN, and the overflow response to climate has a 2-year lag. Technology has greatly promoted ecohydrological model development, but runoff generation and confluence simulations have fallen behind in ecohydrological model development due to limited innovations. To fully understand ecohydrological processes and accurately describe the coupling between ecological and hydrological processes, a distributed ecohydrological model was constructed by integrating multisource information into distributed dynamic process model (DDPM). We mainly describe runoff generation and convergence modules. Based on the improved HBV model and degree-3 h factor method, runoff generation and snow routines were constructed for semiarid grassland basins. In view of meandering and variable steppe river channels and steep hydrological relief characteristics, a confluence module was constructed; the 1-km bend radius equivalent concept was innovatively proposed to unify river channel bend degrees. The daily runoff simulation validation results obtained using two datasets were R 2 = 0.947 and 0.932, NSE = 0.945 and 0.905, KGE = 0.971 and 0.680. In the 3-hour flood simulations, the DDPM could better restore small long-distance water flows than the confluence method that did not consider actual river lengths or bend energy losses; the DDPM more accurately simulated the flood peak arrival time than the confluence method that did not consider overflow. The simulated mainstream overflow frequency increased by 0.84/10 years, and significant interaction periods of 10 to 13 years occurred with local precipitation, ecological status, and global climate change. An approximately 2-year lag occurred in the global climate change response. This study helps us further understand and reveal the ecohydrological processes of steppe rivers in semiarid regions. [ABSTRACT FROM AUTHOR]

With the increasing global population, human demands for natural resources continue to grow. There is a critical need for the sustainable use and development of natural resources. In this context, ecosystem services have attracted more and more attention, and ecosystem services assessment has proven to be useful for guiding research, policy formulation, and management implementation. In this paper, we attempted to assess ecosystem services more comprehensively from various perspectives. We used food provisioning ecosystem services in Minnesota as a case study and proposed two new concepts for assessing ecosystem services: efficiency and trend. We designed a multidimensional assessment framework, analyzed the total output, efficiency, and trend temporally based on both area and space with Exploratory Spatial Data Analysis (ESDA). We also identified major influencing factors based on remote sensing images in Google Earth Engine and explored the quantitative influence on each assessment dimension. We found that: (1) Food provisioning ecosystem service in Minnesota has generally been improving from 1998 to 2018. (2) We identified food provisioning ecosystem services in Minnesota as superior zones, mixed zones, and inferior zones with a 'sandwich geo-configuration'. (3) The total output tends to be stable while the efficiency is disturbed by some natural disasters. Simultaneously, the trend index has been improving with slight fluctuations. (4) Agricultural disaster financial support has a stronger impact on stabilizing the total output of food provisioning than the other two dimensions. (5) Soil moisture, diurnal temperature difference, and crop growth are the three main influencing aspects of food provisioning ecosystem services, and the order of the influential density is: the Perpendicular Drought Index (PDI), Normalized Difference Vegetation Index (NDVI), Rainfall (RF), Daytime Temperature (DT), and Diurnal Temperature Difference (DIF). [ABSTRACT FROM AUTHOR]

The information of building types is highly needed for urban planning and management, especially in high resolution building modeling in which buildings are the basic spatial unit. However, in many parts of the world, this information is still missing. In this paper, we proposed a framework to derive the information of building type using geospatial data, including point-of-interest (POI) data, building footprints, land use polygons, and roads, from Gaode and Baidu Maps. First, we used natural language processing (NLP)-based approaches (i.e., text similarity measurement and topic modeling) to automatically reclassify POI categories into which can be used to directly infer building types. Second, based on the relationship between building footprints and POIs, we identified building types using two indicators of type ratio and area ratio. The proposed framework was tested using over 440,000 building footprints in Beijing, China. Our NLP-based approaches and building type identification methods show overall accuracies of 89.0% and 78.2%, and kappa coefficient of 0.83 and 0.71, respectively. The proposed framework is transferrable to other China cities for deriving the information of building types from web mapping platforms. The data products generated from this study are of great use for quantitative urban studies at the building level. [ABSTRACT FROM AUTHOR]

