西南三省1986－2018年森林覆盖时空动态及驱动过程分析

doi:10.16258/j.cnki.1674-5906.2026.06.007

生态环境学报 ›› 2026, Vol. 35 ›› Issue (6): 898-908.DOI: 10.16258/j.cnki.1674-5906.2026.06.007

西南三省1986－2018年森林覆盖时空动态及驱动过程分析

罗曼秋¹^,²^,³(), 童晓伟¹^,²^,^*(), 岳跃民¹^,², 常静怡¹^,²^,³, 祁向坤¹^,², 王克林¹^,²

¹ 中国科学院亚热带农业生态研究所，湖南长沙 410125
² 中国科学院环江喀斯特生态系统观测研究站/广西喀斯特生态过程与服务重点实验室，广西环江 547100
³ 中国科学院大学，北京 100049

收稿日期:2025-10-11 修回日期:2026-01-21 接受日期:2026-03-07 出版日期:2026-06-18 发布日期:2026-06-08
通讯作者: * 童晓伟，E-mail: tongxiaowei@isa.ac.cn
作者简介:罗曼秋（2001年生），女，硕士研究生，主要从事生态遥感方面的研究。E-mail: luomanqiu23@mails.ucas.ac.cn
基金资助:
国家重点研发计划青年科学家专项(2023YFF1305700);国家自然科学基金项目(42371129);湖南省科技创新项目(2024RC1067)

Spatio-temporal Forest Cover Dynamics and Driving Processes in Southwest China (1986‒2018)

LUO Manqiu¹^,²^,³(), TONG Xiaowei¹^,²^,^*(), YUE Yuemin¹^,², CHANG Jingyi¹^,²^,³, QI Xiangkun¹^,², WANG Kelin¹^,²

¹ Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha 410125, P. R. China
² Guangxi Key Laboratory of Karst Ecological Processes and Services/Huanjiang Observation and Research Station for Karst Ecosystems, Chinese Academy of Sciences, Huanjiang 547100, P. R. China
³ University of Chinese Academy of Sciences, Beijing 100049, P. R. China

Received:2025-10-11 Revised:2026-01-21 Accepted:2026-03-07 Online:2026-06-18 Published:2026-06-08

摘要/Abstract

摘要：

森林生态系统是西南地区生态安全和碳循环的重要基础，深入理解其覆盖的时空动态及驱动机制对区域森林可持续发展具有重要意义。基于1986－2018年Landsat数据，从森林覆盖变化的整体视角出发，将森林损失视为覆盖变化过程中最敏感、最直接的扰动信号，结合土地利用转换矩阵和空间热点分析，系统揭示西南三省森林覆盖的时空演变特征及其驱动因素。结果显示，1）1986－2018年间，研究区累计森林损失面积达1.77×10⁴ km²，呈“先升后降”的倒U型动态，森林损失主要集中在1996－2004年（约7.90×10³ km²）。森林损失以小尺度零散斑块为主，斑块面积小于0.01 km²的数量占95.9%，且损失区域海拔和坡度整体呈向低海拔及缓坡转移趋势。2）1996－2004年间，云南损失面积最大（3.68×10³ km²），其次为广西（3.30×10³ km²）和贵州（918 km²）；森林损失具有显著的空间集聚性，热点区由云南逐渐向广西转移。3）约90.4%的老林损失发生在非保护区，保护区内老林损失率显著低于非保护区，“天然林保护工程”实施后研究区老林损失显著减少。4）土地利用变化分析显示约80%的森林损失区域在后续时期实现再生，剩余主要转为矮小植被和建筑用地，贵州和广西森林损失区向建筑用地转化比例较高，体现城镇化驱动效应，云南则以向矮小植被转化为主。研究结果可为西南地区森林资源监测、老林保护政策制定及生态恢复提供科学依据。

关键词: 西南地区, 森林损失, 土地利用转换, 热点分析, 遥感监测

Abstract:

