Explore the vast range of titles available.

Spatial land cover depictions are essential for ecological and environmental management. The thematic resolution of land cover and vegetation maps is also a significant factor affecting the ability to effectively develop policy and land management decisions based on spatial data. Natural resource and conservation planners often seek to develop strategies at broad scales; however, high-quality spatial data depicting current vegetation and ecosystem types over large areas are often unavailable. Since widely available land cover and vegetation datasets are generally lacking in either thematic resolution or spatial coverage, there is a need to integrate modeling approaches and ancillary data with traditional satellite image classifications to produce more detailed ecosystem maps for large areas. In this study, we present a comprehensive approach using satellite imagery, machine learning, and ancillary modeling approaches to develop high-resolution ecological system type maps statewide for Arkansas, USA. A RandomForest land cover classification of Sentinel-2 imagery was generated and further articulated into ecological types using a comprehensive set of secondary modeling approaches. A total of 123 types were mapped in Arkansas, including common cultural and ruderal land cover and vegetation such as pine plantations and developed types. Ozark–Ouachita Dry–Mesic Forest covered the most area, 17.51% of the state. Row Crops covered 17.16%. Twenty-five pine or pine plantation types covered 19.73% of the state, with Ozark–Ouachita pine woodland or mature pine plantation covering 6.15%. Field survey points were used to assess the quality of the mapped ecological systems. The approaches presented here provide a framework for finer resolution mapping of ecological systems at broad scales in other regions.

Vegetation phenology plays a key role in terrestrial ecosystem nutrient and carbon cycles and is sensitive to global climate change. Compared with spring phenology, which has been well studied, autumn phenology is still poorly understood. In this study, we estimated the date of the end of the growing season (EOS) across the Greater Khingan Mountains, China, from 1982 to 2015 based on the Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index third-generation (NDVI3g) dataset. The temporal correlations between EOS and climatic factors (e.g., preseason temperature, preseason precipitation), as well as the correlation between autumn and spring phenology, were investigated using partial correlation analysis. Results showed that more than 94% of the pixels in the Greater Khingan Mountains exhibited a delayed EOS trend, with an average rate of 0.23 days/y. Increased preseason temperature resulted in earlier EOS in most of our study area, except for the semi-arid grassland region in the south, where preseason warming generally delayed EOS. Similarly, EOS in most of the mountain deciduous coniferous forest, forest grassland, and mountain grassland forest regions was earlier associated with increased preseason precipitation, but for the semi-arid grassland region, increased precipitation during the preseason mainly led to delayed EOS. However, the effect of preseason precipitation on EOS in most of the Greater Khingan Mountains was stronger than that of preseason temperature. In addition to the climatic effects on EOS, we also found an influence of spring phenology on EOS. An earlier SOS led to a delayed EOS in most of the study area, while in the southern of mountain deciduous coniferous forest region and northern of semi-arid grassland region, an earlier SOS was often followed by an earlier EOS. These findings suggest that both climatic factors and spring phenology should be incorporated into autumn phenology models in order to improve prediction accuracy under present and future climate change scenarios.

Using Planetscope imagery, we trained a random forest model to detect Callery pear (Pyrus calleryana) throughout a diverse urban landscape in Columbia, Missouri. The random forest model had a classification accuracy of 89.78%, a recall score of 0.693, and an F1 score of 0.819. The key hyperparameters for model tuning were the cutoff and class–weight parameters. After the distribution of Callery pear was predicted throughout the landscape, we analyzed the distribution pattern of the predictions using Ripley’s K and then associated the distribution patterns with various socio-economic indicators. The analysis identified significant relationships between the distribution of the predicted Callery pear and population density, median household income, median year the housing infrastructure was built, and median housing value at a variety of spatial scales. The findings from this study provide a much-needed method for detecting species of interest in a heterogenous landscape that is both low cost and does not require specialized hardware or software like some alternative deep learning methods.

