| 引用本文: | 杨智程,董春媛,乔荣荣,罗立辉,常学礼.2025.宁夏沿黄绿洲生态环境质量动态与特征[J].地球环境学报,16(1):89-98 |
| YANG Zhicheng,DONG Chunyuan,QIAO Rongrong,LUO Lihui,CHANG Xueli.2025.Ecological environment quality dynamics and characteristics in the oasis of Ningxia section of the Yellow River based on Google Earth Engine[J].Journal of Earth Environment,16(1):89-98 |
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| 宁夏沿黄绿洲生态环境质量动态与特征 |
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杨智程1, 2, 3，董春媛3，乔荣荣4，罗立辉1，常学礼3*
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1. 中国科学院西北生态环境资源研究院，兰州 730000
2. 中国科学院大学，北京 100049
3. 鲁东大学 资源与环境工程学院，烟台 264025
4. 南京大学 生命科学学院，南京 210000
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| 摘要: |
| 利用Google Earth Engine（GEE）平台对宁夏沿黄人工绿洲2000、2011和2020年进行遥感生态指数（remote sensing ecological index，RSEI）评价，同时对不同土地利用方式类型区2020年RSEI评价结果进行比较分析。结果表明：（1）2000—2020年宁夏沿黄人工绿洲生态环境质量在时间上整体呈上升趋势，在空间上RSEI分布体现为中部海拔较低区生态环境质量高，而四周海拔较高区生态环境质量低，在研究周期内生态环境质量分级主要以较差和良为主；（2）2000—2011年不同RSEI级别流转中，优级面积减少623.0 km 2，差级面积增加264.2 km 2，期间总体生态环境趋于变差；2011—2020年RSEI优级面积增加1936.4 km 2，生态环境趋于变好；（3）选择NDVI、WET、NDBSI和LST 4个因子构建RSEI指数在NDVI较高区域的不同研究结果之间具有一致性；但在NDVI较低区域由于影响PC1的主要评价因子并非NDVI，研究结果解释需要因事而异。总体来看，宁夏沿黄绿洲RSEI在研究期内变化呈总体改善、东南边缘恶化的状况；RSEI结果解释需要考虑NDVI等评价因子在PC1上的贡献率。 |
| 关键词: Google Earth Engine 遥感生态指数 主成分分析 宁夏沿黄人工绿洲 |
| DOI：10.7515/JEE222091 |
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| 基金项目:宁夏回族自治区重点研发计划（2021BEG02010）；国家自然科学基金项目（41271193） |
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| Ecological environment quality dynamics and characteristics in the oasis of Ningxia section of the Yellow River based on Google Earth Engine |
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YANG Zhicheng1, 2, 3, DONG Chunyuan3, QIAO Rongrong4, LUO Lihui1, CHANG Xueli3*
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1. Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. School of Resources and Environmental Engineering, Ludong University, Yantai 264025, China
4. School of Life Sciences, Nanjing University, Nanjing 210000, China
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| Abstract: |
| Background, aim, and scope The quality of regional ecological environment is a cornerstone for sustainable human development. Recently, the remote sensing ecology index (RSEI) has garnered widespread adoption in assessing ecological quality, yet studies focusing on the vast artificial oases in northwest China remain scarce. This paper employs the RSEI model to evaluate the ecological quality of the oasis of Ningxia section of the Yellow River, situated in the arid and semi-arid region. Our primary objective is to quantify RSEI’s current status across various timeframes within the study area, leveraging the Google Earth Engine (GEE) platform, and to elucidate the transformations in ecological quality within the artificially irrigated oases (plains) prevalent in northwestern China’s interior. Materials and methods Utilizing Landsat data on the GEE platform, we computed greenness, humidity, dryness, and heat factors for 2000, 2011, and 2020. Subsequently, we integrated these factors with RSEI through principal component analysis (PCA) to determine the ecological quality of the oasis of Ningxia section of the Yellow River. Additionally, we selected Otog Front Banner (a grassland area) and Wuyuan County, Bayannur City (a non-oasis plain agricultural area) in Inner Mongolia Autonomous Region as benchmarks to assess the limitations of RSEI application by comparing them with the oasis of Ningxia section of the Yellow River. Results (1) Temporal and spatial trends: from 2000 to 2020, the overall ecological quality of the oasis of Ningxia section of the Yellow River demonstrated an upward trend, with the RSEI spatially reflecting higher quality in the central lower-elevation areas and lower quality in the surrounding higher-elevation