Forest ecosystems are a critical foundation for ecological security and carbon cycling in southwest China. A comprehensive understanding of the spatiotemporal dynamics and driving mechanisms of forest cover is essential for promoting regional forest sustainability. Based on Landsat data from 1986 to 2018, combined with land-use transition matrices and spatial hotspot analysis, this study systematically investigates the spatiotemporal patterns and drivers of forest cover across three provinces in southwest China. The results indicate that, 1) between 1986 and 2018, the region experienced a cumulative forest cover loss of 17673.81 km², following an inverted U-shaped trend with a peak during 1996‒2004 (approximately 7900.13 km²). Forest loss was dominated by small and fragmented patches, with 95.9% smaller than 0.01 km², and shifted toward lower elevations and gentler slopes. 2) During 1996-2004, Yunnan Province experienced the largest forest loss (3685 km²), followed by Guangxi (3297 km²) and Guizhou Province (918 km²). Forest loss exhibited significant spatial clustering, with hotspots shifting from Yunnan to Guangxi. 3) Approximately 99% of old-growth forest loss occurred outside protected areas, where the loss rate was significantly higher than within protected areas. The implementation of the Natural Forest Protection Program markedly reduced old-growth forest loss in the region. 4) Land-use change analysis revealed that about 80% of forest loss areas underwent subsequent regeneration, while the remainder was primarily converted to shrubland and built-up land. Forest loss areas in Guizhou and Guangxi showed higher conversion rates to built-up land, reflecting urbanization-driven impacts, whereas Yunnan was characterized by conversion to shrubland. These findings provide a scientific basis for forest monitoring, old-growth forest conservation, and ecological restoration in southwest China.

Key words: southwest region, forest loss, land use changes, hotspot analysis, remote sensing

中图分类号:

Q948
X173

罗曼秋, 童晓伟, 岳跃民, 常静怡, 祁向坤, 王克林. 西南三省1986－2018年森林覆盖时空动态及驱动过程分析[J]. 生态环境学报, 2026, 35(6): 898-908.

LUO Manqiu, TONG Xiaowei, YUE Yuemin, CHANG Jingyi, QI Xiangkun, WANG Kelin. Spatio-temporal Forest Cover Dynamics and Driving Processes in Southwest China (1986‒2018)[J]. Ecology and Environmental Sciences, 2026, 35(6): 898-908.

图/表 13

图1 研究区地理位置示意图及森林分布基于自然资源部发布的审图号为GS（2024）0650号的标准地图制作；底图边界无修改

Figure 1 Location of the study area and distribution of plantation and secondary forests in the study area

图2 1986－2018年研究区森林损失面积变化

Figure 2 Forest lost changes in the study area from 1986 to 2018

图3 不同省份对研究区1996－2004年间森林损失贡献率变化

Figure 3 Contribution of forest loss from different provinces to the total forest loss in the study area in 1996?2004

图4 研究区森林损失斑块面积统计直方图

Figure 4 Histogram of forest loss patch size in the study area

图5 研究区逐年森林损失斑块个数及平均面积变化

Figure 5 Changes in the number and average size of forest loss patches in the study area in 1996?2004

图6 1996－2004年不同省份森林损失区平均海拔及坡度变化

Figure 6 Mean elevation and slope changes of forest loss in different areas in 1996?2004

图7 森林损失比例空间分布（1996－2004年）

Figure 7 Spatial distribution of forest loss percentage (1996?2004)

图8 1996－2004年森林损失冷热点区分布

Figure 8 Distribution of forest loss hotspots and coldspots in 1996?2004

图9 1996－2004年不同类型热点格网数量变化趋势

Figure 9 Temporal changes (1996-2004) in the number of grid cells by hotspot types

图10 1996－2004年不同省份老林损失面积变化

Figure 10 Changes of old-growth forest loss in different provinces in 1996?2004

图11 研究区保护区内老林损失

Figure 11 Old-growth forest loss in protected areas

表1 1996－2004年森林损失区转化后的土地利用类型统计

Table 1 Land use types after forest loss (1996?2004)

年份	土地利用类型/km²
年份	森林	矮小植被	建筑用地	水体	耕地	裸地	合计
1996	113.81	17.43	12.32	1.65	1.82	0.01	147.05
1997	126.10	18.42	13.04	2.34	2.04	0.02	161.95
1998	151.36	21.03	15.16	3.17	2.53	0.02	193.27
1999	191.41	24.38	18.54	4.63	3.04	0.03	242.02
2000	248.09	28.52	23.21	6.23	3.86	0.04	309.96
2001	314.88	34.36	28.20	7.78	4.46	0.04	389.73
2002	382.07	39.29	32.96	8.74	4.93	0.06	468.05
2003	433.14	42.03	35.17	9.12	5.14	0.05	524.65
2004	457.96	42.38	35.27	7.87	4.87	0.06	548.41
合计	2418.82	267.84	213.87	51.53	32.70	0.32	2985.09