The Indian Ocean Tsunami of December 26, 2004 devastated coastal ecosystems across South Asia. Along the coastal regions of South India, increased groundwater levels (GWL), largely caused by saltwater intrusion, infiltration from inundated land, and disturbance of freshwater lenses, were reported. Many agencies allocated funding for restoration and rehabilitation projects. However, to streamline funding allocation efforts, district-level groundwater inundation/recession data would have been a useful tool for planners. Thus, to ensure better preparedness for future disaster relief operations, it is crucial to quantify pre- and post-tsunami groundwater levels across coastal districts in India. Since regional scale GWL field observations are not often available, this study instead used space gravimetry data from NASA’s Gravity Recovery and Climate Experiment (GRACE), along with soil moisture data from the Global Land Data Assimilation Systems (GLDAS), to quantify GWL fluctuations caused by the tsunami. A time-series analysis of equivalent groundwater thickness was developed for February 2004–December 2005 and the results indicated a net increase of 274 % in GWLs along coastal regions in Tamil Nadu following the tsunami. The net recharge volume of groundwater due to the tsunami was 16.8 km 3 , just 15 % lower than the total annual groundwater recharge (19.8 km 3 ) for the state of Tamil Nadu. Additionally, GWLs returned to average within 3 months following the tsunami. The analysis demonstrated the utility of remotely sensed data in predicting and assessing the impacts of natural disasters.

Climate change and human activities are important factors driving changes in wetland ecosystems. It is therefore crucial to quantitatively characterize the relative importance of these stressors in wetlands. Previous such analyses have generally not distinguished between wetland types, or have focused on individual wetland types. In this study, three representative wetland areas of the upper, middle and lower reaches of the Heilongjiang River Basin (HRB) were selected as the study area. An object-based classification was used with Landsat TM data to extract the spatial distribution of wetland in 1990, 2000 and 2010. We then quantified the relative importance of climate change and human activities on the wetlands by using the R package “relaimpo” package. The results indicated that: (1) the effects of human activities on wetland changes were greater (contribution rate of 63.57%) than climate change in the HRB. Specifically, there were differences in the relative importance of climate change and human activities for wetlands in different regions. Wetlands of the upper reaches were more affected by climate change, while wetlands in the middle and lower reaches were more affected by human activities; (2) climate change had a greater impact (contribution rate of 65.72%) on low intensity wetland loss, while human activities had a greater impact on moderate and severe intensity wetland loss, with respective contribution rates of 57.22% and 70.35%; (3) climate change had a larger effect on the shrub and forested wetland changes, with respective contribution rates of 58.33% and 52.58%. However, human activities had a larger effect on herbaceous wetland changes, with a contribution rate of 72.28%. Our study provides a useful framework for wetland assessment and management, and could be a useful tool for developing wetland utilization and protection approaches, particularly in sensitive environments in mid- and high-latitude areas.

Human activities in a transborder watershed are complex under the influence of domestic policies, international relations, and global events. Understanding the forces driving human activity change is important for the development of transborder watershed. In this study, we used global historical land cover data, the hemeroby index model, and synthesized major historical events to analyze how human activity intensity changed in the Heilongjiang River (Amur River in Russia) watershed (HLRW). The results showed that there was a strong spatial heterogeneity in the variation of human activity intensity in the HLRW over the past century (1900—2016). On the Chinese side, the human activity intensity change shifted from the plain areas for agricultural reclamation to the mountainous areas for timber extraction. On the Russian side, human activity intensity changes mostly concentrated along the Trans-Siberian Railway and the Baikal-Amur Mainline. Localized variation of human activity intensity tended to respond to regional events while regionalized variation tends to reflect national policy change or broad international events. The similarities and differences between China and Russia in policies and positions in international events resulted in synchronous and asynchronous changes in human activity intensity. Meanwhile, policy shifts were often confined by the natural features of the watershed. These results reveal the historical origins and fundamental connotations of watershed development and contribute to formulating regional management policies that coordinate population, economic, social, and environmental activities.

