Spatiotemporal trends and drivers of forest cover change in Metekel Zone forest areas, Northwest Ethiopia

Environmental Monitoring and Assessment, Год журнала: 2024, Номер 196(12)

Опубликована: Ноя. 6, 2024

The spatiotemporal dynamics of forest cover are essential for understanding the patterns and processes change over time space. This research focused on trends drivers in Metekel Zone Northwest Ethiopia. Landsat 5, 7, 8 imagery, covering period from 1986 to 2019, were used land use/cover classification. Land classification was performed using random (RF) support vector machine (SVM) algorithms Google Earth Engine (GEE) platform, with training samples obtained through visual image interpretation. Spectral indices, such as normalized difference vegetation index, soil-adjusted leaf area water analyzed examine time. In addition, key informant interviews (KIIs) focus group discussions (FGDs) conducted. Findings revealed that decreased significantly 51.37% 17.20% driven largely by human activities agricultural expansion, increased demand firewood, urban expansion. spectral indices further corroborated finding study region (mainly southwestern part) substantially 2019. Concerning depletion, lack local community awareness has become a challenge. problem is attributed communities prioritizing immediate needs fuel agriculture long-term conservation. To combat ongoing deforestation, Administration, collaboration administration office other stakeholders, revisited strengthened existing policies control systems. It also suggested awareness, chiefly among youth, should be enhanced strategic expansion formal nonformal educational initiatives, which empower youth agents promote dissemination knowledge throughout community.

Язык: Английский

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Spatio-Temporal Dynamics of Carbon Storage in Rapidly Urbanizing Shenzhen, China: Insights and Predictions

Land, Год журнала: 2024, Номер 13(10), С. 1566 - 1566

Опубликована: Сен. 26, 2024

Rapid urbanization in developing countries leads to significant land-use and land-cover change (LULCC), which contributes increased carbon dioxide (CO2) emissions the degradation of storage. Studying spatio-temporal changes storage is crucial for guiding sustainable urban development toward neutrality. This study integrates machine-learning random forest algorithm, CA–Markov, InVEST models predict distribution Shenzhen, China, under various scenarios. The findings indicate that, over past two decades, Shenzhen has experienced changes. transformation from high- low-carbon-density land uses, particularly conversion forestland construction land, primary cause loss. Forestland mainly influenced by natural factors, such as digital elevation model (DEM) precipitation, while other (LULC) types are predominantly affected socio-economic demographic factors. By 2030, projected vary significantly across different scenarios, with greatest decline expected scenario (NDS) least ecological priority (EPS). RF-CA–Markov outperforms traditional CA–Markov accurately simulating use, small scattered types. Our conclusions can inform future low-carbon city optimization.

Язык: Английский

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A Comprehensive Review of Smartphone and Other Device-Based Techniques for Road Surface Monitoring

Eng—Advances in Engineering, Год журнала: 2024, Номер 5(4), С. 3397 - 3426

Опубликована: Дек. 16, 2024

Deteriorating road infrastructure is a global concern, especially in low-income countries where financial and technological constraints hinder effective monitoring maintenance. Traditional methods, like inertial profilers, are expensive complex, making them unsuitable for large-scale use. This paper explores the integration of cost-effective, scalable smartphone technologies surface monitoring. Smartphone sensors, such as accelerometers gyroscopes, combined with data preprocessing techniques filtering reorientation, improve quality collected data. Machine learning algorithms, particularly CNNs, utilized to classify anomalies, enhancing detection accuracy system efficiency. The results demonstrate that smartphone-based systems, paired advanced processing machine learning, significantly reduce cost complexity traditional surveys. Future work could focus on improving sensor calibration, synchronization, models handle diverse real-world conditions. These advancements will increase scalability urban areas requiring real-time rapid

Язык: Английский

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Comparative study of multiple algorithms classification for Land Use and Land Cover Change Detection and its impact on local climate of Mardan District, Pakistan

Environmental Challenges, Год журнала: 2024, Номер unknown, С. 101069 - 101069

Опубликована: Дек. 1, 2024

Язык: Английский

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PEMETAAN PENGGUNAAN LAHAN SAWAH BERDASARKAN PENDEKATAN EKOLOGI BENTANG LAHANMENGGUNAKAN CITRA PEREKAMAN TUNGGAL

Jurnal Tanah dan Sumberdaya Lahan, Год журнала: 2025, Номер 12(1), С. 9 - 19

Опубликована: Янв. 1, 2025

A rice field land-use map is essential in the sustainable land management of fields for physical monitoring and planning. Such maps are usually created using multitemporal image data with a spectral approach, but this method can only be applied to certain areas cannot easily other different characteristics. While has been widely used by researchers proven effective, single-date imagery more efficient. This study aimed based on Sentinel-2 landform maps. These were derived through visual interpretation false colour composite bands, DEMNAS, system map. The resulted eleven classes. landscape ecology approach assumed influence landforms land-use. use ten optical bands multispectral classification maximum likelihood algorithm convolutional neural network twelve cover implemented two-dimensional ecological spatial relationship matrix that produced nine obtained an overall accuracy 90,28% Kappa 0,87. result was better than algorithm, which 86,81% 0,83. class had total area 33.686,69 ha mean absolute error (MAE) value 0,0241, while 29.590,21 larger MAE 0,0343.

Язык: Английский

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