图12 各省森林损失非再生区土地利用转化结构

Figure 12 Land use types of non-regenerated forest loss areas by province

参考文献 51

[1]	ALKAMA R, CESCATTI A, 2016. Biophysical climate impacts of recent changes in global forest cover[J]. Science, 351(6273): 600-604. DOI PMID
[2]	AUSTIN K G, SCHWANTES A, GU Y, et al., 2019. What causes deforestation in Indonesia?[J]. Environmental Research Letters, 14: 024007. DOI URL
[3]	BASTIN J F, FINEGOLD Y, GARCIA C, et al., 2019. The global tree restoration potential[J]. Science, 365(6448): 76-79. DOI URL
[4]	BO X, CHEN R S, AN L, et al., 2021. Telecoupling urbanization and mountain areas deforestation between 2000 and 2020: Evidence from Zhejiang Province, China[J]. Land Degradation & Development, 32(16): 4727-4739. DOI URL
[5]	BRANDT M, YUE Y M, WIGNERON J P, et al., 2018. Satellite-observed Major Greening and Biomass Increase in South China Karst During Recent Decade[J]. Earth’s Future, 6(7): 1017-1028. DOI URL
[6]	CADAVID-FLOREZ L, LABORDE J, MCLEAN D J, 2020. Isolated trees and small woody patches greatly contribute to connectivity in highly fragmented tropical landscapes[J]. Landscape and Urban Planning, 196: 103745. DOI URL
[7]	CHENG K, CHEN Y, XIANG T, et al., 2024. A 2020 forest age map for China with 30 m resolution[J]. Earth System Science Data, 16: 803-819. DOI URL
[8]	COHEN W B, YANG Z Q, HEALEY S P, et al., 2018. A LandTrendr multispectral ensemble for forest disturbance detection[J]. Remote Sensing of Environment, 205: 131-140. DOI URL
[9]	CURTIS P G, SLAY C M, HARRIS N L, et al., 2018. Classifying drivers of global forest loss[J]. Science, 361(6407): 1108-1111. DOI PMID
[10]	DING Z, LIU Y, WANG L C, et al., 2021. Effects and implications of ecological restoration projects on ecosystem water use efficiency in the karst region of Southwest China[J]. Ecological Engineering, 170: 106356. DOI URL
[11]	FENG Y, ZIEGLER A D, ELSEN P R, et al., 2021. Upward expansion and acceleration of forest clearance in the mountains of Southeast Asia[J]. Nature Sustainability, 4(10): 892-899. DOI
[12]	GELDMANN J, BARNES M, COAD L, et al., 2013. Effectiveness of terrestrial protected areas in reducing habitat loss and population declines[J]. Biological Conservation, 161: 230-238. DOI URL
[13]	GILHEN-BAKER M, ROVIELLO V, BERESFORD-KROEGER D, et al., 2022. Old growth forests and large old trees as critical organisms connecting ecosystems and human health: A review[J]. Environmental Chemistry Letters, 20: 1529-1538. DOI
[14]	GODAR J, GARDNER T A, TIZADO E J, et al., 2014. Actor-specific contributions to the deforestation slowdown in the Brazilian Amazon[J]. Proceedings of the National Academy of Sciences, 111(43): 15591-15596. DOI URL
[15]	HANSEN M C, POTAPOV P V, MOORE R, et al., 2013. High-Resolution Global Maps of 21st-Century Forest Cover Change[J]. Science, 342(6160): 850-853. DOI PMID
[16]	HANSEN M, POTAPOV P, PICKENS A, et al., 2022. Global land use extent and dispersion within natural land cover using Landsat data[J]. Environmental Research Letters, 17: 034050. DOI
[17]	HE X Y, SPRACKLEN D V, HOLDEN J, et al., 2025. Tropical montane forest loss dominated by increased 1-10 hectare-sized patches[J]. Environmental Research Letters, 20: 024039. DOI
[18]	JANA M, SAR N, 2016. Modeling of hotspot detection using cluster outlier analysis and Getis-Ord Gi* statistic of educational development in upper-primary level, India[J]. Modeling Earth Systems and Environment, 2: 60. DOI URL
[19]	JHA C S, GOPARAJU L, TRIPATHI A, et al., 2005. Forest fragmentation and its impact on species diversity: An analysis using remote sensing and GIS[J]. Biodiversity & Conservation, 14: 1681-1698.
