Remote Sensing Technology and Application

Dynamic Monitoring of Ecological Quality for Hangzhou Greater Bay Area and Its Response to Land Use Cover Change Using Remote Sensing based Ecological Index

Yujun CHEN, Lingyu WANG, Yue LI, Congying GAN, Junwei YE, Zhenjie YANG, Chao SUN

Remote Sensing Technology and Application, 2024, 39(3): 764-776. DOI: 10.11873/j.issn.1004-0323.2024.3.0764

指数	计算公式	说明
NDVI	$ρ 4 - ρ 3 ρ 4 + ρ 3$	$ρ 4$ 和 $ρ 3$ 分别为影像中红光波段与近红外波段^[29]
Wet	Landsat TM： $0.0315 ρ 1 + 0.2021 ρ 2 + 0.3102 ρ 3 + 0.1594 ρ 4 - 0.6806 ρ 5 - 0.6109 ρ 7$	$ρ i i = 1, 2, …, 7$ 为影像各个对应波段的反射率^[30]
	Landsat ETM+： $0.2626 ρ 1 + 0.2141 ρ 2 + 0.0926 ρ 3 + 0.0656 ρ 4 - 0.7629 ρ 5 - 0.5388 ρ 7$
	Landsat OLI： $0.1511 ρ 2 + 0.1973 ρ 3 + 0.3283 ρ 4 + 0.3407 ρ 5 - 0.7117 ρ 6 - 0.4559 ρ 7$
NDBSI	$N D B S I = S I + I B I 2$ $S I = ρ 5 + ρ 3 - ρ 4 + ρ 1 ρ 5 + ρ 3 + ρ 4 + ρ 1$ $I B I = 2 ρ 5 ρ 5 + ρ 4 - ρ 4 ρ 4 + ρ 3 + ρ 2 ρ 2 + ρ 5 2 ρ 5 ρ 5 + ρ 4 + ρ 4 ρ 4 + ρ 3 + ρ 2 ρ 2 + ρ 5$	采用建筑指数IBI^[31]和土壤指数SI的均值表示干度指标（NDBSI）^[32]。 $ρ 1 - 5$ 分别对应遥感影像中蓝光、绿光、红光、近红外、短波红外波段。
LST	$(λ ρ + a) - 273.15$	采用线性拉伸方式将数据还原为真实地表温度， $λ$ 为斜率（L2C2中取值0.003 418 02）， $ρ$ 为Landsat数据热红外波段， $a$ 为截距（L2C2中取值149）^[25-26]。

Table 2 Formulas and their corresponding descriptions for each remote-sensing index

Other figure/table from this article

Fig. 1 Location of Hangzhou Greater Bay Area
Table 1 Details of Landsat images acquired during 1995~2020
Fig.2 Principal component analysis for each indicator
Table 3 Accuracy assessments for land use classifications of Hangzhou Greater Bay Area in the years 2007 and 2020 （2020 in brackets）
Fig. 3 RSEI changes for Hangzhou Greater Bay Area and its prefecture-level cities duing 1995~2020
Fig. 4 Spatial distribution of ecological quality RESI for Hangzhou Greater Bay Area duing 1995~2020
Fig.5 Hotspot areas for ecological quality change during 1995~2020
Fig.6 Influences on ecological quality from different land use types and their transformation