TY - JOUR
T1 - Classification of coffee-forest landscapes using landsat TM imagery and spectral mixture analysis
AU - Schmitt-Harsh, Mikaela
AU - Sweeney, Sean P.
AU - Evans, Tom P.
PY - 2013/5
Y1 - 2013/5
N2 - This research applies linear spectral mixture analysis (LSMA) to a Landsat TM image, and assesses the value of fraction images (green vegetation, shade, soil) and the thermal band (TM-B6) in discriminating shade-grown coffee systems from forests. Four combinations of TM bands and fraction images were compared, and a maximum likelihood algorithm was used to classify five land cover classes: high-density woodlands, low-density woodlands, coffee agroforests, crop / pasturelands, and urban settlements. The classification accuracy of each model combination was assessed using both Kappa analyses and quality and allocation disagreement parameters. Results indicate improvements to classification accuracies following inclusion of TM-B6 and fraction images as inputs to the classification; however, only the use of TM-B6 led to significant improvements at the 95 percent confidence level. The highest classification accuracy achieved was 86 percent (Kstandard = 0.82), with producer's and user's accuracy of coffee agroforests reaching 89 percent and 90 percent, respectively, an improvement over previous research aimed at spectrally distinguishing coffee from other woody cover types.
AB - This research applies linear spectral mixture analysis (LSMA) to a Landsat TM image, and assesses the value of fraction images (green vegetation, shade, soil) and the thermal band (TM-B6) in discriminating shade-grown coffee systems from forests. Four combinations of TM bands and fraction images were compared, and a maximum likelihood algorithm was used to classify five land cover classes: high-density woodlands, low-density woodlands, coffee agroforests, crop / pasturelands, and urban settlements. The classification accuracy of each model combination was assessed using both Kappa analyses and quality and allocation disagreement parameters. Results indicate improvements to classification accuracies following inclusion of TM-B6 and fraction images as inputs to the classification; however, only the use of TM-B6 led to significant improvements at the 95 percent confidence level. The highest classification accuracy achieved was 86 percent (Kstandard = 0.82), with producer's and user's accuracy of coffee agroforests reaching 89 percent and 90 percent, respectively, an improvement over previous research aimed at spectrally distinguishing coffee from other woody cover types.
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U2 - 10.14358/PERS.79.5.457
DO - 10.14358/PERS.79.5.457
M3 - Article
AN - SCOPUS:84877041067
SN - 0099-1112
VL - 79
SP - 457
EP - 468
JO - Photogrammetric Engineering and Remote Sensing
JF - Photogrammetric Engineering and Remote Sensing
IS - 5
ER -