TY - JOUR
T1 - Using multicriterion optimization to classify ecosystems from multitemporal imagery
AU - Ridgley, Mark A.
AU - Aerts, Jeroen C.J.H.
PY - 1996
Y1 - 1996
N2 - One element of research on climate change is modeling of the non-fossil carbon cycle. Non-fossil carbon models such as IMAGE-2 use ecosystem maps as inputs for calculating the production of carbon and the levels of carbon in the atmosphere. The NFOSEUR project aims to classify European ecosystems from remote sensing (RS) data. The data consist of monthly NDVI values for each year, where NDVI is a measure of the photosynthetic activity of the ecosystems. Each ecosystem has a characteristic NDVI curve throughout the year, yielding a characteristic “fingerprint” for each ecosystem. Until now, classification of RS data has been based on various statistical procedures. This paper discusses the use of multicriterion optimization to determine the “best” classification. Goal programming and compromise goal programming models yield classifications that compare closely with those produced by conventional statistical approaches. Case studies include a classification of ecosystems for Germany and southern France. Various potential improvements to our models are discussed, including new formulations and the prospects for fuzzy linear and goal programming models.
AB - One element of research on climate change is modeling of the non-fossil carbon cycle. Non-fossil carbon models such as IMAGE-2 use ecosystem maps as inputs for calculating the production of carbon and the levels of carbon in the atmosphere. The NFOSEUR project aims to classify European ecosystems from remote sensing (RS) data. The data consist of monthly NDVI values for each year, where NDVI is a measure of the photosynthetic activity of the ecosystems. Each ecosystem has a characteristic NDVI curve throughout the year, yielding a characteristic “fingerprint” for each ecosystem. Until now, classification of RS data has been based on various statistical procedures. This paper discusses the use of multicriterion optimization to determine the “best” classification. Goal programming and compromise goal programming models yield classifications that compare closely with those produced by conventional statistical approaches. Case studies include a classification of ecosystems for Germany and southern France. Various potential improvements to our models are discussed, including new formulations and the prospects for fuzzy linear and goal programming models.
KW - Classification
KW - Ecosystems
KW - Multiple-objective optimization
KW - Remote sensing
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U2 - 10.1080/02723646.1996.10642586
DO - 10.1080/02723646.1996.10642586
M3 - Article
AN - SCOPUS:0030317601
VL - 17
SP - 282
EP - 293
JO - Physical Geography
JF - Physical Geography
SN - 0272-3646
IS - 3
ER -