Review on monitoring methods of the effects of forest changes on regional temperature based on multi-source remote sensing data

doi:10.12302/j.issn.1000-2006.202110041

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2022, Vol. 46 ›› Issue (3): 1-11.doi: 10.12302/j.issn.1000-2006.202110041

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Review on monitoring methods of the effects of forest changes on regional temperature based on multi-source remote sensing data

SHEN Wenjuan¹^,²(), JI Mei¹, LI Mingshi¹^,²

1. College of Forestry, Nanjing Forestry University, Nanjing 210037, China
2. Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China

Received:2021-10-22 Accepted:2022-03-20 Online:2022-05-30 Published:2022-06-10

Abstract

Abstract:

Human activities contribute to forest changes by altering land cover, promoting changes in carbon budget and surface energy balance, further affecting climate at global and regional scales. Current studies on the impact of forest changes on regional temperature are mainly focused on the simple statistical method combining forest change data with temperature data with limited accuracy, however, studies on the effects of forest changes with high reliability and its biophysical processes on regional temperature indicate that an accurate and comprehensive understanding of the biophysical interaction mechanisms between forest and climate can provide a scientific support for the comprehensive assessment of forest ecosystems. The author made a comprehensive analysis of various monitoring methods of forest changes combined with thier biophysical processes based on multi-source remote sensing on regional temperature, and found that: (1) the limited availability of high-resolution forest change data from multiple sources has been hindering the accurate quantification of the impact of temperature changes; (2) the evaluation of results about quantifying the impact of the biophysical mechanism of forest charge on regional temperature based on the methods for integrating remote sensing observations is inconsistent. Therefore, the biophysical mechanism of forest change and the temperature effects are worthy of in-depth analysis. In the future, the cross-cutting advantages of rational integration of multiple data sources to explain forest response to climate effects should be fully utilized to understand the interaction between forest change, carbon cycle and climate under biophysical and biochemical mechanisms, and to maximize climate benefits through rational management of forest ecosystems.

Key words: forest change, biophysical mechanisms, temperature effects, multi-source remote sensing

CLC Number:

S757
TP79

SHEN Wenjuan, JI Mei, LI Mingshi. Review on monitoring methods of the effects of forest changes on regional temperature based on multi-source remote sensing data[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2022, 46(3): 1-11.

Figures/Tables 3

Table 1

Table 2

Table 3

The primary methods for quantifying the biophysical mechanisms of forest changes with integrated remote sensing observations"

类型type	方法method	特点trait	应用范围application range
全球气候模式 global climate model	陆面过程气候影响模拟研究^[66] LUCID	多模式比较计划,针对历史土地利用变化对区域和全球气候的影响,通过多模式比较来确定一致的、可靠的影响,区分模式本身的变率和噪声;多种模式间关于温度变化的差别很大,更多反映出不同模式对土地覆被变化敏感性的差异;存在不能以能量变化解释的温度变化	全球毁林、土地利用类型变化等
	一般环流模式^[67-68] GCMS	最广泛使用的气候模型,预测大尺度大气环流对土地覆盖变化的反馈	全球土地覆盖变化
	群落气候系统模式^[69] community climate system	涉及地表过程的气候模式,可以作为一个独立的模块来测试土地覆盖和土地利用变化对气候的显著影响	全球土地覆盖变化
	气候模型 (IPSLCM)^[70] the institute pierre simon Laplace	由大气模式LMDz(代表大气中基本的动力学和物理过程)与陆面模式ORCHIDEE(代表陆地生态系统植被动态和相互作用的碳循环)组成,将观测到的海表温度(SST)和海冰(SIC)作为下边界条件。中分辨率(~1°× 0.6°),粗分辨率(~4°× 2°)	全国造林、全球土地覆盖变化等
区域气候模式 regional climate model	天气研究及预报模式^[71] WRF	短时期数值天气预报系统,模型中嵌入的陆面模型可以反映温度的昼夜循环和降水等气象特征;能表达区域问题的影响,以更好地理解陆地与大气之间的耦合关系。中尺度的区域气候模型,分辨率为≤20 km,不适用于长期气候模拟	内蒙古造林、全国造林绿化、欧洲土地覆盖变化等
	天气研究及预报模式的气候扩展^[72] CWRF	WRF的扩展,在继承WRF本身数值预报功能同时增强了对气候的模拟能力;具有多模式集合预报能力,实现多种设置组合模拟过程	我国植树造林及其他区域等
地表能量平衡模型 surface energy balance model	内在生物物理机制模型^[65] IBM	空气阻力与表面蒸发变化驱动的长波辐射反馈与能量再分配的结果;外部强迫和内部反馈的区分性强	内蒙古、广东及全国造林;高纬度、北美、全球土地覆盖变化等
	双抗性机制^[73] TRM	空气动力阻力和表面阻力控制的辐射强迫和湍流热通量	我国西北、内蒙古、美国加州、美国东南等
	直接分解温度度量机制^[74-75] DTM	单个能量通量;不考虑能量再分配过程;归因结果有更高的量级	内蒙古、全球土地覆盖变化等
共同之处 common		与卫星观测结合能够填补站点观测或者模型模拟的不足

Table 3

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类别category	数据data	类型type	分辨率resolution
遥感数据 remote sensing data	Landsat	5/7/8/9	30 m
	PALSAR	全球镶嵌数据	25 m
	其他中高分辨率数据	GF、Sentinel、worldview、RapidEye等	米级、亚米级
	MCD43系列	MODIS反照率	500 m
	LST/ET/Albedo_avhrr	MODIS/HiGLASS/GLASS LST/ET/Albedo	30 m等
气候数据 climate data	气温/降水	气象站、通量站、数据集	1 km等
	日照	气象站、通量站、数据集	月、年
	辐射数据等	MODIS/GLASS/MERRA-1数据	0.5°×2/3°(1 km)、小时、天
其他数据other data	DEM	SRTM、ALOS DEM、STER GDEM	15 m、30 m、90 m

类型type	影像类别category	特征变量variable	原理theory	特点trait
自动化与半自动化 automatic and semi-automatic	Landsat (VCT^[47]、LandTrendr^[48-49]、CCDC^[50]、BFAST^[51])	IFZ、NBR、NDVI、可见光与红外等	光谱与分类、轨迹分析、时序物候模型、趋势分析、谐波回归等	固定参数设置、数据量大、受图像质量影响大
多源数据集成 multi-source data integration	Landsat、PALSAR、Sentinel、GF	光谱、纹理、极化	单一时相推到多时相的空间分析	自动化水平待提高、尺度统一问题

Review on monitoring methods of the effects of forest changes on regional temperature based on multi-source remote sensing data

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