TY - JOUR
T1 - Using mixture-tuned match filtering to measure changes in subpixel vegetation area in Las Vegas, Nevada
AU - Brelsford, Christa
AU - Shepherd, Doug
N1 - Funding Information:
The authors thank Dr. Brian Munsky for helpful conversations on the application of mutual information to this problem. This work was supported through Los Alamos National Laboratory Directed Research and Development (LDRD).
PY - 2014/1
Y1 - 2014/1
N2 - In desert cities, accurate measurements of vegetation area within residential lots are necessary to understand drivers of change in water consumption. Most residential lots are smaller than an individual 30-m pixel from Landsat satellite images and have a mixture of vegetation and other land covers. Quantifying vegetation change in this environment requires estimating subpixel vegetation area. Mixture-tuned match filtering (MTMF) has been successfully used for subpixel target detection. There have been few successful applications of MTMF to subpixel abundance estimation because the relationship observed betweenMTMF estimates and ground measurements of abundance is noisy.We use a ground truth dataset over 10 times larger than that available for any previous MTMF application to estimate the bias between ground data and MTMF results.We find that MTMF underestimates the fractional area of vegetation by 5%to 10%and show that averaging over multiple pixels is necessary to reduce noise in the dataset.We conclude thatMTMF is a viable technique for fractional area estimation when a large dataset is available for calibration.When this method is applied to estimating vegetation area in Las Vegas, Nevada, spatial and temporal trends are consistent with expectations from known population growth and policy changes.
AB - In desert cities, accurate measurements of vegetation area within residential lots are necessary to understand drivers of change in water consumption. Most residential lots are smaller than an individual 30-m pixel from Landsat satellite images and have a mixture of vegetation and other land covers. Quantifying vegetation change in this environment requires estimating subpixel vegetation area. Mixture-tuned match filtering (MTMF) has been successfully used for subpixel target detection. There have been few successful applications of MTMF to subpixel abundance estimation because the relationship observed betweenMTMF estimates and ground measurements of abundance is noisy.We use a ground truth dataset over 10 times larger than that available for any previous MTMF application to estimate the bias between ground data and MTMF results.We find that MTMF underestimates the fractional area of vegetation by 5%to 10%and show that averaging over multiple pixels is necessary to reduce noise in the dataset.We conclude thatMTMF is a viable technique for fractional area estimation when a large dataset is available for calibration.When this method is applied to estimating vegetation area in Las Vegas, Nevada, spatial and temporal trends are consistent with expectations from known population growth and policy changes.
KW - Las Vegas
KW - Mixture-tuned match filtering
KW - Vegetation abundance
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U2 - 10.1117/1.JRS.8.083660
DO - 10.1117/1.JRS.8.083660
M3 - Article
AN - SCOPUS:84896963853
SN - 1931-3195
VL - 8
JO - Journal of Applied Remote Sensing
JF - Journal of Applied Remote Sensing
IS - 1
M1 - 083660
ER -