Integrating three-dimensional greenness into RSEI improved the scientificity of ecological environment quality assessment for forest

Liu, Yun; Xu, Weiheng; Hong, Zehu; Wang, Leiguang; Ou, Guanglong; Lu, Ning; Dai, Qinling

doi:10.1016/j.ecolind.2023.111092

Cited by 19 publications

(4 citation statements)

References 34 publications

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“…We considered that this might be due to the saturation of NDVI, so we used UAV-derived LAI as an independent variable and NDVI as the dependent variable to explore this saturation issue based on a quadratic function. The independent variable corresponding to the extreme value of the function in the interval is the LAI saturation point [50]. As shown in Figure 14, LAI and 3DGV fit a functional relationship with a R 2 of 0.57, and the NDVI was 0.66, reaching saturation point when LAI was 3.63.…”

Section: Spatial Pattern Of 3dgv In Kunming Citymentioning

confidence: 93%

“…After validating the accuracy, we extended the model to cover the entire city of Kunming, and compared it with CHM, LAI, and FVC to analyze the effectiveness of our model. We selected the maximum and minimum values of the NDVI in the study area instead of the NDV I veg and NDV I soil values [50]. That is, the NDVI values corresponding to a cumulative frequency of 2% were taken as NDVI soil, and the NDVI values corresponding to a cumulative frequency of 98% were taken as NDV I veg .…”

Section: Accuracy Assessment Of Estimation Modelsmentioning

confidence: 99%

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Retrieval of Three-Dimensional Green Volume in Urban Green Space from Multi-Source Remote Sensing Data

Hong,

Xu,

Liu

et al. 2023

Remote Sensing

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Quantification of three-dimensional green volume (3DGV) plays a crucial role in assessing environmental benefits to urban green space (UGS) at a regional level. However, precisely estimating regional 3DGV based on satellite images remains challenging. In this study, we developed a parametric estimation model to retrieve 3DGV in UGS through combining Sentinel-1 and Sentinel-2 images. Firstly, UAV images were used to calculate the referenced 3DGV based on mean of neighboring pixels (MNP) algorithm. Secondly, we applied the canopy height model (CHM) and Leaf Area Index (LAI) derived from Sentinel-1 and Sentinel-2 images to construct estimation models of 3DGV. Then, we compared the accuracy of estimation models to select the optimal model. Finally, the estimated 3DGV maps were generated using the optimal model, and the referenced 3DGV was employed to evaluate the accuracy of maps. Results indicated that the optimal model was the combination of LAI power model and CHM linear model (3DGV = 37.13·LAI−0.3·CHM + 38.62·LAI1.8 + 13.8, R2 = 0.78, MPE = 8.71%). We validated the optimal model at the study sites and achieved an overall accuracy (OA) of 75.15%; then, this model was used to map 3DGV distribution at the 10 m resolution in Kunming city. These results demonstrated the potential of combining Sentinel-1 and Sentinel-2 images to construct an estimation model for 3DGV retrieval in UGS.

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Section: Spatial Pattern Of 3dgv In Kunming Citymentioning

confidence: 93%

Section: Accuracy Assessment Of Estimation Modelsmentioning

confidence: 99%

Retrieval of Three-Dimensional Green Volume in Urban Green Space from Multi-Source Remote Sensing Data

Hong,

Xu,

Liu

et al. 2023

Remote Sensing

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“…In future studies, multi-temporal EEQ, such as monthly data and mean or maximum value synthesis, deserves more attention. In terms of spatial scale, compared with administrative regions, watersheds [16], mining areas [19], and towns [22] are all common research scales, but there are few related works on different ecological functional areas [29], which can become the focus of future work.…”

Section: Shortcomings and Prospectsmentioning

confidence: 99%

“…The main ideas for improving the RSEI include the following: (1) Appropriate subindicators are added to increase the suitability of the results for a study area. For instance, in mining environments, the mine-specific eco-environment index (MSEEI), constructed using the mining intensity index [28], can be added, and for forest ecological environments, the three-dimensional greenness remote sensing ecological index (TDRSEI) constructed using the forest canopy height [29] can be added. (2) Subindicators can be preprocessed to obtain more information.…”

