Filters: Contacts: {oldPartyId:11505} (X)
64 results (134ms)|
Filters
Date Range
Extensions Types Contacts
Categories Tag Types
|
Spatially accurate annual crop cover maps are an important component to various planning and research applications; however, the importance of these maps varies significantly with the timing of their availability. Utilizing a previously developed crop classification model (CCM), which was used to generate historical annual crop cover maps (classifying nine major crops: corn, cotton, sorghum, soybeans, spring wheat, winter wheat, alfalfa, other hay/non alfalfa, fallow/idle cropland, and ‘other’ as one class for remaining crops), we hypothesized that such crop cover maps could be generated in near real time (NRT). The CCM was trained on 14 temporal and 15 static geospatial datasets, known as predictor variables, and...
Switchgrass (Panicum virgatum L.), a highly productive perennial grass, has been recommended as one potential source for cellulosic biofuel feedstocks. Previous studies indicate that planting perennial grasses (e.g., switchgrass) in high topographic relief cropland waterway buffers can improve local environmental conditions and sustainability. The main advantages of this land management practice include (1) reducing soil erosion and improving water quality because switchgrass requires less tillage, fertilizers, and pesticides; and (2) improving regional ecosystem services (e.g., improving water infiltration, minimizing drought and flood impacts on production, and serving as carbon sinks). In this study, we mapped...
Cheatgrass began invading the Great Basin about 100 years ago, changing large parts of the landscape from a rich, diverse ecosystem to one where a single invasive species dominates. Cheatgrass dominated areas experience more fires that burn more land than in native ecosystems, resulting in economic and resource losses. Therefore, the reduced production, or absence, of cheatgrass in previously invaded areas during years of adequate precipitation could be seen as a windfall. However, this cheatgrass dieoff phenomenon creates other problems for land managers like accelerated soil erosion, loss of early spring food supply for livestock and wildlife, and unknown recovery pathways. We used satellite data and scientific...
Categories: Project;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: 2011,
CASC,
Cheatgrass,
Climate Change,
Completed,
Exotic annual grasses [EAG] are one of the most damaging biological stressors in western North America. Despite numerous environmental and societal impacts associated with EAG there remains a need to enhance regional monitoring capabilities to better guide management and conservation efforts. Here we provide estimates of historic and potential future trends in EAG abundance that were developed using linear trend analysis and machine learning techniques at a 30-m spatial resolution. Specifically, these data represent historic (1985 to 2019) and potential future (2025-2040) rates of exotic annual grass change as estimated using Theil-Sen regression and a process-constrained, random forest model assuming only changes...
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Ecology,
Exotic annual grasses, cheatgrass, remote sensing,
Exotic annual grasses, cheatgrass, remote sensing,
Land Use Change,
Remote Sensing,
Exotic annual grasses are one of the most damaging biological stressors in western North America and increase the susceptibility of landscapes to wildfire occurrence. Here we couple estimates of long-term rangeland component fractions (e.g. exotic annual grasses) with remote sensing, climate data, and machine learning techniques to estimate the long-term (1985 to 2019) probability of wildfire occurrence (30-m spatial resolution) in sagebrush-dominated landscapes of the western United States.
The study's goal was to downscale 2013 250-m expedited Moderate Resolution Imaging Spectroradiometer (eMODIS) Normalized Difference Vegetation Index (NDVI) to 30 m (Gu, Y. and Wylie, B.K., 2015, Developing a 30-m grassland productivity estimation map for central Nebraska using 250-m MODIS and 30-m Landsat-8 observations, Remote Sensing of Environment, v. 171, p. 291-298)using 2013 Landsat 8 data. The eMODIS NDVI was downscaled for four periods: mid spring, early summer, late summer and mid fall. The objective was to capture phenologies during periods that correspond to 1) annual grass growth, 2) annual grass senescence, 3) the optimal NDVI profile separation between sagebrush and other shrubs in the region, and...
Fire and hydrology can be significant drivers of permafrost change in boreal landscapes, altering the availability of soil carbon and nutrients that have important implications for future climate and ecological succession. However, not all landscapes are equally susceptible to disturbance. New methods are needed to understand the vulnerability and resilience of different landscapes to permafrost degradation. This project uses remote sensing, geophysical, and other field-based observations to reveal details of both near-surface (<1 m) and deeper (>1 m) permafrost characteristics over multiple scales. This LandCarbon project currently supports the NASA ABoVE project, 'Vulnerability of inland waters and the aquatic...
