Devising a new Digital Vegetation Model for eco-climatic analysis in Africa using GIS and NOAA AVHRR data

• Published in: International journal of remote sensing Vol. 24; no. 18; pp. 3611 - 3633
• Main Authors: Nonomura, A, K., Sanga-ngoie, Fukuyama, K.
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis Group 01.01.2003; Taylor and Francis
• Subjects: Animal, plant and microbial ecology; Applied geophysics; Biological and medical sciences; Earth sciences; Earth, ocean, space; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Internal geophysics; Teledetection and vegetation maps; altitude; geomorphology; rainfall; very high resolution; atmospheric precipitation; NOAA; multispectral remote sensing; Space remote sensing; Africa; geographic information systems; vegetation; cartography; classification; monitoring; forests; principal components analysis; annual variations; climate modification; numerical models; ecosystems; radiometry; temperature; seasonal variations; land cover
• ISSN: 0143-1161; 1366-5901
• DOI: 10.1080/0143116021000053779

Large-scale land cover information is a crucial input in numerical climatic change modelling. Traditionally, this information is encoded as vegetation maps based on ground surveys made at selected points, and is of poor resolution. Nowadays, improvements in land cover mapping/monitoring are obtained by using the Normalized Difference Vegetation Index (NDVI) calculated from NOAA Advanced Very High Resolution Radiometer (AVHRR) satellite sensor data. In this paper, a Digital Vegetation Model (DVM) was derived in an attempt to get an accurate and upgradable digital image of African land cover types, using 1985-1991 NOAA AVHRR data. First, the main seasonal features of Africa are extracted from the NDVI data. Then (unsupervised) classification, using only the principal components analysis (PCA) first components of the NOAA multi-spectral data (the visible channels 1 and 2, and the thermal channel 4) on the IDRISI32 GIS platform, yields our macro-scale seven-category African DVM, consistent with Köppen climatic classification. Subsequent reclassifications, referring to known African sub-regional or local features (vegetation, eco-region maps, altitude, geomorphology), finally lead us to our fine-tuned regional-scale 30 category DVM. Including channel 4 data in our scheme has been pivotal for allowing full-scale classification from dense forests to sparsely or non-vegetated areas. Usually, this is barely possible in NDVI-only classifications.