Dinamika Perubahan Tutupan Lahan dan Tantangan Kebijakan Tata Ruang di Kabupaten Wonogiri, Jawa Tengah, Indonesia
Bibliographic record
Abstract
Land cover changes in Wonogiri Regency are closely related to increased urbanization and development in the Subosukowonosraten area, which has an impact on increasing the risk of disasters such as landslides and floods. This study aims to analyze the patterns and trends of land cover changes in 2019, 2022, and 2025 as a basis for evaluating the implementation of the Wonogiri Regency Spatial Plan (RTRW). A spatial quantitative method was used by classifying 10-meter-resolution Sentinel-2 imagery using the Random Forest algorithm through the Google Earth Engine platform. The results of the analysis show a decrease in natural/semi-natural vegetation cover and an increase in residential/mixed land areas, especially in Eromoko, Wonogiri, and Baturetno Districts. Natural/semi-natural vegetation cover decreased from 61.87% (2019) to 60.30% (2025), while residential/mixed buildings increased from 5.40% (2019) to 6.62% (2025). Other cultivated land tends to be stable. This finding indicates the need for regular evaluation of the implementation of the Spatial Planning (RTRW) and spatial use control interventions to reduce disaster risk and maintain the region's ecological function. This study provides multi-temporal empirical evidence based on Sentinel-2 imagery and the Random Forest algorithm in GEE to assess the suitability of the RTRW at the district level and identify priority locations for spatial planning policy revision and mitigation strategies. Perubahan tutupan lahan di Kabupaten Wonogiri menunjukkan keterkaitan erat dengan peningkatan urbanisasi dan pembangunan kawasan Subosukowonosraten, yang berdampak pada meningkatnya risiko bencana seperti longsor dan banjir. Penelitian ini bertujuan untuk menganalisis pola dan tren perubahan tutupan lahan pada tahun 2019, 2022, dan 2025 sebagai dasar evaluasi terhadap implementasi Rencana Tata Ruang Wilayah (RTRW) Kabupaten Wonogiri. Metode kuantitatif spasial digunakan dengan mengklasifikasikan citra Sentinel-2 beresolusi 10 meter menggunakan algoritma Random Forest melalui platform Google Earth Engine. Hasil analisis menunjukkan adanya penurunan tutupan vegetasi alami/semi-alami dan peningkatan luas lahan permukiman/campuran, terutama di Kecamatan Eromoko, Wonogiri, dan Baturetno. Tutupan vegetasi alami/semi-alami menurun dari 61,87% (2019) menjadi 60,30% (2025), sementara bangunan permukiman/campuran meningkat dari 5,40% (2019) menjadi 6,62% (2025). Lahan tanaman budidaya lainnya cenderung stabil. Temuan ini mengindikasikan perlunya evaluasi berkala terhadap pelaksanaan RTRW serta intervensi pengendalian pemanfaatan ruang guna mengurangi risiko bencana dan menjaga fungsi ekologis wilayah. Studi ini memberikan bukti empiris multi-temporal berbasis citra Sentinel-2 dan algoritma Random Forest di GEE untuk menilai kesesuaian RTRW di tingkat kabupaten, serta mengidentifikasi lokasi prioritas bagi revisi kebijakan tata ruang dan strategi mitigasi.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".