Urban hierarchies are closely related to economic growth, urban planning and sustainable urban development. Due to the limited availability of reliable statistical data at fine scales, most existing studies on urban hierarchy characterization failed to capture the detailed urban spatial structure information. Previous studies have demonstrated that night time light data are correlated with many urban socio-economic indicators and hence can be used to characterize urban hierarchies. This paper presents a novel method for studying urban hierarchies from night time light data. Night time light data were first conceptualized as continuous mathematical surfaces, termed night time light surfaces. From the morphology of these surfaces the corresponding surface networks were derived. Hereafter, a night time light intensity (NTLI) graph was defined to describe the morphology of the surface network. Then, structural similarity between the night time light surfaces of any two different cities was calculated via a threshold-based maximum common induced graph searching algorithm. Finally, urban hierarchies were defined on the basis of the structural similarities between different cities. Using the 2015 annual NPP-VIIRS night time light data, the urban hierarchies of 32 major cities in China were successfully examined. The results are highly consistent with the reference urban hierarchies. [ABSTRACT FROM AUTHOR]

Efforts are increasingly being made to classify the world’s wetland resources, an important ecosystem and habitat that is diminishing in abundance. There are multiple remote sensing classification methods, including a suite of nonparametric classifiers such as decision-tree (DT), rule-based (RB), and random forest (RF). High-resolution satellite imagery can provide more specificity to the classified end product, and ancillary data layers such as the Normalized Difference Vegetation Index, and hydrogeomorphic layers such as distance-to-a-stream can be coupled to improve overall accuracy (OA) in wetland studies. In this paper, we contrast three nonparametric machine-learning algorithms (DT, RB, and RF) using a large field-based dataset (n = 228) from the Selenga River Delta of Lake Baikal, Russia. We also explore the use of ancillary data layers selected to improve OA, with a goal of providing end users with a recommended classifier to use and the most parsimonious suite of input parameters for classifying wetland-dominated landscapes. Though all classifiers appeared suitable, the RF classification outperformed both the DT and RB methods, achieving OA >81%. Including a texture metric (homogeneity) substantially improved the classification OA. However, including vegetation/soil/water metrics (based on WorldView-2 band combinations), hydrogeomorphic data layers, and elevation data layers to increase the descriptive content of the input parameters surprisingly did not markedly improve the OA. We conclude that, in most cases, RF should be the classifier of choice. The potential exception to this recommendation is under the circumstance where the end user requires narrative rules to best manage his or her resource. Though not useful in this study, continuously increasing satellite imagery resolution and band availability suggests the inclusion of ancillary contextual data layers such as soil metrics or elevation data, the granularity of which may define its utility in subsequent wetland classifications. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is a key parameter of great interest in many remote sensing applications. However, no single satellite system can produce thermal infrared (TIR) images at both high spatial and temporal resolution to retrieve LST. Various algorithms have been developed to enhance the spatial or temporal resolution of TIR data in the past decades. Among them, the Spatio-temporal Adaptive Data Fusion Algorithm for Temperature mapping (SADFAT) model is one of the most widely used algorithms for fusing Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. To our knowledge, Visible Infrared Imaging Radiometer Suite (VIIRS) TIR data have not yet been used in thermal downscaling with Landsat-8 TIR data. This study aims to generate daily LST images at Landsat-8 resolution (100 m) by fusing VIIRS and Landsat-8 TIR data for the first time with the SADFAT algorithm. The results indicate that the prediction accuracy for the study area ranged from 1.1 K to 1.4 K, which suggests that VIIRS data can be used as a good alternative for MODIS data for generating daily LST images by fusing Landsat TIR data. [ABSTRACT FROM PUBLISHER]

Urban spatial structure affects many aspects of urban functions and has implications for accessibility, environmental sustainability, and public expenditures. During the urbanization process, a careful and efficient examination of the urban spatial structure is crucial. Different from the traditional approach that relies on population or employment census data, this research exploits the nighttime light (NTL) intensity of the earth surface recorded by satellite sensors. The NTL intensity is represented as a continuous mathematical surface of human activities, and the elemental features of urban structures are identified by analogy with earth’s topography. We use a topographical metaphor of a mount to identify an urban center or subcenter and the surface slope to indicate an urban land-use intensity gradient. An urban center can be defined as a continuous area with higher concentration or density of employments and human activities. We successfully identified 33 urban centers, delimited their corresponding boundaries, and determined their spatial relations for Shanghai metropolitan area, by developing a localized contour tree method. In addition, several useful properties of the urban centers have been derived, such as 9% of Shanghai administrative area has become urban centers. We believe that this method is applicable to other metropolitan regions at different spatial scales. [ABSTRACT FROM PUBLISHER]