regions. Throughout the study period, the ecological quality was primarily categorized as fair to good. (2) Change analysis: between 2000 and 2011, the area with excellent ecological quality decreased by 623.0 km 2 while the poor-quality area expanded by 264.2 km 2, indicating a general decline in ecological health. Conversely, from 2011 to 2020, the excellent-quality area expanded by 1936.4 km 2, signifying an improvement in the ecological environment. (3) Factor consistency: the selected RSEI factors (NDVI, WET, NDBSI, and LST) yielded congruent results in high-NDVI regions. However, in low-NDVI areas, where NDVI is not the primary contributor to PC1, the interpretation of RSEI necessitates a case-by-case approach. Overall, the RSEI of the oasis of Ningxia section of the Yellow River improved substantially during the study period, with localized deterioration at the southeast periphery, necessitating a nuanced consideration of factors such as NDVI’s contribution to PC1. Discussion During the study period, the RSEI of the oasis of Ningxia section of the Yellow River showed a favorable trend, with the superior-grade area expanding from 786.1 km 2 in 2000 to 2099.5 km 2 in 2020, an increase of 1313.4 km 2. Simultaneously, there was a deterioration trend, with the poor-level area also expanding by 469.6 km 2. This pattern of overall improvement amidst localized deterioration aligns with studies on wetlands, mine restoration zones, and urban agglomerations. Notably, the declining areas of significantly deteriorated, deteriorated, and unchanged levels contrasted with the expanding areas of improved and significantly improved. Notably, the unchanged-level area decreased drastically, from 60.30% in 2000—2011 to 44.38% in 2011—2020, a shift of 1671.1 km 2. From 2000 to 2011, deteriorated and significantly deteriorated level area clustered in the study’s central region (Yinchuan urban area), linked to reduced vegetation cover and increased artificial surfaces due to rapid industrialization and urbanization. Regarding RSEI’s applicability, high-vegetation-cover (NDVI) regions yielded relatively consistent and comparable results. Conversely, in low-vegetation-cover areas, changes in the primary factors influencing the PC1 axis necessitate specific interpretations. The RSEI model’s universal applicability in semi-arid artificial oasis regions (the oasis of Ningxia section of the Yellow River and Inner Mongolia’s Hetao Plain) underscores consistent ecological significance (positive and negative relationships) in PCA cumulative. However, the ecological significance of PCA cumulative in the adjacent Ordos grassland area of Inner Mongolia differs markedly from semi-arid oases, emphasizing the need to refine RSEI evaluation factors to capture differences across bioclimatic zones, geographic units (ecosystems), or land use types. Conclusions RSEI assessments in high-vegetation-cover (NDVI) regions yield more consistent and comparable results. Conversely, in low-vegetation-cover areas, the variability in factors influencing the PC1 axis necessitates cautious analysis and generalization of findings. Recommendations and perspectives Relying solely on PC1 in RSEI evaluations leads to unclear cumulative load thresholds, hindering the comparability of research outcomes. A potential solution involves initially determining a cumulative loading threshold in the methodology, subsequently selecting the number of components for RSEI evaluation based on whether the PCA component’s cumulative value exceeds this threshold. This approach could enhance the precision and comparability of RSEI-based ecological quality assessments. |
| Key words: Google Earth Engine remote sensing ecology indices principal components analysis oasis of Ningxia section of the Yellow River |
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