Spatial-temporal evolution of coupling relationship between land development intensity and resources environment carrying capacity in China

Tan, Shukui; Liu, Qi; Han, Si-Yu

doi:10.1016/j.jenvman.2021.113778

Cited by 64 publications

(36 citation statements)

References 51 publications

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“…Simulasi tutupan lahan tahun 2031 merupahkan simulasi kedua, dalam tahap ini dengan menggunakan driving factors dan akurasi transisi potensial yang sama seperti pada simulasi pertama, namun menggunakan nilai makrovian yang berbeda (Tabel 4), sehingga akan menghasilkan model tahun 2031 Hasil simulasi tutupan lahan Kecamatan Sirimau tahun 2031 dapat digunakan sebagai informasi dalam mengevaluasi RTRW Kota Ambon tahun 2011-2031 dan juga sebagai dasar untuk pengambilan kebijakan terkait penataan dan pemanfaatan ruang kedepannya. Peningkatan perkembangan lahan permukiman yang tidak terkendali dapat menyebabkan penurunan daya dukung lingkungan (Tan et al, 2022) dan kerusakan lingkungan (Shang & Wu, 2022). Oleh karena itu hasil analisis dan prediksi tutupan lahan ini dapat memberikan solusi dalam penataan penggunaan lahan Kota Ambon ke depan berdasarkan SDGs.…”

Section: Simulasi Tutupan Lahan Tahun 2031unclassified

Prediksi Perubahan Tutupan Lahan di Kecamatan Sirimau, Kota Ambon Menggunakan Celular Automata- Markov Chain

Sugandhi

Supriatna

Kusratmoko

et al. 2022

JPG

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Sirimau District has the largest population in Ambon City which has an impact on the need for higher residential land, this will certainly affect uncontrolled land cover changes and have an impact on land conversion and environmental damage. This study aims to analyze land cover changes in Sirimau District in 2012, 2017, and 2022, and predict land cover in 2031. This study uses the Cellular Automata Markov Chains (CA-MC) method. The results showed that in the period 2012-2031 the area of residential land cover and open land continued to increase along with the increase in population and the high need for built-up land. In contrast to the land cover of agricultural areas and non-agricultural areas, which experienced a decrease in area. The results of the research are expected to be used as a basis for making policies related to the arrangement and good use of space in the future

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Section: Simulasi Tutupan Lahan Tahun 2031unclassified

Prediksi Perubahan Tutupan Lahan di Kecamatan Sirimau, Kota Ambon Menggunakan Celular Automata- Markov Chain

Sugandhi

Supriatna

Kusratmoko

et al. 2022

JPG

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“…Perubahan tutupan lahan yang tidak sesuai dengan peruntukannya dapat mengakibatkan keprihatinan dalam pengendalian sumber daya alam dan pembangunan berkelanjutan dalam skala lokal dan global (Zheng et al, 2015). Peningkatan jumlah penduduk yang sejalan dengan meningkatnya kegiatan manusia diberbagai sektor terutama sektor ekonomi, sehingga kebutuhan akan sumber daya lahan juga akan meningkat (Teklay et al, 2021), sedangkan keberadaan lahan yang tetap mengakibatkan terjadinya penurunan daya dukung lingkungan (Tan et al, 2022) dan kerusakan lingkungan (Shang dan Wu, 2022). Oleh karena itu, pemanfaatan dan efisiensi tutupan lahan wilayah Daerah Aliran Sungai (DAS) harus ditingkatkan berdasarkan perencanaan tutupan lahan yang rasional dengan tujuan pembangunan berkelanjutan (Tian et al, 2016), sehingga perlu adanya peningkatan dan pengelolaan potensi pengembangan wilayah berdasarkan rencana tata ruang yang sudah diatur (Mohamed dan Worku, 2019).…”

Section: Pendahuluanunclassified

Analisis Dan Prediksi Perubahan Tutupan Lahan Menggunakan Model Celular Automata-Markov Chain Di Das Wae Ruhu Kota Ambon

Rakuasa

Salakory

Latue

2022

JTSL

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The geographical location of the Wae Ruhu watershed is in Sirimau District, which is the sub-district with the largest population in Ambon City and is also the center of economic, educational, and industrial activities; this creates economic and population growth that has the potential to trigger land conversion around this area. This study aimed to analyze land cover changes in Ambon City in 2012, 2017, 2022 and predict land cover in 2031. This study used Cellular Automata Markov Chains modeling to predict land cover changes in 2031. The results showed that the types of land cover are built and land cover. The open area continues to increase in area, while agricultural and non-agricultural areas continue to experience a decrease in area, and water bodies do not experience a decrease or increase in area. The results of land cover predictions in 2031 showed that the built-up area is 345.79 ha, open land is 121.18 ha, agricultural land is 657.35 ha, non-agricultural land is 507.65 ha, and water bodies are 11.46 ha. The results of this study are expected to be used as a reference in making policies related to spatial planning and utilization, especially the Wae Ruhu watershed and can optimize sustainable watershed management as the first step in efforts to mitigate natural disasters.

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“…NGP is affected by nature, economy, society, and institutions. Owing to the comprehensive effect of various factors, the driving factors of NGP are distinct at different scales, but they mainly include subjective factors from farmers themselves, the objective natural environment of cultivated land, and socioeconomic factors [45][46][47][48][49].…”

Section: Drivers Of Ngpmentioning

confidence: 99%

Spatiotemporal Evolution of Non-Grain Production of Cultivated Land and Its Underlying Factors in China

Zhu

Dai

et al. 2022

IJERPH

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Food security is the foundation of development. We comprehensively characterized the spatiotemporal patterns of non-grain production (NGP) areas in China and elucidated the underlying factors driving NGP. Our objectives were to map NGP on cultivated land (NGPCL) in China, and to quantify its spatiotemporal patterns, to investigate the factors underlying NGP spatial differentiation, and to provide a scientific basis for developing NGP management policies and reference points for protecting cultivated land in other countries. We mapped NGPCL in China from 2000 to 2018 using remote sensing and geographic information system data. The spatiotemporal evolution of the NGP rate (NGPR) was also investigated. The dominant factors driving NGP progression and associated interactions were identified using geographic detectors. From 2000 to 2018, the NGPR gradually decreased from 63.02% to 52.82%. NGPR was high in the west and low in the east, and its spatial differentiation and clustering patterns were statistically significant. Precipitation, temperature, altitude, and soil carbon content were the dominant factors affecting the spatial differentiation in NGPR. The interaction between these factors enhanced the spatial differentiation.

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Spatial-temporal evolution of coupling relationship between land development intensity and resources environment carrying capacity in China

Cited by 64 publications

References 51 publications

Prediksi Perubahan Tutupan Lahan di Kecamatan Sirimau, Kota Ambon Menggunakan Celular Automata- Markov Chain

Prediksi Perubahan Tutupan Lahan di Kecamatan Sirimau, Kota Ambon Menggunakan Celular Automata- Markov Chain

Analisis Dan Prediksi Perubahan Tutupan Lahan Menggunakan Model Celular Automata-Markov Chain Di Das Wae Ruhu Kota Ambon

Spatiotemporal Evolution of Non-Grain Production of Cultivated Land and Its Underlying Factors in China

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