基于阈值分割的黑龙江省森林类型遥感识别

doi:10.18306/dlkxjz.2016.05.012

Abstract

Abstract:

Accurately identifying spatial distribution of forest is critically important for dynamic monitoring and sustainable management of forest resources. In this article, in order to acquire a spatially explicit forest cover classification based on the national forest inventory (NFI) statistics at the provincial scale, we developed an identification method using threshold values based on forest area from NFI statistics in 1999-2003 and the Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data in 2000 with a spatial resolution of 500 m for Heilongjiang Province. Based on the seasonal difference of Normalized Difference Vegetation Index (NDVI) of various forest types, threshold values between different forest types in satellite data were set using the NFI statistical data as criteria. Four forest types were differentiated: evergreen needleleaf, deciduous broadleaf, deciduous needleleaf, and mixed forests. Due to the stratified random sampling method used in this study and reliable threshold identification, the accuracy assessment result shows that the spatial pattern of forest cover classifications is highly consistent with the ground reference map, with an overall classification accuracy of 78.1%. Specifically, the applied method resulted in higher classification accuracy for deciduous forests that have distinct seasonal variations of NDVI (with user accuracy above 80%). The study provides a practical method for spatially explicit forest coverage estimation, and for quantifying changes in biomass and carbon stock in the ecosystem at the regional scale based on several periods of NFI statistics and remote sensing data.

Key words: forest inventory, remote sensing, threshold, accuracy assessment, Heilongjiang Province

Yujuan YUAN, Yunhe YIN, Erfu DAI, Ronggao LIU, Shaohong WU. Forest cover classification based on remote sensing threshold consistent with statistics in Heilongjiang Province[J].PROGRESS IN GEOGRAPHY, 2016, 35(5): 655-663.

Figures/Tables 6

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遥感识别结果	地面参照(栅格数)				总计(栅格数)	制图精度/%
遥感识别结果	落叶阔叶林			落叶针叶林	常绿针叶林	混交林
落叶阔叶林	176	18	4	18	216	81.5
落叶针叶林	21	116	6	2	145	80.0
常绿针叶林	2	4	29	6	41	70.7
混交林	18	6	2	61	87	70.1
总计	217	144	41	87	489	78.1

森林分类	本文结果			MCD12Q1
森林分类	面积/万hm²		比例/%	面积/万hm²	比例/%
落叶阔叶林(DBF)	1198.22	66.86		493.06	28.84
落叶针叶林 (DNF)	394.87	22.04		113.50	6.64
常绿针叶林 (ENF)	83.82	4.68		8.19	0.48
混交林(MF)	115.13	6.42		1094.36	64.03
常绿阔叶林 (EBF)	0.00	0.00		0.10	0.01

Forest cover classification based on remote sensing threshold consistent with statistics in Heilongjiang Province

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