Unveiling the Complexities of Land Use Transition in Indonesia’s New Capital City IKN Nusantara: A Multidimensional Conflict Analysis DOI Creative Commons
Alfath Satria Negara Syaban, Seth Appiah‐Opoku

Land, Journal Year: 2024, Volume and Issue: 13(5), P. 606 - 606

Published: April 30, 2024

The relocation of Indonesia’s capital to the IKN (Ibu Kota Negara) Nusantara in East Kalimantan is leading significant changes land use, shifting from natural vegetation and agriculture urban infrastructure. This transition brings about economic diversification expansion, but it also raises concerns its impact on society, economy, environment. rapid development affects biodiversity conservation, food security, livelihoods rural Indigenous communities, conflicts across social dimensions. research uses qualitative quantitative data examine socio-economic environmental area 2003 2023. findings show a notable increase built-up areas, indicating urbanization decrease agricultural land. study discusses implications for local populations ecosystems, emphasizing need inclusive governance, community participation, conflict resolution. It proposes comprehensive policy framework that promotes sustainable management, recognizes rights, fosters growth respect rich cultural heritage.

Language: Английский

Comparison of accuracy and reliability of random forest, support vector machine, artificial neural network and maximum likelihood method in land use/cover classification of urban setting DOI Creative Commons
Md. Sharafat Chowdhury

Environmental Challenges, Journal Year: 2023, Volume and Issue: 14, P. 100800 - 100800

Published: Nov. 27, 2023

Language: Английский

Citations

79
Advancing County-Level Potato Cultivation Area Extraction: A Novel Approach Utilizing Multi-Source Remote Sensing Imagery and the Shapley Additive Explanations–Sequential Forward Selection–Random Forest Model DOI Creative Commons

Qiao Li,

Xueliang Fu, Honghui Li

et al.

Agriculture, Journal Year: 2025, Volume and Issue: 15(1), P. 92 - 92

Published: Jan. 3, 2025

Potato, a vital food and cash crop, necessitates precise identification area estimation for effective planting planning, market regulation, yield forecasting. However, extracting large-scale crop areas using satellite remote sensing is fraught with challenges, such as low spatial resolution, cloud interference, revisit cycle limitations, impeding the creation of high-quality time–series datasets. In this study, we developed high-resolution vegetation index by calculating coordination coefficients integrating reflectance data from Landsat-8, Landsat-9, Sentinel-2 satellites. The were enhanced through linear interpolation Savitzky–Golay (S-G) filtering to reconstruct data. We employed harmonic analysis NDVI (HANTS) method extract features evaluated classification accuracy across five feature sets: features, band means, texture color space features. Random Forest (RF) model, utilizing full set, emerged most accurate, achieving precision rate 0.97 kappa value 0.94. further refined subset SHAP-SFS selection method, leading SHAP-SFS-RF approach differentiating potato non-potato crops. This approximately 0.1 around 0.2 compared RF extracted closely aligning statistical yearbook Our study successfully achieved accurate extraction at county level, offering novel insights methodologies related research fields.

Language: Английский

Citations

2
Identifying land use land cover change using google earth engine: a case study of Narayanganj district, Bangladesh DOI Creative Commons
Sk. Mafizul Haque,

A S M Shanawaz Uddin

Theoretical and Applied Climatology, Journal Year: 2025, Volume and Issue: 156(2)

Published: Jan. 10, 2025

Language: Английский

Citations

2
Deep Learning Semantic Segmentation for Land Use and Land Cover Types Using Landsat 8 Imagery DOI Creative Commons
Wuttichai Boonpook, Yumin Tan, Attawut Nardkulpat

et al.

ISPRS International Journal of Geo-Information, Journal Year: 2023, Volume and Issue: 12(1), P. 14 - 14

Published: Jan. 7, 2023

Using deep learning semantic segmentation for land use extraction is the most challenging problem in medium spatial resolution imagery. This because of convolution layer and multiple levels steps baseline network, which can cause a degradation small features. In this paper, algorithm comprises an adjustment network architecture (LoopNet) dataset proposed automatic classification using Landsat 8 The experimental results illustrate that (SegNet, U-Net) outperforms pixel-based machine algorithms (MLE, SVM, RF) classification. Furthermore, LoopNet convolutional loop block, superior to other networks U-Net, PSPnet) improvement (ResU-Net, DeeplabV3+, U-Net++), with 89.84% overall accuracy good results. evaluation multispectral bands demonstrates Band 5 has performance terms accuracy, 83.91% accuracy. combination different spectral (Band 1–Band 7) achieved highest result (89.84%) compared individual bands. These indicate effectiveness

Language: Английский

Citations

28
Quantitative assessment of Land use/land cover changes in a developing region using machine learning algorithms: A case study in the Kurdistan Region, Iraq DOI Creative Commons
Abdulqadeer Rash, Yaseen T. Mustafa, Rahel Hamad

et al.

