Innovation in Rangeland Monitoring: Annual, 30 M, Plant Functional Type Percent Cover Maps for U.S. Rangelands, 1984-2017

Innovations in machine learning and cloud‐based computing were merged with historical remote sensing and field data to provide the first moderate resolution, annual, percent cover maps of plant functional types across rangeland ecosystems to effectively and efficiently respond to pressing challenges facing conservation of biodiversity and ecosystem services. We utilized the historical Landsat satellite record, gridded meteorology, abiotic land surface data, and over 30,000 field plots within a Random Forests model to predict per‐pixel percent cover of annual forbs and grasses, perennial forbs and grasses, shrubs, and bare ground over the western United States from 1984 to 2017. Results were validated using three independent collections of plot‐level measurements, and resulting maps display land cover variation in response to changes in climate, disturbance, and management. The maps, which will be updated annually at the end of each year, provide exciting opportunities to expand and improve rangeland conservation, monitoring, and management. The data open new doors for scientific investigation at an unprecedented blend of temporal fidelity, spatial resolution, and geographic scale

Indicators and Benchmarks for Wind Erosion Monitoring, Assessment and Management

Wind erosion and blowing dust threaten food security, human health and ecosystem services across global drylands. Monitoring wind erosion is needed to inform management, with explicit monitoring objectives being critical for interpreting and translating monitoring information into management actions. Monitoring objectives should establish quantitative guidelines for determining the relationship of wind erosion indicators to management benchmarks that reflect tolerable erosion and dust production levels considering impacts to, for example, ecosystem processes, species, agricultural production systems and human well-being. Here we: 1) critically review indicators of wind erosion and blowing dust that are currently available to practitioners; and 2) describe approaches for establishing benchmarks to support wind erosion assessments and management. We find that while numerous indicators are available for monitoring wind erosion, only a subset have been used routinely and most monitoring efforts have focused on air quality impacts of dust. Indicators need to be related to the causal soil and vegetation controls in eroding areas to directly inform management. There is great potential to use regional standardized soil and vegetation monitoring datasets, remote sensing and models to provide new information on wind erosion across landscapes. We identify best practices for establishing benchmarks for these indicators based on experimental studies, mechanistic and empirical models, and distributions of indicator values obtained from monitoring data at historic or existing reference sites. The approaches to establishing benchmarks described here have enduring utility as monitoring technologies change and enable managers to evaluate co-benefits and potential trade-offs among ecosystem services as affected by wind erosion management

Incorporating Hydrologic Data and Ecohydrologic Relationships into Ecological Site Descriptions

The purpose of this paper is to recommend a framework and methodology for incorporating hydrologic data and ecohydrologic relationships in Ecological Site Descriptions (ESDs) and thereby enhance the utility of ESDs for assessing rangelands and guiding resilience-based management strategies. Resilience-based strategies assess and manage ecological state dynamics that affect state vulnerability and, therefore, provide opportunities to adapt management. Many rangelands are spatially heterogeneous or sparsely vegetated where the vegetation structure strongly influences infiltration and soil retention. Infiltration and soil retention further influence soil water recharge, nutrient availability, and overall plant productivity. These key ecohydrologic relationships govern the ecologic resilience of the various states and community phases on many rangeland ecological sites (ESs) and are strongly affected by management practices, land use, and disturbances. However, ecohydrologic data and relationships are often missing in ESDs and state-and-transition models (STMs). To address this void, we used literature to determine the data required for inclusion of key ecohydrologic feedbacks into ESDs, developed a framework and methodology for data integration within the current ESD structure, and applied the framework to a select ES for demonstrative purposes. We also evaluated the utility of the Rangeland Hydrology and Erosion Model (RHEM) for assessment and enhancement of ESDs based in part on hydrologic function. We present the framework as a broadly applicable methodology for integrating ecohydrologic relationships and feedbacks into ESDs and resilience-based management strategies. Our proposed framework increases the utility of ESDs to assess rangelands, target conservation and restoration practices, and predict ecosystem responses to management. The integration of RHEM technology and our suggested framework on ecohydrologic relations expands the ecological foundation of the overall ESD concept for rangeland management and is well aligned with resilience-based, adaptive management of US rangelands. The proposed enhancement of ESDs will improve communication between private land owners and resource managers and researchers across multiple disciplines in the field of rangeland management