This study utilizes ICESat Release 33 GLA14 data to analyse water level variation of Xinjiang’s lakes and reservoirs from 2003 to 2009. By using Landsat images, lakes and reservoirs with area larger than 1 km2 are numerically delineated with a software tool. Based on ICESat observations, we analyse the characteristics of water level variation in different geographic environments, as well as investigate the reasons for the variation. Results indicate that climatic warming contributes to rising water levels in lakes in mountainous areas, especially for lakes that are recharged by snow and glacial melting. For lakes in oases, the water levels are affected jointly by human activity and climate change, while the water levels of reservoirs are mainly affected by human activity. Comparing the annual average rates of water levels, those of lakes are higher than those of reservoirs in oasis areas. The main reasons for the decreasing water levels in desert regions are the reduction of recharged runoff and high evaporation. By analysing the variation of water levels and water volume in different geologic environments, it is found that water level and volume increased in mountainous regions, and decreased in oasis regions and desert regions. Finding also demonstrate that decreasing volume is greater than increasing volume, which results in decreasing total volume of Xinjiang lakes and reservoirs. [ABSTRACT FROM AUTHOR]

We compared 10 established and 2 new satellite reflectance algorithms for estimating chlorophyll- a (Chl- a ) in a temperate reservoir in southwest Ohio using coincident hyperspectral aircraft imagery and dense coincident surface observations collected within 1 h of image acquisition to develop simple proxies for algal blooms in water bodies sensitive to algal blooms (especially toxic or harmful algal blooms (HABs)) and to facilitate portability between multispectral satellite imagers for regional algal bloom monitoring. All algorithms were compared with narrow band hyperspectral aircraft images. These images were subsequently upscaled spectrally and spatially to simulate 5 current and near future satellite imaging systems. Established and new Chl- a algorithms were then applied to the synthetic satellite images and compared to coincident surface observations of Chl- a collected from 44 sites within 1 h of aircraft acquisition of the imagery. We found several promising algorithm/satellite imager combinations for routine Chl- a estimation in smaller inland water bodies with operational and near-future satellite systems. The CI, MCI, FLH, NDCI, 2BDA and 3 BDA Chl- a algorithms worked well with CASI imagery. The NDCI, 2BDA, and 3BDA Chl- a algorithms worked well with simulated WorldView-2 and 3, Sentinel-2, and MERIS-like imagery. NDCI was the most widely applicable Chl- a algorithm with good performance for CASI, WorldView 2 and 3, Sentinel-2 and MERIS-like imagery and limited performance with MODIS imagery. A new fluorescence line height “greenness” algorithm yielded the best Chl- a estimates with simulated Landsat-8 imagery. [ABSTRACT FROM AUTHOR]

Extracting ground surface from high-density point clouds collected by Mobile Laser Scanning (MLS) systems is of vital importance in urban planning and digital city mapping. This article proposes a novel approach for automated extraction of ground surface along urban roads from MLS point clouds. The approach, which was designed to handle both ordered and unordered MLS point clouds, consists of three key steps: constructing vertical profile from MLS point clouds along the vehicle trajectory; extracting candidate ground points using an adaptive alpha shapes algorithm; refining the candidate ground points with an elevation variance filter. To evaluate the performance of the proposed method, experiments were conducted using two types of urban street-scene point clouds. The results reveal that the ground points can be detected with an error rate of as low as 1.9%, proving that our proposed method offers a promising solution for automated extraction of ground surface from MLS point clouds. [ABSTRACT FROM PUBLISHER]

This article presents an object-based conceptual framework and numerical algorithms for representing and analyzing coastal morphological and volumetric changes based on repeat airborne light detection and ranging (LiDAR) surveys. This method identifies and delineates individual zones of erosion and deposition as discrete objects. The explicit object representation of erosion and deposition zones is consistent with the perception and cognition of human analysts and geomorphologists. The extracted objects provide ontological and epistemological foundation to localize, represent, and interpret erosion and deposition patches for better coastal resource management and erosion control. The discrete objects are much better information carriers than the grid cells in the field-based representation of source data. A set of spatial and volumetric attributes are derived to characterize and quantify location, area, shape, orientation, depth, volume, and other properties of erosion and deposition objects. Compared with the conventional cell-by-cell differencing approaches, our object-based method gives a concise and high-level representation of information and knowledge about coastal morphological dynamics. The derived attributes enable the discrimination of true morphological changes from artifacts caused by data noise and processing errors. Furthermore, the concise object representation of erosion and deposition zones facilitates overlay analysis in conjunction with other GIS data layers for understanding the causes and impacts of morphological and volumetric changes. We have implemented a software tool for our object-based morphological analysis, which will be freely available for the public. An example is used to demonstrate the utility and effectiveness of this new method. [ABSTRACT FROM AUTHOR]

