Abstract:

The ecological environment serves as the foundation for human survival and social development, and its condition and evolution directly affect human existence and sustainable development. Over the past half-century, rapid population growth and advancements in science and technology have accelerated the rate and scale of natural resource extraction through human activities. While these activities have brought substantial material wealth to human society, they have also exacerbated the imbalances in natural ecosystems. Ecological issues such as environmental pollution, climate change, deforestation and degradation, soil erosion, desertification, and biodiversity loss have emerged one after another. These problems have become critical factors threatening regional ecological security and sustainable economic development. Under the framework of ecological civilization construction and the “Beautiful China” initiative, accurately understanding and evaluating the urban ecological environment is fundamental and essential for protecting the ecosystem. It also provides an important basis for formulating environmental protection policies and resource utilization plans. As China’s first special economic zone, Shenzhen has transitioned from a small fishing village to one of the country's four first-tier cities and serves as a core engine of the Guangdong-Hong Kong-Macao Greater Bay Area. It is one of the most economically dynamic cities in mainland China and a significant global financial and port hub. Shenzhen was the first city in China to achieve 100% urbanization, and is known for its diverse and open cultural environment. However, over the past few decades, intensive economic development and urban expansion in Shenzhen have disrupted the elements, structure, and functions of its original ecosystem, presenting significant challenges to its ecological civilization initiatives. Timely and accurate acquisition of spatiotemporal distribution characteristics and evolutionary trends in Shenzhen's ecological environment quality is critical for promoting ecological governance, supporting sustainable urban development, and optimizing the living environment. However, Shenzhen, located in southern China, experiences a humid and rainy climate along with drastic ecological changes, posing challenges for long-term ecological monitoring and assessment using remote sensing ecological indices. Optical remote sensing imagery is often affected by cloud cover, which hinders regional image mosaicking and index extraction. This also affects the normalization of indices and the consistency of acquisition times for multi-temporal remote sensing images, reducing the comparability of long-term ecological quality assessments. Previous studies on long-term ecological quality assessments in Shenzhen have often faced low time-series comparability, making it difficult to accurately reflect the spatial-temporal distribution and evolution patterns of ecological quality. To address these issues, this study used four periods of Landsat remote sensing data (1990, 2000, 2010, and 2020) to develop a Unified Remote Sensing Ecological Index (URSEI) tailored to long-term series and areas with drastic ecological changes. The methodology involved significant advancements to ensure its accuracy and comparability. For the greenness, wetness, and dryness indices, a two-step compositing approach was adopted. The median values were first synthesized separately for the autumn growing season and winter non-growing season to minimize the influence of factors such as weather, precipitation, and solar angle. The averages of these results provide a comprehensive annual ecological assessment, while reducing the impact of anomalies from single observations. For the heat index, autumn growing season imagery was used to address the seasonal variability in the urban heat island effect. Using Landsat thermal infrared data, surface temperature consistency was ensured through random forest regression models incorporating land cover type, NDVI, elevation, and slope as the explanatory variables. To further enhance data consistency across the 30-year timeline, a novel normalization method was introduced. Invariant regions, identified as areas with no significant land cover change, were used to normalize the ecological indices. A cumulative distribution function (CDF) method was applied to these regions, defining the 1st and 99th percentiles as the normalization boundaries, ensuring that all indices were standardized within a 0-1 range. Negative indicators, such as dryness and heat, were inverted to align with the positive ecological trends. Finally, a multi-temporal fused principal component analysis was conducted across all monitoring periods using unified weights for ecological factors to derive the first principal component (PC1) as the URSEI. This approach ensured robust comparability of ecological quality changes over time, enabling a comprehensive 30-year analysis of Shenzhen’s spatiotemporal ecological evolution. The results were as follows: 1) From 1990 to 2020, the overall ecological environment quality in Shenzhen exhibited a declining trend, followed by a rising trend. During the early urbanization phase, ecological quality deteriorated significantly. However, the subsequent implementation of ecological protection policies and urban planning adjustments gradually restored and improved the environment. The mean URSEI value decreased from 0.477 in 1990 to 0.429 in 2000 but rose to 0.491 in 2020, marking a 2.94% increase over 30 years. 2) Spatially, regions with poor ecological quality were widely distributed in northern districts, such as Bao’an, Guangming, Longhua, and Longgang, and in southern districts, such as Futian and Nanshan. Areas of “fairly poor” ecological quality were typically located on the periphery of these poor regions. Regions with excellent ecological quality were concentrated in southeastern districts, such as Dapeng New District, Yantian District, eastern Luohu, southern Pingshan, and the edges of western urbanized areas. 3) Significant differences in ecological quality were observed between the districts in Shenzhen. Ranking the mean URSEI values for 10 districts over 30 years, Dapeng New District and Yantian District consistently ranked the highest, with superior ecological quality, whereas Longhua District ranked lowest. The Futian District showed significant improvement, climbing from 10th to 6th, whereas Guangming and Bao’an experienced declines, dropping from 5th and 7th to 8th and 9th, respectively. 4) A time-series analysis of Shenzhen’s ecological quality revealed that, between 1990 and 2020, the area of ecological improvement exceeded that of degradation. Although the city has experienced rapid economic growth, its overall ecological environment has improved. A 1-km grid-based analysis of spatiotemporal changes indicated a slight decline in ecological quality from 1990 to 2000, improvement in most areas from 2000 to 2010, and further enhancement between 2010 and 2020. 5) The proposed URSEI demonstrated significant advancements in addressing the consistency challenges posed by Shenzhen’s cloudy and rainy climates. It showed strong potential for application in multitemporal ecological assessments and spatial visualization mapping. These findings provide scientific support for Shenzhen’s ecological protection efforts, and valuable references for sustainable ecological development in the Greater Bay Area and across China.

Key words: remote sensing ecological index, ecological quality assessment, spatiotemporal pattern, Shenzhen, long-term sequence.

摘要： 深圳市地处多云多雨地区，且生态状况变化剧烈，基于传统的遥感生态指数（Remote Sensing Ecological Index，RSEI）难以确保长时序生态质量评估的一致性。对此，基于RSEI，通过优化数据选取、建立指标合成、不变区域指标归一化，以及多时相融合主成分分析等步骤，构建统一遥感生态指数（Unified RSEI，URSEI）。在此基础上，应用1990－2020年Landsat系列影像数据，开展了深圳市30年间生态质量时空演变格局分析研究。结果表明，1）1990－2020年间，深圳市生态质量总体呈先下降后上升的趋势，30年间URSEI总体上升了2.94%。2）对1 km网格生态质量时空演变分析表明，1990－2000年，深圳市生态质量总体略有下降。其中，2000－2010年，深圳市东北部和西北部的生态质量有所下降，其他地区呈现改善趋势；2010－2020年，深圳市生态质量进一步提升，仅在部分在建区域表现出生态质量的下降。3）该研究所提出的URSEI能够有效减弱多云多雨天气对长时序生态质量评估一致性的影响。反演的深圳市生态质量在长时序制图过程中没有出现明显的拼接痕迹，且其时空分布特征与深圳市的实际状况高度吻合，能够准确反映深圳市生态质量的时空演变过程。研究结果可为深圳市生态保护和可持续发展提供科学依据。

关键词: 遥感生态指数, 生态质量评估, 时空格局, 深圳市, 长时序