Indicators and benchmarks for wind erosion monitoring, assessment and management

Wind erosion and blowing dust threaten food security, human health and ecosystem services across global drylands. Monitoring wind erosion is needed to inform management, with explicit monitoring objectives being critical for interpreting and translating monitoring information into management actions. Monitoring objectives should establish quantitative guidelines for determining the relationship of wind erosion indicators to management benchmarks that reflect tolerable erosion and dust production levels considering impacts to, for example, ecosystem processes, species, agricultural production systems and human well-being. Here we: 1) critically review indicators of wind erosion and blowing dust that are currently available to practitioners; and 2) describe approaches for establishing benchmarks to support wind erosion assessments and management. We find that while numerous indicators are available for monitoring wind erosion, only a subset have been used routinely and most monitoring efforts have focused on air quality impacts of dust. Indicators need to be related to the causal soil and vegetation controls in eroding areas to directly inform management. There is great potential to use regional standardized soil and vegetation monitoring datasets, remote sensing and models to provide new information on wind erosion across landscapes. We identify best practices for establishing benchmarks for these indicators based on experimental studies, mechanistic and empirical models, and distributions of indicator values obtained from monitoring data at historic or existing reference sites. The approaches to establishing benchmarks described here have enduring utility as monitoring technologies change and enable managers to evaluate co-benefits and potential trade-offs among ecosystem services as affected by wind erosion management

Data from: Process-based simulation of prairie growth

When field research is cost- or time-prohibitive, models can inform decision-makers regarding the impact of agricultural policy on production and the environment, but process-based models that simulate animal-plant-soil interaction and ecosystem services in grazing lands are rare. In the U.S.A., APEX (Agricultural Policy/Environmental eXtender) is a model commonly used to inform policy on cropland, but its ability to simulate grazinglands was less robust. Therefore, we enhanced the APEX model’s plant growth module to improve its utility on grazing lands. Improvements addressed allocation of new biomass, response to water stress, competition for soil water, and regrowth of herbaceous perennials. Sensitivity analysis demonstrated that simulated biomass responded to changes in precipitation through adjustments to both total biomass and distribution of biomass aboveground and belowground. A deep-rooted species generally outperformed a shallow-rooted species but the relative advantage was greatest when precipitation was historically low. A 10-year dataset of peak biomass collected in central Kansas, U.S.A., was divided among 5 species and species groups and was used for calibration and validation. When the mass of all species was combined in the validation dataset, the percent bias was −2%, Willmott’s Dr was 0.79, and r2 was 0.84. When biomass production of individual species was analyzed, the model did not perform as well, with the percent bias ranging from −36 to 29%, Willmott’s Dr ranging from 0.58 to 0.71, and r2 from 0.25 to 0.67. Because grazing lands often have a rich species diversity, the improvements made APEX better-suited to modeling such heterogeneous landscapes. However, simulating biomass of individual species, rather than the sum of all species, is an area that still needs improvement. Further testing at additional sites to calibrate single- and multiple-species growth and identify any spatial trends in model performance will also be beneficial

APEX 1203

APEX model executable, version 120

Rangeland CEAP: An Assessment of Natural Resources Conservation Service Practices