Monitoring harmful algal blooms (HABs) in freshwater over regional scales has been implemented through mapping chlorophyll-a (Chl-a) concentrations using multi-sensor satellite remote sensing data. Cloud-free satellite measurements and a sufficient number of matched-up ground samples are critical for constructing a predictive model for Chl-a concentration. This paper presents a methodological framework for automatically pairing surface reflectance values from multi-sensor satellite observations with ground water quality samples in time and space to form match-up points, using the Google Earth Engine cloud computing platform. A support vector machine model was then trained using the match-up points, and the prediction accuracy of the model was evaluated and compared with traditional image processing results. This research demonstrates that the integration of multi-sensor satellite observations through Google Earth Engine enables accurate and fast Chl-a prediction at a large regional scale over multiple years. The challenges and limitations of using and calibrating multi-sensor satellite image data and current and potential solutions are discussed. [ABSTRACT FROM AUTHOR]

Community composition and activity of soil microbes are responsive to changes in physiochemical and biological attributes during forest succession. However, whether soil microbial community structure and activity in soil particle-size fractions under different restoration approaches (regeneration through secondary succession and afforestation) during a chronosequence ranging from 20 to 70 years following deforestation of natural forest is still unclear. 24 soil samples from the top 10 cm of soil profiles were collected for four stand ages of spruce plantations and natural birch forests from three different sites. Soil aggregates were fractionated with dry - sieving technique and soil microbial communities in soil particle - size fractions were characterized by PLFAs profiling and enzymatic activity assay. The effects of restoration approach, soil aggregate-size and stand age on soil microbial biomass were depended on microbial group identity. Differences in microbial biomass were correlated to soil organic carbon, total nitrogen and pH. Additionally, differences in soil organic carbon, total nitrogen, pH and C:N ratio across restoration approaches, soil aggregate-sizes and stand ages jointly regulated soil microbial community composition. Soil aggregate - size and stand age also exerted significant effects on the potential activities of β - 1, 4 - glucosidase (βG), β - 1, 4 - N - acetyl - glucosaminidase (NAG), acid phosphatase (ACP) and polyphenol oxidase (POX), while the effect of restoration approach on enzyme activities depended on the enzyme identity. Soil organic carbon, total nitrogen were major predictors of enzymatic activity in particle-size fractions. The carbon quality index (CQI = lnPOX / (ln β G + lnPOX)) rather than soil quality index (SQI = (β G*NAG*ACP*POX 4)) was a promising indicator of the response of soil microbial activity to restoration approach and stand age. In conclusion, the effects of restoration approach on soil microbial biomass and enzymatic activity vary depending on aggregate - size and stand age. Soil microbial biomass, community structure and enzymatic activity provide evidence that natural restoration enhancing soil quality in comparison with artificial restoration in the fragile subalpine forest ecosystem. • The effects of restoration approach on soil microbial biomass and enzymatic activity vary depending on aggregate - size and stand age. • Soil microbial community composition and activity in particle-size fractions was correlated to soil organic carbon and total nitrogen contents. • Carbon quality index was a promising indicator of the response of soil microbial activity to restoration approach and stand age. [ABSTRACT FROM AUTHOR]

