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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]

[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]

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]

• A grid scheme with buffer was proposed to estimate solar radiance at building-scale. • A four-quadrant based segmentation method was designed to distinguish rooftop type. • Two scenarios were set up to assess rooftop's solar energy utilization potential. • A successful application in Shanghai revealed the details of solar energy potential. The assessment of potential and utilization of solar energy for each building has become an essential precondition of urban sustainable development. Currently, there are two challenges, namely high computation cost for an assessment within a large city and missing rooftop information for each building. In this study, by taking the downtown area in Shanghai as sample, a grid scheme with buffer was proposed to alleviate the computational burden and successfully estimate solar radiation with an accuracy of 0.91. Also, a quadrant-based segmentation method was developed to classify rooftop types with an overall accuracy of 83.63%, which helps better calculate solar energy utilization cost. Finally, we assumed PhotoVoltaic conversion (PV) systems or Domestic Solar Water Heater (DSWH) systems are installed on all suitable rooftops and analyzed their utilization cost, as well as economic and ecological benefits. The results show that 165,529 and 52,131 buildings are suitable for PV and DSWH systems, respectively. PV system's average payback time is about five years with reducing standard coal consumption of 1.85 × 1011kg. The DSWH system requires three more years to balance the cost and benefits. The criteria of further solar energy planning are also concluded from three perspectives: building height and density, and rooftop structure. [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]

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]

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]

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]

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]