We mapped 22 soil ecogroups and 66 ecological mapping systems (EMSs), essentially current vegetation, for eight coastal counties in South Texas, from Refugio and Aransas County south to the Mexican border. We used supervised classification to extract land cover from 10-m resolution Sentinel-2 satellite imagery. We accessed Lidar point cloud information to derive vegetation height and then use it to distinguish among herbaceous, shrubland, and woodland-forest EMS types. We derived elevation and potential ponding information from Lidar. We created image objects (uniform polygons), from Sentinel-2 data, and attributed land cover, soil ecogroup, vegetation height, elevation, and ponding potential to the objects. We used those attributes of image objects to model EMS types. We completed heads-up modification of both land cover and EMS modeling results by using aerial photograph interpretation to improve results. The agreement between EMS-mapped type and 643 field-collected plots was >75%. The most abundant EMS types included Coastal and Sandsheet: Deep Sand Grassland (10.7% of the region); Native Invasive: Mesquite-Mixed Shrubland (5.0%); Gulf Coast: Coastal Prairie (4.6%); and South Texas: Sandy Mesquite Savanna Grassland (4.4%). The EMS results are nine times better resolution than currently available maps, and use of height to identify EMS type by distinguishing among herbaceous, shrubland, and woodland types enhanced mapping beyond what has been previously accomplished. The new EMS dataset will facilitate analysis of the distribution of habitats and modeling of species of conservation concern that are tied to EMS types or vegetation landscape patterns. Finer resolution EMS type maps can be combined with improved soil ecogroup maps to facilitate better identification of appropriate vegetation management and restoration options at resolutions that are useful to land managers. Se crearon 22 mapas de ecogrupos de suelo y 66 sistemas de mapeo ecológico (EMS; por sus siglas en inglés). Estos incluyeron temas sobre vegetación de ocho condados de la costa del sur de Texas, ubicados desde el sur de los condados_de Refugio y Aransas hasta la frontera con México. Utilizamos clasificación supervisada para extraer la cobertura del suelo a partir de imágenes satelitales Sentinel-2 con una resolución de 10 m. Accedimos a información de nubes de puntos Lidar para obtener la altura de la vegetación, la cual fue utilizada para distinguir entre tipos de EMS de herbáceas, matorrales y bosques. Obtuvimos información de elevación y de posibles encharcamientos a partir de Lidar. Creamos objetos de imagen (polígonos uniformes) a partir de datos Sentinel-2, y atribuimos cobertura de suelos, ecogrupo de suelo, altura de vegetación, elevación y potencial estancamiento de agua. Utilizamos esos atributos e imagen para modelar tipos de EMS. Completamos la modificación visual tanto de la cobertura de suelos como de los resultados de modelado de EMS utilizando la interpretación de fotografías aéreas para mejorar los resultados. El acuerdo entre el tipo de EMS mapeado y las 643 parcelas recolectadas en el campo fue superior al 75%. Los tipos de EMS más abundantes incluyeron Costa y Láminas de Arena: Pastizal de Arena Profunda (10.7% de la región), Invasora Nativa: Matorral de Mezquite/Mixto (5.0%), Costa del Golfo: Pradera Costera (4.6%), y el Sur de Texas: Sabana de Mezquite Arenoso (4.4%). Los resultados de EMS tienen una resolución nueve veces mejor que los mapas actualmente disponibles, y el uso de la altura para identificar el tipo de EMS al distinguir entre herbáceos, matorrales y bosques mejoró el mapeo más allá de lo que se ha logrado previamente. El nuevo conjunto de datos de EMS facilitará el análisis de la distribución de hábitats y el modelado de especies de importancia para la conservación vinculadas a tipos de EMS o a patrones de paisaje de vegetación. Los mapas EMS de mayor resolución pueden combinarse con mapas mejorados de ecogrupos de suelos para facilitar una mejor identificación de opciones de manejo y restauración de vegetación útiles para los gestores de tierras.