• Parcel-level artificial surface features were linked to Luojia 1-01 nighttime light (NTL) imagery. • NTL variations among artificial surface features in Shanghai were explained using NPP-VIIRS data for comparison. • Luojia 1-01 had fewer "blooming" phenomena than NPP-VIIRS data. • Luojia 1-01 is more suitable for estimating socioeconomic activities at a finer scale than NPP-VIIRS data. Nighttime light (NTL) remote sensing data have been widely used to derive socioeconomic indices at national and regional scales. However, few studies analyzed the factors that may explain NTL variations at a fine scale due to the limited resolution of existing NTL data. As a new generation NTL satellite, Luojia 1-01 provides NTL data with a finer spatial resolution of ∼130 m and can be used to assess the relationship between NTL intensity and artificial surface features on an unprecedented scale. This study represents the first efforts to assess the relationship between Luojia 1-01 NTL intensity and artificial surface features at the parcel level in comparison to the Suomi National Polar-orbiting Partnership-Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data. Points-of-interest (POIs) and land-use/land-cover (LULC) data were used in random forest (RF) regression models for both Luojia 1-01 and NPP-VIIRS to analyze the feature contribution of artificial surface features to NTL intensity. The results show that luminosity variations in Luojia 1-01 data for different land-use types were more significant than those in NPP-VIIRS data because of the finer spatial resolution and wider measurement range. Seventeen variables extracted from POI and LULC data explained the Luojia 1-01 and NPP-VIIRS NTL intensity, with a good out-of-bag score of 0.62 and 0.76, respectively. Moreover, Luojia 1-01 data had fewer "blooming" phenomena than NPP-VIIRS data, especially for cropland, water body, and rural area. Luojia 1-01 is more suitable for estimating socioeconomic activities and can attain more comprehensive information on human activities, since the feature contribution of POI variables is more sensitive to NTL intensity in the Luojia 1-01 RF regression model than that in the NPP-VIIRS RF regression model. [ABSTRACT FROM AUTHOR]

To understand the timing, extent, and magnitude of land use/land cover (LULC) change in buffer areas surrounding Midwestern US waters, we analyzed the full imagery archive (1982–2017) of three Landsat footprints covering ~100,000 km2. The study area included urbanizing Chicago, Illinois and St. Louis, Missouri regions and agriculturally dominated landscapes (i.e., Peoria, Illinois). The Continuous Change Detection and Classification algorithm identified 1993–2017 LULC change across three Landsat footprints and in 90 m buffers for ~110,000 surface waters; waters were also size-binned into five groups for buffer LULC change analyses. Importantly, buffer-area LULC change magnitude was frequently much greater than footprint-level change. Surface-water extent in buffers increased by 14–35x the footprint rate and forest decreased by 2–9x. Development in buffering areas increased by 2–4x the footprint-rate in Chicago and Peoria area footprints but was similar to the change rate in the St. Louis area footprint. The LULC buffer-area change varied in waterbody size, with the greatest change typically occurring in the smallest waters (e.g., <0.1 ha). These novel analyses suggest that surface-water buffer LULC change is occurring more rapidly than footprint-level change, likely modifying the hydrology, water quality, and biotic integrity of existing water resources, as well as potentially affecting down-gradient, watershed-scale storages and flows of water, solutes, and particulate matter. [ABSTRACT FROM AUTHOR]

Satellite time-series data are bolstering global change research, but their use to elucidate land changes and vegetation dynamics is sensitive to algorithmic choices. Different algorithms often give inconsistent or sometimes conflicting interpretations of the same data. This lack of consensus has adverse implications and can be mitigated via ensemble modeling, an algorithmic paradigm that combines many competing models rather than choosing only a single "best" model. Here we report one such time-series decomposition algorithm for deriving nonlinear ecosystem dynamics across multiple timescales—A Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST). As an ensemble algorithm, BEAST quantifies the relative usefulness of individual decomposition models, leveraging all the models via Bayesian model averaging. We tested it upon simulated, Landsat, and MODIS data. BEAST detected changepoints, seasonality, and trends in the data reliably; it derived realistic nonlinear trends and credible uncertainty measures (e.g., occurrence probability of changepoints over time)—some information difficult to derive by conventional single-best-model algorithms but critical for interpretation of ecosystem dynamics and detection of low-magnitude disturbances. The combination of many models enabled BEAST to alleviate model misspecification, address algorithmic uncertainty, and reduce overfitting. BEAST is generically applicable to time-series data of all kinds. It offers a new analytical option for robust changepoint detection and nonlinear trend analysis and will help exploit environmental time-series data for probing patterns and drivers of ecosystem dynamics. Unlabelled Image • Deriving land dynamics from satellite time series is sensitive to algorithmic choice. • Satellite-derived ecosystem dynamics improved by embracing algorithmic uncertainty. • A Bayesian model averaging time-series decomposition algorithm (BEAST) developed. • BEAST is a generic tool to detect changepoint, trend, and seasonality in time series. • BEAST is available as a MATLAB library and an R package called "Rbeast". [ABSTRACT FROM AUTHOR]