On The Ground • The Conservation Effects Assessment Project (CEAP) is a multi-agency effort to quantify the Environmental effects of conservation practices and programs and develop the science base for managing the agricultural landscape for environmental quality. • The rangeland CEAP review evaluated the scientific literature on seven core NRCS conservation practices: prescribed grazing, prescribed burning, brush management, range planting, riparian herbaceous cover, upland wildlife habitat management, and herbaceous weed control. • The scientific literature “broadly supports” the reviewed rangeland conservation practices standards; however, there is a disjunct in integrating science and field-based knowledge so that managers and conservationists can fully understand the individualistic dynamic aspects of rangeland conservation practices. • The CEAP synthesis establishes a precedent for partnerships among scientists, land managers, conservation specialists, and policymakers to provide NRCS with useful, current, science-based information for rangeland conservation practices.The Rangelands archives are made available by the Society for Range Management and the University of Arizona Libraries. Contact [email protected] for further information.Migrated from OJS platform March 202

An artificial neural network emulator of the rangeland hydrology and erosion model

Machine learning (ML) is becoming an ever more important tool in hydrologic modeling. Previous studies have shown the higher prediction accuracy of those ML models over traditional process-based ones. However, there is another advantage of ML which is its lower computational demand. This is important for the applications such as hydraulic soil erosion estimation over a large area and at a finer spatial scale. Using traditional models like Rangeland Hydrology and Erosion Model (RHEM) requires too much computation time and resources. In this study, we designed an Artificial Neural Network that is able to recreate the RHEM outputs (annual average runoff, soil loss, and sediment yield and not the daily storm event-based values) with high accuracy (Nash-Sutcliffe Efficiency ≈ 1.0) and a very low computational time (13 billion times faster on average using a GPU). We ran the RHEM for more than a million synthetic scenarios and train the Emulator with them. We also, fine-tuned the trained Emulator with the RHEM runs of the real-world scenarios (more than 32,000) so the Emulator remains comprehensive while it works specifically accurately for the real-world cases. We also showed that the sensitivity of the Emulator to the input variables is similar to the RHEM and it can effectively capture the changes in the RHEM outputs when an input variable varies. Finally, the dynamic prediction behavior of the Emulator is statistically similar to the RHEM

Multi-Temporal LiDAR and Hyperspectral Data Fusion for Classification of Semi-Arid Woody Cover Species

Mapping the spatial distribution of woody vegetation is important for monitoring, managing, and studying woody encroachment in grasslands. However, in semi-arid regions, remotely sensed discrimination of tree species is difficult primarily due to the tree similarities, small and sparse canopy cover, but may also be due to overlapping woody canopies as well as seasonal leaf retention (deciduous versus evergreen) characteristics. Similar studies in different biomes have achieved low accuracies using coarse spatial resolution image data. The objective of this study was to investigate the use of multi-temporal, airborne hyperspectral imagery and light detection and ranging (LiDAR) derived data for tree species classification in a semi-arid desert region. This study produces highly accurate classifications by combining multi-temporal fine spatial resolution hyperspectral and LiDAR data (~1 m) through a reproducible scripting and machine learning approach that can be applied to larger areas and similar datasets. Combining multi-temporal vegetation indices and canopy height models led to an overall accuracy of 95.28% and kappa of 94.17%. Five woody species were discriminated resulting in producer accuracies ranging from 86.12% to 98.38%. The influence of fusing spectral and structural information in a random forest classifier for tree identification is evident. Additionally, a multi-temporal dataset slightly increases classification accuracies over a single data collection. Our results show a promising methodology for tree species classification in a semi-arid region using multi-temporal hyperspectral and LiDAR remote sensing data

Case Study: Application of Ecological Site Information to Transformative Changes on Great Basin Sagebrush Rangelands

On the Ground • The utility of ecological site descriptions (ESD) in the management of rangelands hinges on their ability to characterize and predict plant community change, the associated ecological consequences, and ecosystem responsiveness to management. • We demonstrate how enhancement of ESDs with key ecohydrologic information can aid predictions of ecosystem response and targeting of conservation practices for sagebrush rangelands that are strongly regulated by ecohydrologic or ecogeomorphic feedbacks. • The primary point of this work is that ESD concepts are flexible and can be creatively augmented for improved assessment and management of rangelands.The Rangelands archives are made available by the Society for Range Management and the University of Arizona Libraries. Contact [email protected] for further information.Migrated from OJS platform March 202