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Remote Sensing for Natural Resources    2023, Vol. 35 Issue (1) : 180-188     DOI: 10.6046/zrzyyg.2022016
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Forest classification for Motuo County: A method based on spatio-temporal-spectral characteristics
WU Yuxin1,2(), WANG Juanle2,4(), HAN Baomin1, YAN Xinrong2,3
1. School of Architecture Engineering, Shandong University of Technology, Zibo 255000, China
2. State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
3. University of Chinese Academy of Sciences, Beijing 100049, China
4. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
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Abstract  

The forest area of Tibet ranks among the top in China, and the forest resources in Tibet play an important role in water conservation and ecological service. Therefore, it is of great significance to assess the assets of forest natural resources in this region. However, existing products and statistical data related to forest cover fail to meet the demands for the assessment of forest natural resource assets in this region, and it is necessary to explore a fine-scale forest classification method suitable for this region. Based on the cloud computing platform Google Earth Engine (GEE), this study constructed the temporal, spatial, spectral, and auxiliary feature sets of the forest coverage in Motuo County using the Landsat8 remote sensing images of 2015 and 2020, as well as field survey data, and the basic geographic data. Then, it conducted forest classification using the random forest (RF) and classification and regression tree (CART) algorithms. As indicated by the accuracy evaluation of the assessment results obtained using the two algorithms, the forest classification results of 2015 and 2020 obtained using the RF algorithm had relatively high accuracy, with overall classification accuracy of 0.88 and 0.87, respectively and Kappa coefficients of both greater than 0.8. The analyses of the areal and spatio-temporal characteristics of forest classification results show that: ① Motuo County had a total forest area of 34 000 km2 in 2015, with a forest cover rate of up to 84.63%, which was 2% less than that in 2020; ② The forest resources in Motuo County are dominated by broadleaved forests, which are mainly distributed in Yarlung Zangbo Grand Canyon and low-altitude areas and accounted for 72.27% and 75.37% of the total forest area in 2015 and 2020, respectively. Coniferous forests accounted for 25.96% and 23.19% of the total forest area in 2015 and 2020, respectively and are concentrated in high-altitude areas, such as the Namcha Barwa and Gyala Peri peaks. This study determined the spatio-temporal distribution of the forests in Motuo County in 2015 and 2020 by developing a spatio-temporal-spectral classification method. It can provide a reference method for calculating specific forest cover indices SDGs and fill the gap of forest data of small zones. The obtained monitoring data will provide data support for the natural asset assessment and ecological function evaluation in Motuo County.
Keywords forest classification      spatio-temporal-spectral characteristics      random forest      classification and regression tree      Motuo County     
ZTFLH:  S771.8  
Issue Date: 20 March 2023
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Yuxin WU
Juanle WANG
Baomin HAN
Xinrong YAN
Cite this article:   
Yuxin WU,Juanle WANG,Baomin HAN, et al. Forest classification for Motuo County: A method based on spatio-temporal-spectral characteristics[J]. Remote Sensing for Natural Resources, 2023, 35(1): 180-188.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022016     OR     https://www.gtzyyg.com/EN/Y2023/V35/I1/180
森林数据集 空间分
辨率/m		 时间分辨率 对森林的分类 无法满足自然资源资产评价的原因
Global Forest Watch 30 树木覆盖: 2000年/2010年
树木增加: 2000—2012年
树木减少: 2000—2017年		 常绿针叶林、常绿阔叶林、落叶针叶林、落叶阔叶林与混交林 从全球尺度出发,在中国区域应用时存在明显的分类不一致
PALSAR-2/PALSAR forest /Non-Forest Map 25 2007—2010年 森林 其分类体系依然存在划分单一,存在没有对森林类型进行细分
GlobeLand30 30 2000年/2010年/2020年 森林 其对林地的划分依然较粗,森林子集部分仅包含一种类型
GlobCover 300 1992—2015年 常绿针叶林、常绿阔叶林、落叶针叶林、落叶阔叶林与混交林 空间分辨率太大,无法各种森林类型的精细化管理
FROM-GLC 30 2010年/2015年/2017年 林地 没有对森林类型进行细分,中国区域应用时,存在着一些缺陷
Forest Map for China 30 2010年 常绿针叶林、常绿阔叶林、落叶针叶林、落叶阔叶林与混交林 与所需年份时间不同
《林芝地区统计年鉴》(2015年) 1986—2014年 林芝地区的林业生产情况、墨脱县林业产值总数据 缺少县级的森林统计数据
Tab.1  Main land cover products, forest cover thematic products and statistical data products
Fig.1  Forest classification process based on GEE platform
类型 指标
时间特征 目标年份相邻年份同一时期的影像选择
空间特征 con, cor, ent, var
光谱特征 blue, green, red, NIR, NDVI, EVI, NDWI, MSAVI
辅助特征 elevation, aspect, slope
Tab.2  Feature set of forest remote sensing classification
类别 2015年 2020年
RF算法 CART算法 RF算法 CART算法
总体精度 0.88 0.82 0.87 0.82
Kappa系数 0.83 0.74 0.81 0.74
Tab.3  GEE-based remote sensing classification accuracy evaluation of Forest resources in Motuo County
Fig.2  Forest distribution in Motuo County in 2015 and 2020
森林资源 2015年/km2 2020年/km2 变化量/km2 变化率/%
针叶林 7 470.08 6 539.40 -930.68 -12.46
阔叶林 20 794.67 21 240.65 445.98 2.14
针阔混交林 510.35 418.00 -92.35 -18.10
Tab.4  Forest resources in Motuo County in 2015 and 2020
Fig.3  Changes in forest interior and non-forest land
森林类型 针叶林 阔叶林 针阔混交林 非林地
针叶林 5 106.60 532.91 126.34 1 704.24
阔叶林 336.6 318 983.02 164.75 1 310.26
针阔混交林 202.13 159.98 98.36 49.88
非林地 894.04 1 564.74 28.55 3826.65
Tab.5  Forest resource type transfer matrix of Motuo County from 2015 to 2020(km2)
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