Categories: Project;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Alaska,
Carbon Stock,
Disturbance,
Geophysics,
Permafrost,
A warming climate influences boreal forest productivity, dynamics, and disturbance regimes. We used ecosystem models and 250 m satellite Normalized Difference Vegetation Index (NDVI) data averaged over the growing season (GSN) to model current, and estimate future, ecosystem performance. We modeled Expected Ecosystem Performance (EEP), or anticipated productivity, in undisturbed stands over the 2000–2008 period from a variety of abiotic data sources, using a rule-based piecewise regression tree. The EEP model was applied to a future climate ensemble A1B projection to quantify expected changes to mature boreal forest performance. Ecosystem Performance Anomalies (EPA), were identified as the residuals of the EEP and...
Categories: Project;
Types: Journal Citation;
Tags: Alaska,
Boreal forest,
Climate change,
Disturbance,
Fire,
Spatially accurate annual crop cover maps are an important component to various planning and research applications; however, the importance of these maps varies significantly with the timing of their availability. Utilizing a previously developed crop classification model (CCM), which was used to generate historical annual crop cover maps (classifying nine major crops: corn, cotton, sorghum, soybeans, spring wheat, winter wheat, alfalfa, other hay/non alfalfa, fallow/idle cropland, and ‘other’ as one class for remaining crops), we hypothesized that such crop cover maps could be generated in near real time (NRT). The CCM was trained on 14 temporal and 15 static geospatial datasets, known as predictor variables, and...
We mapped eleven years of cheatgrass dieoff in the northern Great Basin. If we estimated that a dieoff occurred in a pixel anytime during that eleven year period, then the pixel was coded as dieoff. If no dieoff occurred, the pixel was coded as a non dieoff. The cheatgrass dieoff probability map was produced by inputting the coded data into a decision-tree model along with topographic data, edaphic data, land cover data, and climate data. A proxy for latitude was included. The resulting model was input into a mapping application that generated a map of cheatgrass dieoff probability.
Spatially accurate annual crop cover maps are an important component to various planning and research applications; however, the importance of these maps varies significantly with the timing of their availability. Utilizing a previously developed crop classification model (CCM), which was used to generate historical annual crop cover maps (classifying nine major crops: corn, cotton, sorghum, soybeans, spring wheat, winter wheat, alfalfa, other hay/non alfalfa, fallow/idle cropland, and ‘other’ as one class for remaining crops), we hypothesized that such crop cover maps could be generated in near real time (NRT). The CCM was trained on 14 temporal and 15 static geospatial datasets, known as predictor variables, and...
The dryland ecosystems of the western United States have been invaded by exotic annual grasses, such as cheatgrass (Bromus tectorum L.), that has promoted increased fire activity and reduced biodiversity detrimental to socio-environmental systems. The use of remote sensing tools to monitor exotic annual grass cover and dynamics over large areas can support early detection and rapid response initiatives. This dataset was generated using in situ observations from Bureau of Land Management's (BLM) Assessment, Inventory, and Monitoring data (AIM) plots, weekly composites of harmonized Landsat and Sentinel-2 (HLS) data, relevant environmental, vegetation, remotely sensed, and geophysical factors and machine learning...
This dataset provides a near-real-time estimate of 2018 herbaceous annual cover with an emphasis on annual grass (Boyte and Wylie. 2016. Near-real-time cheatgrass percent cover in the Northern Great Basin, USA, 2015. Rangelands 38:278-284.) This estimate was based on remotely sensed enhanced Moderate Resolution Imaging Spectroradiometer (eMODIS) Normalized Difference Vegetation Index (NDVI) data gathered through July 1, 2018. This is the second iteration of an early estimate of herbaceous annual cover for 2018 over the same geographic area. The previous dataset used eMODIS NDVI data gathered through May 1 (https://doi.org/10.5066/P9KSR9Z4). The pixel values for this most recent estimate ranged from 0 to100% with...
The dataset provides a near real time estimation of 2020 herbaceous mostly annual fractional cover predicted on July 1st with an emphasis on annual exotic grasses Historically, similar maps were produced at a spatial resolution of 250m (Boyte et al. 2019 https://doi.org/10.5066/P96PVZIF., Boyte et al. 2018 https://doi.org/10.5066/P9RIV03D.), but starting this year we are mapping at a 30m resolution (Pastick et al. 2020 doi:10.3390/rs12040725). This dataset was generated using in situ observations from Bureau of Land Management’s (BLM) Assessment, Inventory, and Monitoring data (AIM) plots; weekly composites of harmonized Landsat and Sentinel-2 (HLS) data (https://hls.gsfc.nasa.gov/); relevant environmental, vegetation,...