Human activities in a transborder watershed are complex under the influence of domestic policies,international relations,and global events.Understanding the forces driving human activity change is important for the development of transborder watershed.In this study,we used global historical land cover data,the hemeroby index model,and synthesized major historical events to analyze how human activity intensity changed in the Heilongjiang River(Amur River in Russia)watershed(HLRW).The results showed that there was a strong spatial heterogeneity in the variation of human activity intensity in the HLRW over the past century(1900-2016).On the Chinese side,the human activity intensity change shifted from the plain areas for agricultural reclamation to the mountainous areas for timber extraction.On the Russian side,human activity intensity changes mostly concentrated along the Trans-Siberian Railway and the Baikal-Amur Mainline.Localized variation of human activity intensity tended to respond to regional events while regionalized variation tends to reflect national policy change or broad international events.The similarities and differences between China and Russia in policies and positions in international events resulted in synchronous and asynchronous changes in human activity intensity.Meanwhile,policy shifts were often confined by the natural features of the watershed.These results reveal the historical origins and fundamental connotations of watershed development and contribute to formulating regional management policies that coordinate population,eco-nomic,social,and environmental activities.

Land-use changes and climatic changes are two of the most significant phenomena impacting water regimes globally. Since ongoing land-use changes in the form urbanization and projected climate changes are expected to have significant effects on hydrologic processes in many watersheds during coming decades, it is increasingly important for planners to understand how these stressors affect water regimes in order to develop successful watershed management strategies. Given these concerns, the objective of this research was to develop and utilize integrated modeling approaches in order to characterize potential changes to the water regime in Hinkson Creek Watershed (HCW), Boone County, Missouri. The research consisted of three main objectives: 1) to couple a CA-based urban growth model with a process-based hydrologic model to investigate the potential impacts of urbanization on hydrologic processes in HCW during the next two decades, 2) to develop climate scenarios using downscaled GCM output and couple these with a process-based hydrologic model to estimate the potential impacts of mid- and late-21st century climate changes on streamflow related processes in HCW, and 3) to use downscaled GCM output along with a cellular automata (CA) based urban growth model and a process-based hydrologic model to analyze the combined effects of mid-21 st century climatic changes and urbanization on hydrologic processes in HCW. The research provided new insight into how two of the most significant stressors affecting water resources could impact a watershed in the Midwestern United States. Aside from providing direct estimates of hydrologic changes for HCW that can be referenced by local planners and decision makers, the results from these studies provide a basis for comparison for other watersheds that share similar characteristics. This work also contributes to the field of integrated modeling in natural resources, as there is currently a paucity of research investigating the potential impacts of urbanization and climatic changes on water resources, particularly using high-resolution impervious cover estimates and data from the most recent suite of climate models. In addition, the approaches presented here provide a transferrable modeling framework that can be used by decision makers to analyze watersheds in other regions and help to develop urbanization and climate change mitigation strategies.

Renal agenesis is a devastating birth defect, and although genes encoding retinoic acid signaling components have been shown to be important for renal... Renal agenesis (RA) is one of the more extreme examples of congenital anomalies of the kidney and urinary tract (CAKUT). Bilateral renal agenesis is almost invariably fatal at birth, and unilateral renal agenesis can lead to future health issues including end-stage renal disease. Genetic investigations have identified several gene variants that cause RA, including EYA1, LHX1, and WT1. However, whereas compound null mutations of genes encoding α and γ retinoic acid receptors (RARs) cause RA in mice, to date there have been no reports of variants in RAR genes causing RA in humans. In this study, we carried out whole exome sequence analysis of two families showing inheritance of an RA phenotype, and in both identified a single candidate gene, GREB1L. Analysis of a zebrafish greb1l loss-of-function mutant revealed defects in the pronephric kidney just prior to death, and F0 CRISPR/Cas9 mutagenesis of Greb1l in the mouse revealed kidney agenesis phenotypes, implicating Greb1l in this disorder. GREB1L resides in a chromatin complex with RAR members, and our data implicate GREB1L as a coactivator for RARs. This study is the first to associate a component of the RAR pathway with renal agenesis in humans.