Research on temporal and spatial evolution of land use and landscape pattern in Anshan City based on GEE

Abstract

Frequent mining activities can bring about problems such as soil erosion and environmental pollution, which are detrimental to the efficient use of land and the sustainable development of cities. Existing studies have paid little attention to mining areas and lack comparative analysis of landscape changes in multiple mining pits. In this paper, the main urban area of Anshan City, where the mining areas are concentrated, was used as the research area, and the Landsat TM/OLI surface reflectance (SR) data of the Google Earth Engine (GEE) platform and the random forest algorithm were used to map the land use in 2008, 2014, and 2020. On this basis, land use dynamics and landscape pattern indices were used to analyze the changes in land use and landscape patterns in the Anshan City area. In addition, a moving window method was combined to further analyze and compare the landscape changes between different pits. The results show that:1. From 2008 to 2020, the construction land in Anshan urban area continued to decline, the forest land continued to expand, and the construction land was shifted to the forest land and cultivated land. Mining land increased before 2014 and remained almost unchanged after 2014, which is in line with the actual situation. 2. During the study period, the landscape fragmentation degree and landscape heterogeneity in the urban area of Anshan kept increasing. The high value areas of landscape fragmentation were the urban-rural combination areas and the mining areas. Among them, the reclamation of Dagushan and Donganshan is better, while the reclamation of Anqian, Yanqianshan and Xiaolingzi mines needs to be strengthened. 3. The random forest algorithm based on GEE shows a high degree of accuracy for land use classification. The overall classification accuracy in 3 years exceeds 90% and the kappa coefficient exceeds 0.85. The study results can be used as an essential reference for optimizing the urban ecological environment and provide technical backing for the urbanization construction and rational use of land in Anshan City.

Study of Ecosystem Degradation Dynamics in the Peruvian Highlands: Landsat Time-Series Trend Analysis (1985–2022) with ARVI for Different Vegetation Cover Types

“…The positive trend observed in urban areas (p > 0.01) is consistent with the existing literature suggesting that activities associated with urban areas, especially mining, have significant negative impacts on vegetation cover and land use [42]. In particular, studies in Nigeria and China have highlighted the extensive damage caused by mining, including sand mining, in peri-urban and urban areas, respectively [43,44]. These findings support the idea that mining activities contribute to land degradation in urban areas, which could explain the trends observed in our study.…”

Identifikasi Perubahan Area Pertambangan Menggunakan Citra Satelit Landsat Dengan Machine Learning Pada Google Earth Engine Di Kecamatan Cipatat, Kabupaten Bandung Barat

“…Salah satu teknologi penginderaan jauh baru dalam pemanfaatan big data berbasis cloud computing untuk menyediakan data dan fasilitas pengolahan spasial dari citra satelit yaitu Google earth engine (GEE). GEE yang baru diluncurkan pada tanggal 2 Desember 2010 oleh Perusahaan Google merupakan platform analisis geospatial yang telah banyak digunakan didalam bermacam penelitian seperti analisis tata guna lahan (Fu & Zhang, 2022;Julianto et al, 2020;Novianti, 2021;Rizaldi et al, 2022;Zurqani et al, 2018), kebencanaan seperti gerakan tanah (Ilmy et al, 2021;Wu et al, 2022).…”

“…Menurutnya, random forest merupakan sebuah algoritma yang mampu mengatasi missing data, mampu memberikan hasil klasifikasi dengan tingkat kesalahan yang lebih rendah, dan mampu mengatasi volume data yang besar (Breiman, 2001). Beberapa penelitian menunjukkan random forest memiliki nilai akurasi yang tinggi (Fu & Zhang, 2022;Junus et al, 2023;Teluguntla et al, 2018;Vergni & Todisco, 2023).…”

Impact of Huanglongbing on Citrus Orchards: A Spatiotemporal Study in Xunwu County, Jiangxi Province

“…Analyzing the spatiotemporal pattern changes in citrus orchard land with and without IPS coverage contributes to evaluating the stability of the study area's ecosystem. It further unveils complex human-environment relationships, providing data support for optimizing land-use planning and formulating sustainable land-use strategies and management plans [26]. Studies on the influencing factors of spatiotemporal changes in citrus orchards currently utilize methods such as spatial autocorrelation [27], spatial econometric models, geographic weighted regression models [28], and geographical detectors [29].…”