Section: Introductionmentioning

confidence: 99%

Evolution and Spatiotemporal Response of Ecological Environment Quality to Human Activities and Climate: Case Study of Hunan Province, China

Hui,

Cheng

2024

Remote Sensing

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Human beings are facing increasingly serious threats to the ecological environment with industrial development and urban expansion. The changes in ecological environmental quality (EEQ) and their driving factors are attracting increased attention. As such, simple and effective ecological environmental quality monitoring processes must be developed to help protect the ecological environment. Based on the RSEI, we improved the data dimensionality reduction method using the coefficient of variation method, constructing RSEI-v using Landsat and MODIS data. Based on RSEI-v, we quantitatively monitored the characteristics of the changes in EEQ in Hunan Province, China, and the characteristics of its spatiotemporal response to changes in human activities and climate factors. The results show the following: (1) RSEI-v and RSEI perform similarly in characterizing ecological environment quality. The calculated RSEI-v is a positive indicator of EEQ, but RSEI is not. (2) The high EEQ values in Hunan are concentrated in the eastern and western mountainous areas, whereas low values are concentrated in the central plains. (3) A total of 49.40% of the area was experiencing substantial changes in EEQ, and the areas with significant decreases (accounting for 2.42% of the total area) were concentrated in the vicinity of various cities, especially the Changsha–Zhuzhou–Xiangtan urban agglomeration. The areas experiencing substantial EEQ increases (accounting for 16.97% of the total area) were concentrated in the eastern and western forests. (4) The areas experiencing substantial EEQ decreases, accounting for more than 60% of the area, were mainly affected by human activities. The areas surrounding Changsha and Hengyang experienced noteworthy decreases in EEQ. The areas where the EEQ was affected by precipitation and temperature were mainly concentrated in the eastern and western mountainous areas. This study provides a valuable reference for ecological environment quality monitoring and environmental protection.

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Optimization Strategies for Ecological Security Pattern Based on the Remote Sensing Ecological Index in Yunnan Province, China

Lai,

Li,

Liu

2024

Land Degrad Dev

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Current research on ecological security pattern (ESP) primarily emphasizes preserving the overall connectivity of ecosystems. However, a significant gap exists in systematically identifying ecologically degraded areas and optimizing regional ecological networks. In particular, the southwestern region of China, characterized by complex terrain and diverse ecosystems, lacks comprehensive research on the dynamic changes in regional ESP. This study uses machine learning to identify the primary drivers of spatial patterns in the remote sensing ecological index (RSEI) and establishes a corresponding ecological resistance surface. Through integrating morphological spatial pattern analysis with circuit theory, we developed a “point‐line‐area” combined ESP for Yunnan Province and proposed recommendations for optimizing regional ecological protection. Results show that 66 ecological sources were identified in 2000, decreasing to 52 in 2023, with the area increasing from 29,730.57 to 43,122.06 km2, primarily in the southwest. Additionally, the study identified 69 ecological corridors in 2000 and 52 in 2023, with total length reducing from 5934 to 4813 km. Ecological connectivity in the southwestern region improved significantly; however, ecological corridors in the northeast remain sparse, contributing to ecological pressure. Furthermore, 71 and 65 ecological pinch points, along with 46 and 53 ecological barriers, were identified. Overall, ecological source areas in Yunnan Province increased significantly, with substantial connectivity improvements, especially in the southwest. However, areas such as the Kunming economic circle exhibit limited ecological functionality. Future efforts should focus on expanding buffer zones and restoring corridors to enhance connectivity and stability.

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Integrating three-dimensional greenness into RSEI improved the scientificity of ecological environment quality assessment for forest

Cited by 19 publications

References 34 publications

Retrieval of Three-Dimensional Green Volume in Urban Green Space from Multi-Source Remote Sensing Data

Retrieval of Three-Dimensional Green Volume in Urban Green Space from Multi-Source Remote Sensing Data

Evolution and Spatiotemporal Response of Ecological Environment Quality to Human Activities and Climate: Case Study of Hunan Province, China

Optimization Strategies for Ecological Security Pattern Based on the Remote Sensing Ecological Index in Yunnan Province, China

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