Heliyon, Journal Year: 2023, Volume and Issue: 9(11), P. e21253 - e21253

Published: Oct. 24, 2023

The identification of land use/land cover (LULC) changes is important for monitoring, evaluating, and preserving natural resources. In the Kurdistan region, utilization remotely sensed data to assess effectiveness machine learning algorithms (MLAs) LULC classification change detection analysis has been limited. This study monitors analyzes in area from 1991 2021 using a quantitative approach with multi-temporal Landsat imagery. Five MLAs were applied: Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), Extreme Gradient Boosting (XGBoost). results showed that RF algorithm produced most accurate maps three-decade period, accompanied by high kappa coefficient (0.93-0.97) compared SVM (0.91-0.95), ANN (0.91-0.96), KNN (0.92-0.96), XGBoost (0.92-0.95) algorithms. Consequently, classifier was implemented categorize all obtainable satellite images. Socioeconomic throughout these transition periods revealed results. Rangeland barren areas decreased 11.33 % (-402.03 km2) 6.68 (-236.8 km2), respectively. transmission increases 13.54 (480.18 3.43 (151.74 0.71 (25.22 occurred agricultural land, forest, built-up areas, outcomes this contribute significantly monitoring developing regions, guiding stakeholders identify vulnerable better use planning sustainable environmental protection.

Language: Английский

Citations

26
Towards Improving Sustainable Water Management in Geothermal Fields: SVM and RF Land Use Monitoring DOI Creative Commons
Widya Utama, Rista Fitri Indriani, Maman Hermana

et al.

Journal of Human Earth and Future, Journal Year: 2024, Volume and Issue: 5(2), P. 216 - 242

Published: June 1, 2024

The management and monitoring of land use in geothermal fields are crucial for the sustainable utilization water resources, as well striking a balance between production renewable energy preservation environment. This study primarily compared Support Vector Machine (SVM) Random Forest (RF) machine learning methods, using satellite imagery from Landsat 8 Sentinel 2 2021 2023, to monitor Patuha area. objective is improve practices by accurately categorizing different cover types. comparative analysis assessed efficacy these techniques upholding sustainability regions. examined application SVM RF techniques, with particular emphasis on parameter refinement model assessment, enhance classification accuracy. By employing Kernlab e1071 algorithm comparison, research sought produce precise Land Use Model Map, which underscores significance advanced analytical environmental management. approach was utmost importance improving reinforcing practices. evaluation methods demonstrates superiority terms accuracy, stability, precision, particularly intricate urban settings, hence establishing it preferred tasks demanding high reliability. areas alignment Sustainable Development Goals (SDGs) 6 15, fosters conservation ecosystems. Doi: 10.28991/HEF-2024-05-02-06 Full Text: PDF

Language: Английский

Citations

17
Multi-spectral remote sensing and GIS-based analysis for decadal land use land cover changes and future prediction using random forest tree and artificial neural network DOI
Quoc Bao Pham, Sk Ajim Ali, Farhana Parvin

et al.

Advances in Space Research, Journal Year: 2024, Volume and Issue: 74(1), P. 17 - 47

Published: March 15, 2024

Language: Английский

Citations

16
Exploring the impact of land use/land cover changes on the dynamics of Deepor wetland (a Ramsar site) in Assam, India using geospatial techniques and machine learning models DOI
Tamal Kanti Saha, Haroon Sajjad,

Roshani Singh

et al.

Modeling Earth Systems and Environment, Journal Year: 2024, Volume and Issue: 10(3), P. 4043 - 4065

Published: April 15, 2024

Language: Английский

Citations

15
An integrated GEE and machine learning framework for detecting ecological stability under land use/land cover changes DOI Creative Commons

Atiyeh Amindin,

Narges Siamian,

Narges Kariminejad

et al.

Global Ecology and Conservation, Journal Year: 2024, Volume and Issue: 53, P. e03010 - e03010

Published: May 27, 2024

Ecological stability (ES) is recognized as a crucial factor for sustainable development at global and regional scales. However, the importance of this was not considered significant. Hence, main aim study to introduce new approach that focuses on detecting ES over Maharloo watershed in Iran. To achieve goal, we extracted land use cover (LULC) data from Google Earth Engine (GEE) platform by applying random forest (RF) machine learning method, which obtained Kappa statistics 0.85, 0.86, 0.87 years 2002, 2013, 2023, respectively. We identified both stable unstable regions based LULC changes employed them using forecast ES. The most important predictors ecological were elevation, soil organic carbon index, precipitation, salinity. results research revealed certain areas within have experienced instability recent years, with gardens showing highest percentage (60.65%) among all land-use categories. performance validation our model suggest are reliable (AUC = 0.86). This offers detailed maps trends, offering valuable insights decision makers support landscape conservation restoration efforts. Overall, findings contribute more comprehensive understanding dynamics provide efforts other regions.

Language: Английский

Citations

12
Utilizing Multitemporal Indices and Spectral Bands of Sentinel-2 to Enhance Land Use and Land Cover Classification with Random Forest and Support Vector Machine DOI

Atefe Arfa,

Masoud Minaei

Advances in Space Research, Journal Year: 2024, Volume and Issue: 74(11), P. 5580 - 5590

Published: Aug. 30, 2024

Language: Английский

Citations

10