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Remote Sensing for Land & Resources    2020, Vol. 32 Issue (2) : 73-80     DOI: 10.6046/gtzyyg.2020.02.10
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Object-oriented rapid forest change detection based on distribution function
Linyan FENG1,2, Bingxiang TAN1,2(), Xiaohui WANG1,2, Xinyun CHEN3, Weisheng ZENG3, Zhao QI1,2
1. Research Institute of Forest Resources Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
2. Key Laboratory of Forestry Remote Sensing and Information System, NFGA, Beijing 100091, China
3. Academy of Forest and Grassland Inventory and Planning, Beijing 100714, China
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Abstract  

Plantation in southern China is growing rapidly, and rotation cutting period is short. To explore the forest change detection method used to update the forest resource database effectively and to monitor the dynamic changes in forest harvesting and renewal in a short period, the authors chose the plantation area of Shangsi County in Guangxi as the study area, where the plantation area changes frequently and rapidly and the change patterns are numerous and small. The GF-2 remote sensing images of two phases were used as data sources. Multi-scale segmentation and spectral difference segmentation were used to segment the two-phase images. The change areas and change types were extracted from the NDVI difference of the objects and the threshold value was determined based on the distribution function, so as to realize the rapid detection of forest change. In addition, the same method was adopted for pixel-based processing in comparison with object-oriented NDVI difference method. The results show that the overall accuracy of the object-oriented NDVI difference method is 87.12%, and the Kappa coefficient is 0.81. The accuracy and extraction effect are better than those of the pixel-based NDVI difference method, indicating that the object-oriented NDVI difference method can better depict the shape and boundary of the change spots and can also more accurately detect the small change area. This method can be adapted to detect the changing characteristics of plantation in south China and can also be used to update the forest resource database for the purpose of rapid change detection.
Keywords GF-2      change detection      NDVI      object-oriented      distribution function     
:  TP79  
Corresponding Authors: Bingxiang TAN     E-mail: tan@ifrit.ac.cn
Issue Date: 18 June 2020
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Linyan FENG
Bingxiang TAN
Xiaohui WANG
Xinyun CHEN
Weisheng ZENG
Zhao QI
Cite this article:   
Linyan FENG,Bingxiang TAN,Xiaohui WANG, et al. Object-oriented rapid forest change detection based on distribution function[J]. Remote Sensing for Land & Resources, 2020, 32(2): 73-80.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020.02.10     OR     https://www.gtzyyg.com/EN/Y2020/V32/I2/73
Fig.1  Study area image and geographical location map
Fig.2  Technical roadmap
空间分辨率/m 最小可检测面积/hm2
250~1 000 6~100
10~30 0.05~0.30
0.5~5 0.01
Tab.1  Relationship between sensor spatial resolution and minimum detectable area
图层 分割方法 分割对象 分割尺度 形状参数 紧致度参数 对象数量/个
1 多尺度分割 全部像元 70 0.1 0.5 73 328
2 光谱差异分割 图层1 50 — — 69 134
Tab.2  Parameters of image segmentation
Fig.3  Segmentation results of images in local areas
Fig.4  Image statistics of NDVI difference of two methods
变化类型 面向对象判定条件 基于像元判定条件
植被变裸地 △Mi≤-0.085 △Mi≤-0.098
裸地变植被 △Mi≥0.18 △Mi≥0.148
未变化 -0.085<△Mi<0.18 -0.098<△Mi<0.148
Tab.3  Judgment conditions for each change type
Fig.5  Change detection results of two methods
检测
方法		 检测变化
类型		 实际变化类型 合计 漏分
误差/%		 错分
误差/%		 总体
精度/%		 Kappa系数
植被变裸地 裸地变植被 未变化
面向对象NDVI差值法 植被变裸地 19 121 755 1 465 21 341 9.80 10.40
裸地变植被 0 12 589 5 082 17 671 10.91 28.76 89.76 0.81
未变化 2 078 787 57 314 60 179 10.25 4.76
基于像元NDVI差值法 植被变裸地 17 590 596 1 363 19 549 17.02 10.02
裸地变植被 0 12 813 6 484 19 297 9.33 33.60 87.12 0.76
未变化 3 609 722 56 014 60 345 12.29 7.18
合计 21 199 14 131 63 861 99 191
Tab.4  Change detection results of the confusion matrix
Fig.6  Local change detection results
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