Forestland change detection based on spectral and texture features

doi:10.13474/j.cnki.11-2246.2019.0269

Abstract

Abstract: The investigation of forest land change can provide accurate spatial information and attribute information for forest law enforcement supervision and forest land "one map" renewal, which is of great significance for forest resources monitoring and management. In view of the time-consuming and laborious situation of large-scale multi-temporal remote sensing images, this paper presents a method of forest land change detection based on spectral and texture features. Taking the northeastern part of Lingshan County as an example, the GF-2 remote sensing images of 20171209 and 20180201 are used to carry out experiments. The results show that, on the basis of reducing manpower input and time cost, this method not only improves the detection efficiency of remote sensing image by more than half, but also achieves more than 77% detection accuracy. This method has certain application value in forest resources census.

Key words: principal component analysis, maximum likelihood method, normalized difference vegetation index, forestland change, change detection

CLC Number:

P237

MEI Shuhong, FAN Chengcheng, LIAO Yongsheng, LI Yaran, SHI Yujun, MAI Chao. Forestland change detection based on spectral and texture features[J]. 测绘通报, 2019, 0(8): 140-143.

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