Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data

Remote Sensing, Год журнала: 2024, Номер 16(5), С. 928 - 928

Опубликована: Март 6, 2024

Wetlands provide vital ecological and socioeconomic services but face escalating pressures worldwide. This study undertakes an integrated spatiotemporal assessment of the multifaceted vulnerabilities shaping Khinjhir Lake, ecologically significant wetland ecosystem in Pakistan, using advanced geospatial machine learning techniques. Multi-temporal optical remote sensing data from 2000 to 2020 was analyzed through spectral water indices, land cover classification, change detection risk mapping examine moisture variability, modifications, area changes proximity-based threats over two decades. The random forest algorithm attained highest accuracy (89.5%) for classification based on rigorous k-fold cross-validation, with a training 91.2% testing 87.3%. demonstrates model’s effectiveness robustness vulnerability modeling area, showing 11% shrinkage open bodies since 2000. Inventory zoning revealed 30% present-day areas under moderate high vulnerability. cellular automata–Markov (CA–Markov) model predicted continued long-term declines driven by swelling anthropogenic like 29 million population growth surrounding Lake. research integrating satellite analytics, algorithms spatial generate actionable insights into guide conservation planning. findings robust baseline inform policies aimed at ensuring health sustainable management Lake wetlands human climatic that threaten functioning these ecosystems.

Язык: Английский

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Random forest-based analysis of land cover/land use LCLU dynamics associated with meteorological droughts in the desert ecosystem of Pakistan

Ecological Indicators, Год журнала: 2024, Номер 159, С. 111670 - 111670

Опубликована: Фев. 1, 2024

Dry land ecosystems extend over 40 % of the Earth, supporting an estimated 3 billion human population. Thus, quantifying LCLU changes in such is essential for achieving sustainable development goals. In this context, research aimed to examine past three decades (1990 – 2020) arid ecosystem Pakistan, i.e., Cholisatn desert. Three remote sensing indices, normalized difference vegetation index (NDVI), barren (NDBaI), and top grain soil (TGSI) are taken as representatives their temporal relationship associated with meteorological drought, e.g. standardized precipitation (SPI). Moreover, machine learning-based random forest (RF) classification followed by change detection techniques was implemented. Results from RF classifier revealed applicability accurately predicting LULC validation overall accuracy 0.99. Output interesting finding where desert experienced significant last decades. The highest expansion (4.4 %) took place 2014 2020 at expense reduction (-6.3 %). Mann-Kendall trend (MK) Sen's slope (SS) analysis showed a (P < 0.001) increasing NDVI (SS = 0.004), SPI 0.01 0.04) decreasing NDBaI TGSI -0.001, −0.005). Interestingly, positive Pearson correlation range (r 0.6–0.8) SPI-1 6, negative 0.5–0.7) indices reveals strong linear between drought. provides substantial implications policy makers stakeholders emphasizing need proactive strategies drought resistant improve maintain ecological health combating impacts climatic change.

Язык: Английский

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Leveraging Machine Learning for Analyzing the Nexus Between Land Use and Land Cover Change, Land Surface Temperature And Biophysical Indices in an Eco-Sensitive Region of Brahmani-Dwarka Interfluve

Results in Engineering, Год журнала: 2024, Номер unknown, С. 102854 - 102854

Опубликована: Сен. 1, 2024

Язык: Английский

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Using Enhanced Gap-Filling and Whittaker Smoothing to Reconstruct High Spatiotemporal Resolution NDVI Time Series Based on Landsat 8, Sentinel-2, and MODIS Imagery

ISPRS International Journal of Geo-Information, Год журнала: 2023, Номер 12(6), С. 214 - 214

Опубликована: Май 23, 2023

Normalized difference vegetation index (NDVI) time series data, derived from optical images, play a crucial role for crop mapping and growth monitoring. Nevertheless, images frequently exhibit spatial temporal discontinuities due to cloudy rainy weather conditions. Existing algorithms reconstructing NDVI using multi-source remote sensing data still face several challenges. In this study, we proposed novel method, an enhanced gap-filling Whittaker smoothing (EGF-WS), reconstruct (EGF-NDVI) Google Earth Engine. EGF-WS, calculated MODIS, Landsat-8, Sentinel-2 satellites were combined generate high-resolution continuous data. The MODIS was employed as reference fill missing pixels in the Sentinel–Landsat (SL-NDVI) method. Subsequently, filled smoothed filter reduce residual noise SL-NDVI series. With all-round performance assessment (APA) metrics, of EGF-WS compared with conventional Savitzky–Golay approach (GF-SG) Fusui County Guangxi Zhuang Autonomous Region. experimental results have demonstrated that can capture more accurate details GF-SG. Moreover, EGF-NDVI exhibited low root mean square error (RMSE) high coefficient determination (R2). conclusion, holds significant promise providing resolution 10 m 8 days, thereby benefiting mapping, land use change monitoring, various ecosystems, among other applications.

