基于多源数据集的森林损失评估研究

doi:10.13466/j.cnki.lczyyj.2024.06.004

摘要/Abstract

摘要：

精确评估森林损失对于遏制森林面积减少、保证森林生态系统服务供给至关重要。系统梳理森林损失评估领域的研究进展,将现有的数据集分为森林资源清查数据集和卫星遥感影像数据集,深入分析这两类数据集的优缺点、局限性及其评估效能。提出3种改进策略:1)对数据集进行内生优化,提高其评估精度;2)建议采用数据集融合方法,整合多种数据集的优势,实现优势互补;3)引入地理信息数据,增强森林损失评估的全面性与准确性。全面审视森林损失评估数据集不仅丰富了该研究领域的理论基础,还为森林资源的高效保护和管理提供了科学依据。

关键词: 森林损失, 森林资源评估, 森林资源清查, 卫星遥感, 森林保护

Abstract:

Accurate assessment of forest loss is essential to effectively curb forest area loss and guaranteeing the provision of forest ecosystem services.This research systematizes the research progress of forest loss assessment and categorizes principal datasets into forest inventory datasets and satellite remote sensing image datasets.A comprehensive analysis of these datasets reveals the advantages and disadvantages and evaluates the assessment efficacy of different datasets by comparing their limitations as indicated in current studies.Based on these findings,three optimization strategies are proposed.1)Endogenous optimization of the dataset to improve its assessment accuracy.2)Employing dataset fusion techniques to integrate the strengths of multiple datasets for complementary benefits.3)Incorporating geographic information data to enhance the comprehensiveness and accuracy of forest loss assessment.A comprehensive review of datasets for forest loss assessment not only augments the theoretical framework of this research domain but also lays the scientific foundation for efficient conservation and management of forest resources.

Key words: forest loss, forest resource assessment, forest inventory, satellite remote sensing, forest protection

中图分类号:

S718.55

蔡颖莉, 朱洪革. 基于多源数据集的森林损失评估研究[J]. 林草资源研究, 2024,(6): 27-34.

CAI Yingli, ZHU Hongge. Research on Forest Loss Assessment Based on Multi-Source Datasets[J]. Forest and Grassland Resources Research, 2024,(6): 27-34.

图/表 2

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数据集类型	代表数据集	优点	缺点
森林资源清查数据集	FRA、各国森林资源清查数据集	1)数据详细具体 2)信息易获得,公开透明 3)数据标准和方法统一,便于比较	1)时间滞后,更新周期较长 2)数据精度相对较低,难以捕捉小规模变化 3)成本较高
卫星遥感影像数据集	GFC数据集、MODIS数据集、Sentinel系列数据	1)覆盖广泛,提供广泛的地理空间信息 2)高分辨率,能捕捉细致变化 3)时间序列数据,可用于长期监测 4)部分数据可免费获取,成本较低	1)受天气因素影响,可能导致数据缺失 2)自动分类算法在复杂地区易误分类 3)需要专业知识进行图像处理和解释

研究尺度	研究区域	数据来源	研究结论	相关文献
宏观	非洲、拉丁美洲、南亚和东南亚	Landsat和MODIS数据	非洲、东南亚和中美洲等地区的森林损失动态比巴西和东南亚岛屿更精细,基于样本的森林损失估计高于基于地图的估计	Tyukavina等^[40]
	中国	森林资源清查和MODIS 的数据	统计年鉴和MODIS森林覆盖率的空间分布具有较高的一致性,但个别省(区、市)年鉴数据森林面积增加量明显高于MODIS的变化	韩云环等^[18]
	巴西	SOSMA和GFC数据	GFC数据反映的森林损失面积明显高于巴西官方绘制的SOSMA地图,且GFC数据存在植树造林、森林更新和草地的分类错误	Andreacci等^[38]
微观	巴西的阿克里省和朗多尼亚省	GFC、PRODES和FORMA数据	政府统计的森林损失数据要显著低于GFC数据,但GFC数据对小范围(<2 hm²)森林损失也会产生严重低估	Milodowski等^[41]
微观	中国西南地区	GFC、数字高程模型(Digital Elevation Model,DEM)、航天飞机雷达地形测绘任务数据(Shuttle Radar Topography Mission,SRTM)和更精细的全球土地覆盖观测和监测数据(Finer Resolution Observation and Monitoring of Global Land Cover,FROM-GLC)	国家森林资源清查的时效性不强,森林损失现象逐渐向海拔较低、坡度较缓的地方转移	王淑静等^[2]