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: California,
Colorado,
Great Basin,
Harmonized Landsat Sentinel,
Idaho,
Fire can be a significant driver of permafrost change in boreal landscapes, altering the availability of soil carbon and nutrients that have important implications for future climate and ecological succession. However, not all landscapes are equally susceptible to fire-induced change. As fire frequency is expected to increase in the high latitudes, methods to understand the vulnerability and resilience of different landscapes to permafrost degradation are needed. Geophysical and other field observations reveal details of both near-surface (<1 m) and deeper (>1 m) impacts of fire on permafrost along 11 transects that span burned-unburned boundaries in different landscape settings within interior Alaska. Data collected...
Categories: Data;
Types: Downloadable,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
Shapefile;
Tags: Alaska,
City of Fairbanks,
Disturbance,
Fire,
Geophysics,
In this study, we developed a method that identifies an optimal sample data usage strategy and rule numbers that minimize over- and underfitting effects in regression tree mapping models. A LANDFIRE tile (r04c03, located mainly in northeastern Nevada), which is a composite of multiple Landsat 8 scenes for a target date, was selected for the study. To minimize any cloud and bad detection effects in the original Landsat 8 data, the compositing approach used cosine-similarity-combined pixels from multiple observations based on data quality and temporal proximity to a target date. Julian date 212, which yielded relatively low "no data and/or cloudy” pixels, was used as the target date with Landsat 8 observations from...
In support of mapping ecological conditions (e.g. invasive annual grass) in sagebrush-dominated landscapes of the western United States, we developed weekly (starting from week 7 to week 42 and Week 1 starts January 1 or Day of the year 1 to 7, week 2 is from Day of year 8 to 14, and so on) 30-m cloud-free Normalized Difference Vegetation Index (NDVI) from 2016 to 2019. The data was generated with machine-learning techniques (i.e., regression tree [RT]) and harmonized Landsat and Sentinel -2 (HLS) data. The geographic coverage includes areas in the Great Basin, the Snake River Plain, the state of Wyoming, and contiguous areas. This NDVI collection allows for local-scale detection and analysis such as, fuel breaks...
The dataset provides a spatially explicit estimate of 2019 herbaceous annual percent cover predicted on May 1st with an emphasis on annual grasses. The estimate is based on the mean output of two regression-tree models. For one model, we include, as an independent variable amongst other independent variables, a dataset that is the mean of 17-years of annual herbaceous percent cover (https://doi.org/10.5066/F71J98QK). This model's test mean error rate (n = 1670), based on nine different randomizations, equals 4.9% with a standard deviation of +/- 0.15. A second model was developed that did not include the mean of 17-years of annual herbaceous percent cover, and this model's test mean error rate (n = 1670), based...
Categories: Data;
Types: Downloadable,
GeoTIFF,
Map Service,
Raster;
Tags: Bromus tectorum,
California,
Colorado,
Desert,
Ecology,
Spatially accurate annual crop cover maps are an important component to various planning and research applications; however, the importance of these maps varies significantly with the timing of their availability. Utilizing a previously developed crop classification model (CCM), which was used to generate historical annual crop cover maps (classifying nine major crops: corn, cotton, sorghum, soybeans, spring wheat, winter wheat, alfalfa, other hay/non alfalfa, fallow/idle cropland, and ‘other’ as one class for remaining crops), we hypothesized that such crop cover maps could be generated in near real time (NRT). The CCM was trained on 14 temporal and 15 static geospatial datasets, known as predictor variables, and...
Electrical resistivity tomography (ERT), downhole nuclear magnetic resonance (NMR), and manual permafrost-probe measurements were used to quantify permafrost characteristics along transects within several catchments in interior Alaska in late summer 2016 and 2017. Geophysical sites were chosen to coincide with additional soil, hydrologic, and geochemical measurements adjacent to various low-order streams and tributaries in a mix of burned and unburned watersheds in both silty and rocky environments. Data were collected in support of the Striegl-01 NASA ABoVE project, "Vulnerability of inland waters and the aquatic carbon cycle to changing permafrost and climate across boreal northwestern North America." Additional...
Categories: Data;
Types: Downloadable,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
Shapefile;
Tags: Active layer thickness,
Alaska,
Borehole nuclear magnetic resonance,
Disturbance,
Fire,
|
|
