Towards implementing precision conservation practices in agricultural watersheds: A review of the use and prospects of spatial decision support systems and tools.

Agricultural nonpoint source (NPS) pollution leads to water quality degradation. While agriculture is faced with the challenge of feeding a growing population in a changing climate, farmers must also strive to minimize adverse impacts of agriculture on the environment. As a result, policies, and agri-environmental programs to promote agricultural conservation practices for controlling NPS pollution have been emerging. Despite progress, reducing NPS is a complex challenge that requires ongoing innovation and investment. A major challenge is to achieve an optimal spatial trade-off between the economic costs and positive environmental outcomes of conservation practices on complex agricultural landscapes. Geospatial systems and tools can help to address this challenge and enhance the effectiveness and efficiency of conservation efforts. However, using these tools for precision conservation is underexamined. This review paper aims to address this gap through a critical exploration of spatial decision support systems and tools to provide synthesized knowledge for implementing precision conservation practices. This paper proposes a conceptual framework to guide the implementation of precision conservation and identifies areas for further development of geospatial systems and tools on planning and assessment of precision conservation efforts. All of which will be helpful for decision-makers and watershed managers in determining the most effective approaches for precision conservation. Furthermore, this review highlights the need for further research and development towards establishing an integrated spatial decision support system framework, which can improve socio-economic, environmental, and ecological outcomes.

Vulnerable Waters are Essential to Watershed Resilience.

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.

High aboveground carbon stock of African tropical montane forests.

Tropical forests store 40-50 per cent of terrestrial vegetation carbon1. However, spatial variations in aboveground live tree biomass carbon (AGC) stocks remain poorly understood, in particular in tropical montane forests2. Owing to climatic and soil changes with increasing elevation3, AGC stocks are lower in tropical montane forests compared with lowland forests2. Here we assemble and analyse a dataset of structurally intact old-growth forests (AfriMont) spanning 44 montane sites in 12 African countries. We find that montane sites in the AfriMont plot network have a mean AGC stock of 149.4 megagrams of carbon per hectare (95% confidence interval 137.1-164.2), which is comparable to lowland forests in the African Tropical Rainforest Observation Network4 and about 70 per cent and 32 per cent higher than averages from plot networks in montane2,5,6 and lowland7 forests in the Neotropics, respectively. Notably, our results are two-thirds higher than the Intergovernmental Panel on Climate Change default values for these forests in Africa8. We find that the low stem density and high abundance of large trees of African lowland forests4 is mirrored in the montane forests sampled. This carbon store is endangered: we estimate that 0.8 million hectares of old-growth African montane forest have been lost since 2000. We provide country-specific montane forest AGC stock estimates modelled from our plot network to help to guide forest conservation and reforestation interventions. Our findings highlight the need for conserving these biodiverse9,10 and carbon-rich ecosystems.

Isolating Anthropogenic Wetland Loss by Concurrently Tracking Inundation and Land Cover Disturbance across the Mid-Atlantic Region, U.S.

Global trends in wetland degradation and loss have created an urgency to monitor wetland extent, as well as track the distribution and causes of wetland loss. Satellite imagery can be used to monitor wetlands over time, but few efforts have attempted to distinguish anthropogenic wetland loss from climate-driven variability in wetland extent. We present an approach to concurrently track land cover disturbance and inundation extent across the Mid-Atlantic region, United States, using the Landsat archive in Google Earth Engine. Disturbance was identified as a change in greenness, using a harmonic linear regression approach, or as a change in growing season brightness. Inundation extent was mapped using a modified version of the U.S. Geological Survey's Dynamic Surface Water Extent (DSWE) algorithm. Annual (2015-2018) disturbance averaged 0.32% (1095 km2 year-1) of the study area per year and was most common in forested areas. While inundation extent showed substantial interannual variability, the co-occurrence of disturbance and declines in inundation extent represented a minority of both change types, totaling 109 km2 over the four-year period, and 186 km2, using the National Wetland Inventory dataset in place of the Landsat-derived inundation extent. When the annual products were evaluated with permitted wetland and stream fill points, 95% of the fill points were detected, with most found by the disturbance product (89%) and fewer found by the inundation decline product (25%). The results suggest that mapping inundation alone is unlikely to be adequate to find and track anthropogenic wetland loss. Alternatively, remotely tracking both disturbance and inundation can potentially focus efforts to protect, manage, and restore wetlands.

Integrating LiDAR data and multi-temporal aerial imagery to map wetland inundation dynamics using Google Earth Engine.

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 watershed-scale model for depressional wetland-rich landscapes.

Wetlands are often dominant features in low relief, depressional landscapes and provide an array of hydrologically driven ecosystem services. However, contemporary models do not adequately represent the role of spatially distributed wetlands in watershed-scale water storage and flows. Such tools are critical to better understand wetland hydrological, biogeochemical, and biological functions and predict management and policy outcomes at varying spatial scales. To develop a new approach for simulating depressional landscapes, we modified the Soil and Water Assessment Tool (SWAT) model to incorporate improved representations of depressional wetland structure and hydrological processes. Specifically, we refined the model to incorporate: (1) water storage capacity and surface flowpaths of individual wetlands and (2) local wetland surface and subsurface exchange. We utilized this model, termed SWAT-DSF (DSF for Depressional Storage and Flows), to simulate the ~289 km2 Greensboro watershed within the Delmarva Peninsula of the US Coastal Plain. Model calibration and verification used both daily streamflow observations and remotely sensed surface water extent data (ca. 2-week temporal resolution), allowing us to assess model performance with respect to both streamflow and watershed inundation patterns. Our findings demonstrate that SWAT-DSF can successfully replicate distributed wetland processes and resultant watershed-scale hydrology. SWAT-DSF provides improved temporal and spatial characterization of watershed-scale water storage and flows in depressional landscapes, providing a new tool to quantify wetland functions at broad spatial scales.

