Evaluation of Ecological Quality based on the Google Earth Engine

Main Contribution: Data collection, processing and analysis

Main plaform: GEE, Matlab, ArcMap 10.4, Arcpy

Introduction

The ecology is a complex of various natural and social elements that are human-centered. It is not only the basis for human survival, but also a guarantee for sustainable development (Zhao et al., 2018). Ecological quality refers to how a combination of ecological factors is suitable for human survival and sustainable development (Ye and Liu, 2000). The evaluation of the ecological quality can enhance the understanding of the current state of the regional ecology and provide references for regional development. Xu (2013) proposed the Remote Sensing Ecological Index (RSEI). Remote sensing data as the data source, RSEI enables quantitative analysis, regional visualization, spatial and temporal analysis, and projection of ecological quality. The Google Earth Engine (GEE) platform provides users with access to multi-source remote sensing data and processing services for geographic datasets. It offers a solution to remote sensing evaluation of ecological quality at long time series and large spatial scales.

The Three Norths region of China (Northeastern, north of Northern and Northwestern China) have long been characterized by severe desertification and fragile ecological environments (Huang et al., 2016; Nie et al., 2005). Forests have played an important role in ecology with wind and sand control, air purification, water conservation and economic benefits (Huang et al., 2016; Chen et al., 2015; Wang et al., 2019; Wu et al., 2020) Since 1978, China has launched a large-scale afforestation called the Three North Shelter Forest Project, forming the Three North Shelter Forest Region (TNSFR) covering most Three Norths region (Zhu and Zheng, 2019). TNSFR includes four sub-regions: Inner Mongolia-Xinjiang in the west, Loess Plateau and North of the Northern China in the middle, and Northeastern China in the east. Accordingly, this study will quantitatively evaluate and visualize the ecological quality of the TNSFR from 1985 to 2020. Based on the GEE platform, this study uses RSEI as an indicator to quantify the ecological quality of the region, to quantitatively monitor and evaluate the ecological quality of the TNSFR at a long time series and large spatial scale, to infer the benefits of forest construction, and to provide references for regional ecological construction.

Data & Method

Spatial (a) and temporal (b) distributions of Landsat images

The study mainly used Landsat series satellite remote sensing images (Wulder et al., 2019), including all available Landsat 5 TM and Landsat 8 OLI/TIRS images in the study area during the summer of 1984-2020 (April-September), with a total of 64975 images. Other data included spatial data on atmospheric water content and radiative output.

Workflow

The study used the GEE platform to acquire, mask, and synthesize regional remote sensing data and ancillary data. Afterward, the normalized differential vegetation index (NDVI), wet content (WET) and normalized differential building index (NDBI) were calculated through exponential operations. The land surface temperature (LST) was obtained using the single window algorithm for inversion. Finally, the water body mask was processed, and the RSEI index was obtained using normalization and principal component analysis in the range of 0-1 (the eigenvectors of principle component 1 were used to get the RSEI index).

Using the year-by-year RSEI, the study analyzed ecological quality changes and spatial differentiation in the TNSFR from 1985 to 2020:

(1) Based on the distribution of the regional (at both full and sub regional scale) RSEI data, the regional ecological quality was classified into five levels: very poor, poor, medium, good, and excellent.

(2) The study carried out the mean statistics, mapping and analysis of the RSEI's distribution and trends at the spatial and temporal scales.

(3) A comparative analysis of the intra-regional trends and inter-regional differences in RSEI was carried out at the sub-regional scale. And a transfer matrix was used to calculate the proportion of level-rising and level-falling pixels to all pixels in the sub-region at different times to further reveal the spatial differentiation and variability of the ecological quality over time.

(4) The study analyzed trends of the RSEI components at the sub-regional scale and discussed the reasons for the changes.

Result

Distribution of the RSEI in the Three-North Shelter Forest region during 1985-2020

The changes in RSEI in the Three-North Shelter Forest region during 1985-2020

The ecological quality in the TNSFR appears a stable and slight deterioration in the early stage and recovery in the later stage, with a slight improvement in general. From 1985 to 2005, the average regional RSEI decreased from 0.443 to 0.439. From 2005 to 2015, the value increased from 0.439 to 0.466, reflecting the improvement in the quality of the regional ecology.

The changes in RSEI in the sub-regions of the Three-North Shelter Forest region during 1985-2020

The proportion of different RSEI levels in the sub-region of the Three-North Shelter Forest region during 1985-2020

By region, the ecological quality of the TNSFR generally shows a pattern of better in the east and worse in the west. North of Northern China's average RSEI shows a fluctuating upward trend; Northeast China's is stable in the early stage and increasing in the later stage; Northwest China (Inner Mongolia)'s shows a steady but slightly decreasing trend.

Proportion of Pixels with changed RSEI levels in the Three-North Shelter Forest during 1985—2020

For the time-series variation, the ecological quality improvement rate in the TNSFR generally tends to decrease and then increase. While the proportion of level-rising pixels tends to accelerate increasing, that of level-falling pixels tends to increase and then decrease. The rate of improvement is the fastest in the north of Northern China and fluctuating in Northeast China. In contrast, the ecological quality in Northwestern China is generally decreasing with decreasing rate rising with time.

Changes of RSEI components in the sub-regions of the Three-North Shelter Forest Region

during 1995—2015

On the scale of RSEI components, from 1995 to 2015, NDVI and WET in the TNSFR show an increasing trend, and NDBI shows a decreasing trend. The above three indicators contribute positively to the regional ecological quality. The LST is on the rise and harms the regional ecological quality. It is the main factor leading to the decrease of ecological quality in Northwest China.

Conclusion & Discussion

From 1985 to 2020, the overall ecological quality in the TNSFR has improved to some extent, with the average regional RSEI rising from 0.443 to 0.466. The ecological quality, the degree of improvement and the rate of improvement in the eastern part of the study area are better than those in the western part. The rate of improvement in ecological quality shows a fluctuating upward trend. The LST increases, with the average LST in Northwest China rising by more than 4°C from 1985 to 2015, serving as the dominant factor in the decline of ecological quality.

Wang et al. (2012) pointed out that the promotion effect of human activities on the TNSFR was greater than the destruction effect. The regional vegetation cover shows an overall increasing trend. Given that large-scale afforestation is the most considerable environmental variable in the region, there is a high probability that afforestation will positively affect regional ecological quality. Meanwhile, the results of forest construction are regionally diverse. For example, Northwest China has little suitable land for afforestation, and natural disasters such as drought, rodent damage, and snow freezing are frequent (Zhu and Zheng, 2019). Afforestation generates limited improvement in the regional ecological quality under such conditions. In addition, for the arid and semi-arid climate zone in the study area, extensive afforestation may lead to excessive requirements for the carrying capacity of regional water resources, resulting in an increased risk of degradation of protective forests. Therefore, areas of poor ecological quality should be protected scientifically and afforestation activities should be carried out cautiously. At the same time, areas with good conditions are encouraged to conduct further afforestation under existing patterns.

References

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