[20]	JIANG Z C, LIAN Y Q, QIN X Q, 2014. Rocky desertification in Southwest China: Impacts, causes, and restoration[J]. Earth-Science Reviews, 132: 1-12. DOI URL
[21]	KENNEDY R E, YANG Z, COHEN W B, 2010. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — Temporal segmentation algorithms[J]. Remote Sensing of Environment, 114(12): 2897-2910. DOI URL
[22]	LAURANCE W F, CAROLINA USECHE D, RENDEIRO J, et al., 2012. Averting biodiversity collapse in tropical forest protected areas[J]. Nature, 489: 290-294. DOI
[23]	MAO C, TONG X W, BRANDT M, et al., 2025. How three decades of forestation has impacted forest fragmentation in southern China[J]. Remote Sensing, 17(11): 1922. DOI URL
[24]	MENG G, WU S R, YU Y T, 2025. Forestry policy effectiveness and performance evaluation in China - Quantitative study based on policy texts 1998-2020[J]. Forest Policy and Economics, 170: 103367. DOI URL
[25]	Food and Agriculture Organization of the United Nations, 2020. Global Forest Resources Assessment 2020 - Key findings[R]. Rome: FAO: 1-3.
[26]	NEPSTAD D, SOARES-FILHO B S, MERRY F, et al., 2009. The End of Deforestation in the Brazilian Amazon[J]. Science, 326(5958): 1350-1351. DOI URL
[27]	PRASETYO L B, DHARMAWAN A H, NASDIAN F T, et al., 2016. Historical forest fire occurrence analysis in Jambi province during the period of 2000-2015: Its distribution & land cover trajectories[J]. Procedia Environmental Sciences, 33: 450-459. DOI URL
[28]	PRĂVĂLIE R, 2018. Major perturbations in the Earth’s forest ecosystems. Possible implications for global warming[J]. Earth-Science Reviews, 185: 544-571. DOI URL
[29]	QIN Y W, XIAO X M, WIGNERON J P, et al., 2021. Carbon loss from forest degradation exceeds that from deforestation in the Brazilian Amazon[J]. Nature Climate Change, 11: 442-448. DOI
[30]	SHIMADA M, ITOH T, MOTOOKA T, et al., 2014. New global forest/non-forest maps from ALOS PALSAR data (2007-2010)[J]. Remote Sensing of Environment, 155: 13-31. DOI URL
[31]	SONG X P, HANSEN M C, STEHMAN S V, et al., 2018. Global land change from 1982 to 2016[J]. Nature, 560: 639-643. DOI
[32]	TONG X W, BRANDT M, YUE Y M, et al., 2018. Increased vegetation growth and carbon stock in China karst via ecological engineering[J]. Nature Sustainability, 1: 44-50. DOI
[33]	TONG X W, BRANDT M, YUE Y M, et al., 2023. Reforestation policies around 2000 in southern China led to forest densification and expansion in the 2010s[J]. Communications Earth & Environment, 4: 260.
[34]	WADE C M, AUSTIN K G, CAJKA J, et al., 2020. What Is Threatening Forests in Protected Areas? A Global Assessment of Deforestation in Protected Areas, 2001-2018[J]. Forests, 11(5): 539. DOI PMID
[35]	WANG K L, ZHANG C H, CHEN H S, et al., 2019. Karst landscapes of China: Patterns, ecosystem processes and services[J]. Landscape Ecology, 34: 2743-2763. DOI
[36]	WEI X X, LIU R G, LIU Y, 2023. Forest Change in China: A Review[J]. Chinese Geographical Science, 33(3): 489-502. DOI
[37]	ZHANG J Q, CORLETT R T, ZHAI D, 2019. After the rubber boom: good news and bad news for biodiversity in Xishuangbanna, Yunnan, China[J]. Regional Environmental Change, 19: 1713-1724. DOI
[38]	ZHANG K, DE ALMEIDA CASTANHO A D, GALBRAITH D R, et al., 2015. The fate of Amazonian ecosystems over the coming century arising from changes in climate, atmospheric CO₂, and land use[J]. Global Change Biology, 21(7): 2569-2587. DOI URL
[39]	包蕊, 李涛, 张欣怡, 等, 2021. 森林生态系统损害评估体系与管理制度研究[J]. 生态学报, 41(3): 924-933.
	BAO R, LI T, ZHANG X Y, et al., 2021. Study on the assessment and management system of forest ecosystem damage[J]. Acta Ecologica Sinica, 41(3): 924-933.
