Dataset Information

Rapid Crop Cover Mapping for the Conterminous United States.

ABSTRACT: Timely crop cover maps with sufficient resolution are important components to various environmental planning and research applications. Through the modification and use of a previously developed crop classification model (CCM), which was originally developed to generate historical annual crop cover maps, we hypothesized that such crop cover maps could be generated rapidly during the growing season. Through a process of incrementally removing weekly and monthly independent variables from the CCM and implementing a 'two model mapping' approach, we found it viable to generate conterminous United States-wide rapid crop cover maps at a resolution of 250?m for the current year by the month of September. In this approach, we divided the CCM model into one 'crop type model' to handle the classification of nine specific crops and a second, binary model to classify the presence or absence of 'other' crops. Under the two model mapping approach, the training errors were 0.8% and 1.5% for the crop type and binary model, respectively, while test errors were 5.5% and 6.4%, respectively. With spatial mapping accuracies for annual maps reaching upwards of 70%, this approach demonstrated a strong potential for generating rapid crop cover maps by the 1st of September.

SUBMITTER: Dahal D

PROVIDER: S-EPMC5988726 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Rapid Crop Cover Mapping for the Conterminous United States.

Dahal Devendra D Wylie Bruce B Howard Danny D

Scientific reports 20180605 1

Timely crop cover maps with sufficient resolution are important components to various environmental planning and research applications. Through the modification and use of a previously developed crop classification model (CCM), which was originally developed to generate historical annual crop cover maps, we hypothesized that such crop cover maps could be generated rapidly during the growing season. Through a process of incrementally removing weekly and monthly independent variables from the CCM ...[more]

PMID: 29872107

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Project description:Watershed integrity is the capacity of a watershed to support and maintain the full range of ecological processes and functions essential to sustainability. Using information from EPA's StreamCat dataset, we calculated and mapped an Index of Watershed Integrity (IWI) for 2.6 million watersheds in the conterminous US with first-order approximations of relationships between stressors and six watershed functions: hydrologic regulation, regulation of water chemistry, sediment regulation, hydrologic connectivity, temperature regulation, and habitat provision. Results show high integrity in the western US, intermediate integrity in the southern and eastern US, and the lowest integrity in the temperate plains and lower Mississippi Valley. Correlation between the six functional components was high (r = 0.85-0.98). A related Index of Catchment Integrity (ICI) was developed using local drainages of individual stream segments (i.e., excluding upstream information). We evaluated the ability of the IWI and ICI to predict six continuous site-level indicators with regression analyses - three biological indicators and principal components derived from water quality, habitat, and combined water quality and habitat variables - using data from EPA's National Rivers and Streams Assessment. Relationships were highly significant, but the IWI only accounted for 1-12% of the variation in the four biological and habitat variables. The IWI accounted for over 25% of the variation in the water quality and combined principal components nationally, and 32-39% in the Northern and Southern Appalachians. We also used multinomial logistic regression to compare the IWI with the categorical forms of the three biological indicators. Results were consistent: we found positive associations but modest results. We compared how the IWI and ICI predicted the water quality PC relative to agricultural and urban land use. The IWI or ICI are the best predictors of the water quality PC for the CONUS and six of the nine ecoregions, but they only perform marginally better than agriculture in most instances. However, results suggest that agriculture would not be appropriate in all parts of the country, and the index is meant to be responsive to all stressors. The IWI in its present form (available through the StreamCat website; https://www.epa.gov/national-aquatic-resource-surveys/streamcat) could be useful for management efforts at multiple scales, especially when combined with information on site condition. The IWI could be improved by incorporating empirical or literature-derived relationships between functional components and stressors. However, limitations concerning the absence of data for certain stressors should be considered.

Project description:Nonnative plants are widely recognized as stressors to wetlands and other ecosystems. They may compete with native plant species or communities and alter ecosystem properties, which can affect ecological condition, posing challenges to resource managers. As part of the United States Environmental Protection Agency's National Wetland Condition Assessment (NWCA), we characterized the status of nonnative plants in wetlands across the conterminous United States (US). Our primary goals were to (1) document the composition of nonnative taxa at 1138 NWCA sites sampled in 2011 and (2) estimate the areal extent of wetland under stress from nonnative plants within the NWCA 2011 sampled population of ~?25 million ha of wetland (represented by 967 sampled probability sites and the NWCA survey design). A total of 443 unique nonnative taxa were observed, encompassing a species pool adapted to diverse ecological conditions. For individual sites, the number of nonnative taxa ranged from 0 to 29, and total absolute cover of nonnatives ranged from 0 to 160%. We devised the nonnative plant indicator (NNPI) as a categorical indicator of stress (low to very high) from the collective set of nonnative plant taxa occurring at a particular location, based on a decision matrix of exceedance values for nonnative richness, relative frequency, and relative cover. Wetland area of the sampled population occurring in each NNPI category was estimated at the scale of the conterminous US and within five large ecoregions and four broad wetland types. Potential stress from nonnative plants, as indicated by the NNPI category, was low for approximately 61% (~?15.3 million ha), moderate for about 20% (~?5.2 million ha), high for about 10% (~?2.48 million ha), and very high for about 9% (~?2.2 million ha) of the wetland area in the entire sampled population. Percent of wetland area with high and very high NNPI varied by ecoregional subpopulations: greater within interior and western ecoregions (~?29 to 87%) than within ecoregions in the eastern half of the nation (~?11%). Among wetland type subpopulations, greater percent of wetland area with high and very high NNPI was observed for herbaceous vs. woody types and for inland vs. estuarine types. Estimates of wetland area by NNPI categories are expected to be useful to policy makers or resource managers for prioritizing management actions by identifying situations where stress from nonnative plants is most extensive. We also considered four exploratory analyses aimed at providing ecological information useful in interpreting NNPI extent results. We conducted three population-scale analyses examining ecoregional and wetland type population means for (1) the three NNPI metrics, (2) absolute cover of growth-habit groups of nonnative plants, and (3) metrics describing human-mediated disturbance. Finally, we examined ecological relationships with site-level NNPI status using a random forest (RF) analysis with NNPI as the response variable and predictor variables including ecoregion, wetland type, and a variety of characteristics describing natural vegetation structure, environment, and human-mediated disturbance.

Dataset Information

Rapid Crop Cover Mapping for the Conterminous United States.

Publications

Rapid Crop Cover Mapping for the Conterminous United States.

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