Collating multisource geospatial data for vegetation detection using Bayesian network - a case study of Yellow River Delta. (2017)

• Record Type: Journal Article
• Authors: Mo, Dingyuan; Yu, Liangju; Gao, Meng
• Is Part Of: International journal of computational science and engineering. Volume 15:Number 3/4(2017)
• Page Start: 277
• Page End: 284
• Publication Date: 2017
• Subjects: GIS -- normalised difference vegetation index -- NDVI -- human activity -- oil exploitation -- urbanisation -- road construction -- Bayesian network; Computer science -- Mathematics -- Periodicals; Computer simulation -- Mathematical aspects -- Periodicals; Computational intelligence -- Periodicals; 004.015105
• Languages: English
• ISSNs: 1742-7185

Abstract

Multisource geospatial data contains a lot of information that can be used for environment assessment and management. In this paper, four environmental indicators representing typical human activities in Yellow River Delta, China are extracted from multisource geospatial data. By analysing the causal relationship between these human-related indicators and NDVI, a Bayesian network (BN) model is developed. Part of the raster data pre-processed using GIS is used for training the BN model, and the other data is used for model testing. Sensitivity analysis and performance assessment showed that the BN model was good enough to reveal the impacts of human activities on land vegetation. With the trained BN model, the vegetation change under three different scenarios was also predicted. The results showed that multisource geospatial data could be successfully collated using the GIS-BN framework for vegetation detection.