Язык: Английский

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Marsh decrease was much faster than the water increase among the Yellow River Source wetlands during 1986–2022

The Science of The Total Environment, Год журнала: 2024, Номер 947, С. 174377 - 174377

Опубликована: Июль 4, 2024

Язык: Английский

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Analyzing Land Use/Land Cover Changes Using Google Earth Engine and Random Forest Algorithm and Their Implications to the Management of Land Degradation in the Upper Tekeze Basin, Ethiopia

The Scientific World JOURNAL, Год журнала: 2024, Номер 2024(1)

Опубликована: Янв. 1, 2024

Land use and land cover change (LULCC) without appropriate management practices has been identified as a major factor contributing to degradation, with significant impacts on ecosystem services climate hence human livelihoods. Therefore, up‐to‐date accurate LULCC data maps at different spatial scales are for regular monitoring of existing ecosystems, proper planning natural resource management, promotion sustainable regional development. This study investigates the temporal dynamics (LULC) changes over 31 years (1990–2021) in upper Tekeze River basin, Ethiopia, utilizing advanced remote sensing techniques such Google Earth Engine (GEE) Random Forest (RF) algorithm. Landsat surface reflectance images from Thematic Mapper (TM) (1990, 2000, 2010) 8 Operational imager (OLI) sensors (2021) were used. Besides, auxiliary utilized improve classification LULC classes. was classified using algorithm (GEE). The OpenLand R package used map transition intensity across period. Despite complexity topographic climatic features area, RF achieved high accuracy 0.83 0.75 overall Kappa values, respectively. results 1990 2021 showed that forest, bushland, shrubland, bareland decreased by 12.2, 24.8, 1.2, 15.4%, Bareland changed farmland, settlement, dry riverbed stream channels. Expansion channels sandy surfaces observed 2021. Bushland shown an increment 17.2% 1900 2010 but 19.5% Throughout period, water, riverbeds, urban settlements positive net gains 484, 8.7, 82, 26778.5%, However, bush, shrub, experienced 12.17, 15.37% losses. degradation future vulnerability basin which would serve evidence mitigate avoiding conversion shrubland one hand, scaling up farmland afforestation degraded vulnerable areas, other hand.

Язык: Английский

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Identifying land use land cover change using google earth engine: a case study of Narayanganj district, Bangladesh

Theoretical and Applied Climatology, Год журнала: 2025, Номер 156(2)

Опубликована: Янв. 10, 2025

Язык: Английский

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Efficient Argan Tree Deforestation Detection Using Sentinel-2 Time Series and Machine Learning

Applied Sciences, Год журнала: 2025, Номер 15(6), С. 3231 - 3231

Опубликована: Март 16, 2025

The argan tree (Argania spinosa) is a rare species native to southwestern Morocco, valued for its fruit, which produces oil, highly prized natural product with nutritional, health, and cosmetic benefits. However, increasing deforestation poses significant threat survival. This study monitors changes in an forest near Agadir, from 2017 2023 using Sentinel-2 satellite imagery advanced image processing algorithms. Various machine learning models were evaluated detection, LightGBM achieving the highest accuracy when trained on dataset integrating spectral bands, temporal features, vegetation indices information. model achieved 100% tabular test data 85% image-based data. generated maps estimated approximate loss of 2.86% over six years. explores methods enhance detection accuracy, provides valuable statistical mitigation, highlights critical role remote sensing, processing, artificial intelligence environmental monitoring conservation, particularly forests.

Язык: Английский

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Annual 30 m land cover dataset on the Tibetan Plateau from 1990 to 2023

Scientific Data, Год журнала: 2025, Номер 12(1)

Опубликована: Март 27, 2025

Accurate land cover data was fundamental for formulating sound planning and sustainable development strategies. This study focused on the Tibetan Plateau (TP), a globally sensitive ecological area, developed locally tailored annual 30 m resolution dataset from 1990 to 2023 (TPLCD). Leveraging Google Earth Engine (GEE) platform Landsat processing, LandTrendr employed generate robust, high-precision training samples. Subsequently, random forest classification spatiotemporal smoothing strategies were applied precisely map dynamics of TP. Rigorous validation through visual interpretation, authoritative third-party datasets (Geo-Wiki GLCVSS), thematic cross-comparisons, revealed an overall accuracy 84.8%, Kappa coefficient 0.78, fully affirming dataset's high reliability. provided invaluable empirical evidence understanding vulnerability adaptability TP's ecosystem.

Язык: Английский

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Influence of Avocado Plantations as Driver of Land Use and Land Cover Change in Chile’s Aconcagua Basin

Land, Год журнала: 2025, Номер 14(4), С. 750 - 750

Опубликована: Апрель 1, 2025

Land use and land cover (LULC) change is a dynamic process influenced by various factors, including agricultural expansion. In Chile’s Aconcagua Basin, avocado plantations are potentially driving territorial transformations. However, current data lacks the resolution required to accurately assess this impact. Accordingly, our study used advanced geospatial analysis techniques address gap. Through detailed of spatial temporal changes, it was determined that most significant expansion occurred between 2003 2013, with an increase 402%. This growth primarily took place at expense native vegetation, particularly sclerophyllous shrubland, as well other lands, near urban lands. By 2023, changes in plantation were significantly slower, minimal alterations LULC (5%), suggesting possible influence drought on small-scale farmers. small loss mainly replaced fruit farm land. Moreover, findings suggest while have become larger, more dominant, isolated, vegetation has fragmented reduced patch size. Based these results, sustainable management practices proposed. These provide crucial foundation for developing strategies balance production environmental sustainability, landscape transformation well-being local communities.

Язык: Английский

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