HR: 1340h
AN: B13D-1532    [Abstracts]
TI: Developing Cropped Area Estimates for Niger From Multi-sensor Satellite Imagery
AU: * Husak, G J
EM: husak@geog.ucsb.edu
AF: UCSB Department of Geography, Department of Geography University of California, Santa Barbara, CA 93106,
AU: Marshall, M
EM: marshall@geog.ucsb.edu
AF: UCSB Department of Geography, Department of Geography University of California, Santa Barbara, CA 93106,
AU: Funk, C
EM: chris@geog.ucsb.edu
AF: UCSB Department of Geography, Department of Geography University of California, Santa Barbara, CA 93106,
AU: Pedreros, D
EM: pedreros@usgs.gov
AF: UCSB Department of Geography, Department of Geography University of California, Santa Barbara, CA 93106,
AU: Michaelsen, J
EM: joel@geog.ucsb.edu
AF: UCSB Department of Geography, Department of Geography University of California, Santa Barbara, CA 93106,
AU: Harrison, L
EM: harrison@geog.ucsb.edu
AF: UCSB Department of Geography, Department of Geography University of California, Santa Barbara, CA 93106,
AB: Estimates of cropped area in developing countries can be critical in determining allocation of food aid. However, these countries frequently lack the resources or infrastructure to perform adequate national assessments. In these instances the use of remote sensing can provide estimates over wide areas which may be difficult to survey in person. This study uses manual interpretation of multi-resolution satellite imagery as the primary inputs to creating a national estimate of cropped area in Niger. A nationwide set of regular grid points at a 2-km interval covering most potential crop-growing areas of Niger provides comprehensive analysis of cropped area. A secondary set of samples consists of points on a regular grid at a 500-m interval for select regions. Spatially comprehensive samples are interpreted using Landsat ETM data from the Landsat7 satellite. Imagery covering late-season and post-harvest periods were selected to maximize contrast between crop and non-crop areas, as well as increase the likelihood of obtaining cloud-free imagery. Nearly 150,000 points were classified using the LCmapper tool developed at EROS Data Center. Secondary sampling units are designed to relate primary samples to actual ground cover. These samples are interpreted using 1m Quickbird or IKONOS satellite imagery and serve as the groundtruth for this study, relating the actual cropped area over small areas. The images were selected to provide a representative sample of the landscape. The goal of the secondary samples is to establish a bias correction. Because the bias may be dependent on farming practices, crop type, crop phenology or many other characteristics it is necessary to select regions which will have a consistent bias. Relating the crop percentage found using the moderate-resolution Landsat data to the percentage using the high-resolution interpretations is the critical piece of this research. This study uses FEWS NET livelihood zones, which are consistent with climatologic gradients, but also incorporate sociological components. Combining this information with physical parameters such as slope and elevation it is possible to unbias the estimates based on Landsat interpretations. National estimates based on the satellite estimates confirm existing estimates of cropped area. Uncertainty in the national estimate is conveyed by the standard error of the modeling phase of this research. Distributing the cropped area according to the remotely sensed data highlights areas with large potential production.
DE: 0402 Agricultural systems
DE: 0480 Remote sensing
SC: Biogeosciences [B]
MN: 2007 Fall Meeting