Design and Implementation of an Interactive Web-Based Near Real-Time Forest Monitoring System.

This paper describes an interactive web-based near real-time (NRT) forest monitoring system using four levels of geographic information services: 1) the acquisition of continuous data streams from satellite and community-based monitoring using mobile devices, 2) NRT forest disturbance detection based on satellite time-series, 3) presentation of forest disturbance data through a web-based application and social media and 4) interaction of the satellite based disturbance alerts with the end-user communities to enhance the collection of ground data. The system is developed using open source technologies and has been implemented together with local experts in the UNESCO Kafa Biosphere Reserve, Ethiopia. The results show that the system is able to provide easy access to information on forest change and considerably improves the collection and storage of ground observation by local experts. Social media leads to higher levels of user interaction and noticeably improves communication among stakeholders. Finally, an evaluation of the system confirms the usability of the system in Ethiopia. The implemented system can provide a foundation for an operational forest monitoring system at the national level for REDD+ MRV applications.

Characterizing Forest Change Using Community-Based Monitoring Data and Landsat Time Series.

Increasing awareness of the issue of deforestation and degradation in the tropics has resulted in efforts to monitor forest resources in tropical countries. Advances in satellite-based remote sensing and ground-based technologies have allowed for monitoring of forests with high spatial, temporal and thematic detail. Despite these advances, there is a need to engage communities in monitoring activities and include these stakeholders in national forest monitoring systems. In this study, we analyzed activity data (deforestation and forest degradation) collected by local forest experts over a 3-year period in an Afro-montane forest area in southwestern Ethiopia and corresponding Landsat Time Series (LTS). Local expert data included forest change attributes, geo-location and photo evidence recorded using mobile phones with integrated GPS and photo capabilities. We also assembled LTS using all available data from all spectral bands and a suite of additional indices and temporal metrics based on time series trajectory analysis. We predicted deforestation, degradation or stable forests using random forest models trained with data from local experts and LTS spectral-temporal metrics as model covariates. Resulting models predicted deforestation and degradation with an out of bag (OOB) error estimate of 29% overall, and 26% and 31% for the deforestation and degradation classes, respectively. By dividing the local expert data into training and operational phases corresponding to local monitoring activities, we found that forest change models improved as more local expert data were used. Finally, we produced maps of deforestation and degradation using the most important spectral bands. The results in this study represent some of the first to combine local expert based forest change data and dense LTS, demonstrating the complementary value of both continuous data streams. Our results underpin the utility of both datasets and provide a useful foundation for integrated forest monitoring systems relying on data streams from diverse sources.

An integrated pan-tropical biomass map using multiple reference datasets.

We combined two existing datasets of vegetation aboveground biomass (AGB) (Proceedings of the National Academy of Sciences of the United States of America, 108, 2011, 9899; Nature Climate Change, 2, 2012, 182) into a pan-tropical AGB map at 1-km resolution using an independent reference dataset of field observations and locally calibrated high-resolution biomass maps, harmonized and upscaled to 14 477 1-km AGB estimates. Our data fusion approach uses bias removal and weighted linear averaging that incorporates and spatializes the biomass patterns indicated by the reference data. The method was applied independently in areas (strata) with homogeneous error patterns of the input (Saatchi and Baccini) maps, which were estimated from the reference data and additional covariates. Based on the fused map, we estimated AGB stock for the tropics (23.4 N-23.4 S) of 375 Pg dry mass, 9-18% lower than the Saatchi and Baccini estimates. The fused map also showed differing spatial patterns of AGB over large areas, with higher AGB density in the dense forest areas in the Congo basin, Eastern Amazon and South-East Asia, and lower values in Central America and in most dry vegetation areas of Africa than either of the input maps. The validation exercise, based on 2118 estimates from the reference dataset not used in the fusion process, showed that the fused map had a RMSE 15-21% lower than that of the input maps and, most importantly, nearly unbiased estimates (mean bias 5 Mg dry mass ha(-1) vs. 21 and 28 Mg ha(-1) for the input maps). The fusion method can be applied at any scale including the policy-relevant national level, where it can provide improved biomass estimates by integrating existing regional biomass maps as input maps and additional, country-specific reference datasets.

Localization of Rheb to the endomembrane is critical for its signaling function.

Rheb, a small GTPase, has emerged as a key molecular switch that directly regulates the activity of the mammalian target of rapamycin (mTOR). Similar to other members of the Ras superfamily, Rheb has a C-terminal CaaX box that is subject to farnesylation. This study reports that farnesylation is a key determinant of Rheb's subcellular localization and directs its association with the endomembrane. Timed imaging of live cells expressing EGFP-Rheb reveals that following brief association with the ER, Rheb localizes to highly ordered, distinct structures within the cytoplasm that display characteristics of Golgi membranes. Failure of Rheb to localize to the endomembrane impairs its ability to interact with mTOR and activate downstream targets. Consistent with the notion that the endomembrane may serve as a platform for the assembly of a functional Rheb/mTOR complex, treatment of cells with brefeldin A interferes with transmission of Rheb signals to p70S6K.