Fractional cover estimates of sweet clover derived from UAV, aerial, and Sentinel-2 imagery for central Montana and northwest South Dakota, 2019

Yellow sweet clover (Melilotus officinalis; clover hereafter) is a biennial legume native to Eurasia that is now present in all 50 states. Clover can grow 2 m tall and achieve high densities across large areas in the Northern Great Plains when conditions are conducive, such as in 2019. Clover is highly efficient at fixing nitrogen in soils which reduces the abundance of native grasses, while simultaneously facilitating invasion of non-native grasses, which may alter fire regimes. In contrast, clover provides considerable forage for ungulates, attracts a wide variety of insects that, along with clover seeds, are important to waterfowl, gamebirds, and songbirds, and supports numerous pollinators. Little is known about the extent of clover in central Montana and northwest South Dakota and this study represents the first known attempt to map clover in these regions. 
In 2019, the Bureau of Land Management conducted Assessment, Inventory, and Monitoring (AIM) surveys at 10 sites in central Montana (defined as the approximate geographic extent of Musselshell County) and 24 sites in northwest South Dakota (defined as the approximate geographic extent of Butte and Harding Counties). Concurrent Unmanned Aerial Vehicle (UAV) flights were conducted at 22 sites: 6 in Montana and 16 in South Dakota. We created orthoimages from the 22 UAV surveys as well as clover maps for the 19 sites with clover. Percent clover cover from the UAV-derived clover maps closely matched percent cover from AIM data along surveyed transects. The UAV clover map with the greatest percent cover in each region was then used to identify pixels comprised of clover in National Agricultural Imagery Program (NAIP) imagery: 5,000 pixels in Montana and 2,500 pixels in South Dakota. We used separate MaxEnt models to classify clover across 1 NAIP tile in central Montana and 2 NAIP tiles in northwest South Dakota. Next, for each region, we calculated the percent of classified NAIP pixels within each Sentinel-2 pixel and selected 1,000 pixels from each of 2 fractional cover (FC) bins representing 20% increments from 10-50% cover and 1,000 pixels from each of 5 fractional cover (FC) bins representing 10% increments from 55-95% cover. We also selected 1,000 pixels in each region from dense clover strands visible in Sentinel-2 imagery representing pure (i.e., 100% cover) clover areas. Separate MaxEnt models were run in each region for the pure clover areas and each FC class. We fixed the pure clover area for each region and added fractional coverage components outside this consistent pure clover area by thresholding each of the 5 FC models using 5 common MaxEnt thresholds and merging results using 3 classification approaches for pixels classified by multiple FC models: minimum, mean, and maximum cover predicted. Accuracy of the 15 FC maps were validated by comparison to AIM survey data (30 m buffer from AIM plot center) and UAV-derived clover maps (300 x 300 m grid of 900 Sentinel-2 pixels centered on AIM plot centers).
Datasets in this release include the following items in associated zipped folders:
22 UAV orthoimages of which 19 have embedded clover maps aligned to Sentinel-2 imagery (MT1-6_Sentinel_proj and SD1-16_Sentinel_proj).
2 Classified NAIP images aligned to Sentinel-2 imagery. (MT/SD_NAIP_Sweet_Clover_Sentinel_proj)
15 Fractional cover maps for both central Montana and northwest South Dakota (MT/SD_Sweet_Clover_Fractional_Cover_Maps).
2 Point shapefiles of AIM plot centers and 2 polygon shapefiles for Sentinel-2 to UAV comparison extents (MT/SD_Sweet_Clover_Shapefiles).
Seven .csv files (Sweet_Clover_csv) that contain 1) Green Leaf Index reclassification values for UAV clover classifications (S1_GLI_Reclass.csv); 2) Clover cover from AIM surveys and UAV-derived clover maps along AIM transects and sites (S2_UAV_AIM_Comparisons.csv); 3) Center locations for all pixels used to classify clover with MaxEnt models (S3_Training.csv); 4) MaxEnt variable permutation importance values for NAIP classifications (S4_NAIP_PI.csv); 5) MaxEnt variable permutation importance values for Sentinel-2 classifications (S5_Sentinel_PI.csv); 6) Clover cover from Sentinel-2 FC models, AIM surveys, and UAV clover maps within appropriate comparison extents (S6_Sent_FC_AIM_UAV_Comparisons.csv); and 7) Frequency tables for Sentinel-2 FC classifications (S7_Frequency.csv).

Tags: biota, invasive-species, maximum-entropy-modeling, montana, remote-sensing, south-dakota, usgs-61ae6c3bd34eb622f69a729b
Organization: Department of the Interior