[40]	陈文洁, 陈阳, 夏江周, 2024. 林龄数据及其估算方法研究进展[J]. 遥感技术与应用, 39(5): 1039-1053. DOI
	CHEN W J, CHEN Y, XIA J Z, 2024. Review of forest age datasets and their estimation methods[J]. Remote Sensing Technology and Application, 39(5): 1039-1053.
[41]	李春干, 李振, 2021. 机载激光雷达大区域亚热带森林参数估测的普适性模型式[J]. 林业科学, 57(10): 23-35.
	LI C G, LI Z, 2021. Generalizing predictive models of sub-tropical forest inventory attributes using an area-based approach with airborne LiDAR data[J]. Scientia Silvae Sinicae, 57(10): 23-35.
[42]	吕莹莹, 钱者东, 王晔, 等, 2024. 中国市域自然保护区时空格局演变特征[J]. 生态学报, 44(10): 4021-4037.
	LÜ Y Y, QIAN Z D, WANG Y, et al., 2024. City-scale spatiotemporal evolution characteristics of nature reserves in China[J]. Acta Ecologica Sinica, 44(10): 4021-4037.
[43]	农胜奇, 张伟, 蔡会德, 2014. 1977-2010年广西森林资源变化动态及其主要驱动因素分析[J]. 广西林业科学, 43(2): 171-178.
	NONG S Q, ZHANG W, CAI H D, et al., 2014. The dynamic change and main driving factors of forest resources in Guangxi from 1977 to 2010[J]. Guangxi Forestry Science, 43(2): 171-178.
[44]	庞勇, 蒙诗栎, 史锴源, 等, 2021. 中国天然林保护工程区森林覆盖遥感监测[J]. 生态学报, 41(13): 5080-5092.
	PANG Y, MENG S L, SHI K Y, et al., 2021. Forest coverage monitoring in the Natural Forest Protection Project area of China[J]. Acta Ecologica Sinica, 41(13): 5080-5092.
[45]	王昊, 吕植, 顾垒, 等, 2015. 基于Global Forest Watch观察2000-2013年间中国森林变化[J]. 生物多样性, 23(5): 575-582. DOI
	WANG H, LÜ Z, GU L, et al., 2015. Observations of China’s forest change (2000-2013) based on Global Forest Watch dataset[J]. Biodiversity Science, 23(5): 575-582. DOI URL
[46]	王淑静, 2022. 近20年西南地区森林变化特征遥感监测及时空分析[D]. 重庆: 西南大学: 19-32.
	WANG S J, 2022. Remote sensing monitoring and temporal and spatial analysis of forest change characteristics in Southwest China in recent 20 years[D]. Chongqing: Southwest University: 19-32.
[47]	王旭熙, 彭立, 苏春江, 等, 2016. 基于景观生态安全格局的低丘缓坡土地资源开发利用——以四川省泸县为例[J]. 生态学报, 36(12): 3646-3654.
	WANG X X, PENG L, SU C J, et al., 2016. Development and utilization of low-slope hilly land resources based on a landscape security pattern theory: A case study in Luxian County, Sichuan Province[J]. Acta Ecologica Sinica, 36(12): 3646-3654.
[48]	余涛, 庞勇, 蒙诗栎, 等, 2023. 天然林资源保护工程区植被覆盖度遥感估算及变化分析[J]. 北京林业大学学报, 45(5): 1-13.
	YU T, PANG Y, MENG S L, et al., 2023. Remote sensing estimation and change analysis of fractional vegetation coverage in Natural Forest Resource Protection Project area[J]. Journal of Beijing Forestry University, 45(5): 1-13.
[49]	周彬, 蒋有绪, 臧润国, 2010. 西南地区天然林资源近60年动态分析[J]. 自然资源学报, 25(9): 1536-1546. DOI
	ZHOU B, JIANG T X, ZANG R G, et al., 2010. Resource dynamics of natural forests in southwest China in recent 60 years[J]. Journal of Natural Resources, 25(9): 1536-1546. DOI
[50]	周维, 刘国华, 段兴武, 等, 2022. 滇金丝猴分布区森林面积变化的时空特征及其影响因素[J]. 生态学报, 42(2): 791-803.
	ZHOU W, LIU G H, DUAN X W, et al., 2022. Spatio-temporal characteristics of forest area change and its determinants in the distribution area of Yunnan snub-nosed monkey (Rhinopithecus bieti)[J]. Acta Ecologica Sinica, 42(2): 791-803.
[51]	朱若柠, 沈文娟, 张亚丽, 等, 2019. 基于时间序列MODIS-VCF数据的云南省森林覆盖变化及破碎化分析[J]. 南京林业大学学报(自然科学版), 43(2): 184-190. DOI
	ZHU R N, SHEN W J, ZHANG Y L, et al., 2019. Assessing changes in forest coverage and forest fragmentation patterns in Yunnan Province from time series MODIS-VCF products (2000-2016)[J]. Journal of Nanjing Forestry University (Natural Sciences Edition), 43(2): 184-190.

西南三省1986－2018年森林覆盖时空动态及驱动过程分析

Spatio-temporal Forest Cover Dynamics and Driving Processes in Southwest China (1986‒2018)

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