Integrating forest cover change and carbon storage dynamics: Leveraging Google Earth Engine and InVEST model to inform conservation in hilly regions

Ecological Indicators, Journal Year: 2023, Volume and Issue: 152, P. 110374 - 110374

Published: May 22, 2023

Forests are vital in combating climate change by storing and sequestrating CO2 from the atmosphere. Measuring influence of land use/land cover (LULC) changes on capacity carbon storage (CS) within forest ecosystems presents a significant challenge. This study employs remote sensing techniques to examine spatiotemporal patterns CS Chittagong Hill Tracts (CHT), resulting LULC alterations between 1996 2021. were identified for six different years utilizing Google Earth Engine (GEE). The Integrated Valuation Ecosystem Services Tradeoffs (InVEST) model was combined with GEE evaluate changing CS. discovered that CHT region experienced loss 21.65 × 106 Mg CS, owing 21% reduction vegetation (2862.85 km^2) during period. central city area (Chittagong) accounted most (7.99 Mg), while suburban areas Khagrachari (0.92 Mg) Rangamati (3.53 contributed least. multiple regression revealed elevation characteristics significantly influenced findings underscore importance developing policies strategies mitigate adverse effects advocate sustainable management practices strike balance ecological, social, economic concerns.

Language: Английский

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Crop monitoring by multimodal remote sensing: A review

Remote Sensing Applications Society and Environment, Journal Year: 2023, Volume and Issue: 33, P. 101093 - 101093

Published: Nov. 10, 2023

Effective approaches to achieve food safety and security can prevent catastrophic situations. Therefore, it is required monitor agricultural crops on a regular basis. This be easily achieved by capturing data from various remote sensing (RS) devices followed processing them. Most RS are useful in monitoring analysing different stages of plant growth successfully. However, individual have some limitations. To overcome this, multimodal (MRS) methods been gradually gaining popularity. In the approach, more than one modality used together obtain better outcome. because, modalities when complement each other same objective combining their strengths reducing limitations, simultaneously. MRS found particularly for crop as they allow integration multiple sources, resulting comprehensive understanding development. By using methods, possible accurate detailed analysis conditions, leading improved decision-making ultimately, yields. this paper, we will explore how successfully utilised obtained these provide valuable insights into health development plants.

Language: Английский

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Towards Improving Sustainable Water Management in Geothermal Fields: SVM and RF Land Use Monitoring

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

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Evaluating the ecological security of ecotourism in protected area based on the DPSIR model

Ecological Indicators, Journal Year: 2023, Volume and Issue: 155, P. 110957 - 110957

Published: Sept. 19, 2023

Evaluating the ecological security of ecotourism (EES) in protected areas is critical because these play a vital role protecting biodiversity and natural resources. This study evaluated EES status Central Alborz Protected Area (Northern Iran), based on Driver, Pressure, State, Impact, Response (DPSIR) model. We developed comprehensive list 59 indicators for DPSIR model employed an Analytical Network Process (ANP) to determine indicator weights harnessing opinion experts which are most influential. approach facilitated identification regions with highest vulnerability, notably northern western sectors our area along boundary between Tehran Mazandaran provinces. Here, mechanisms that drive change include activities, livestock overgrazing, uncontrolled physical economic extensive road highway development, land use cover changes. Indicators effective determining status, activities. conclude by discussing respond increasing threat Areas such as involvement government strategic integrated management. Our serves methodological blueprint how evaluate Areas.

Language: Английский

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Using machine learning and remote sensing to track land use/land cover changes due to armed conflict

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 898, P. 165600 - 165600

Published: July 18, 2023

Armed conflicts have detrimental impacts on the environment, including land systems. The prevailing understanding of relation between Land Use/Land Cover (LULC) and armed conflict fails to fully recognize complexity their dynamics - a shortcoming that could undermine food security sustainable land/water resources management in settings. Syrian portion transboundary Orontes River Basin (ORB) has been site violent since 2013. Correspondingly, Lebanese Turkish portions ORB seen large influxes refugees. A major challenge any geoscientific investigation this region, specifically portion, is unavailability directly-measured "ground truth" data. To circumvent problem, we develop novel methodology combines remote sensing products, machine learning techniques quasi-experimental statistical analysis better understand LULC changes 2004 2022. Through resulting annual maps, can draw several quantitative conclusions. Cropland areas decreased by 21-24 % Syria's hotspot zones after 2013, whereas 3.4-fold increase was detected Lebanon. development refugee settlements also tracked Lebanon Syrian/Turkish borders, revealing different patterns depend settlement dynamics. results highlight importance heterogenous spatio-temporal conflict-affected refugee-hosting countries. developed flexible, cloud-based approach be applied wide variety investigations related conflict, policy climate.

Language: Английский

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Global trend assessment of land use and land cover changes: A systematic approach to future research development and planning

Journal of King Saud University - Science, Journal Year: 2024, Volume and Issue: 36(7), P. 103262 - 103262

Published: May 17, 2024

The diverse landscape of global land use and cover (LULC) change studies were evaluated to uncover the current advances in data future research potential through bibliometrics Preferred Reporting Items for Systematic Reviews Meta-Analyses (PRISMA) approach. A total 2710 published articles with search phrase "Land Land change" OR "Land-use use/Land changes" retrieved using Scopus, Web Science (WOS), ScienceDirect databases from 1993 2022. findings indicated a 24.37% annual growth rate LULC studies, reflecting rapid overall increase articles. China USA emerged as most influential countries regarding article numbers, citations, single-country publications. Ethiopia, Ghana, South Africa, among top 20 rankings underscore importance this research. However, disparity output between multiple-country publications dominant trend highlights geographical bias particularly Global South. This finding underscores need more balanced approach can stimulate further investigation. results also revealed that remote sensing, rapidly growing field utilising advanced computing techniques, is prevalent keyword has significant applications reducing degradation. These significantly enhance research, climate policy programs, management, forest ecology planning, which are crucial face demand agriculture habitable land.

Language: Английский

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A novel geospatial machine learning approach to quantify non-linear effects of land use/land cover change (LULCC) on carbon dynamics

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2024, Volume and Issue: 128, P. 103712 - 103712

Published: Feb. 20, 2024

Land Use and Cover Change (LULCC) introduces considerable uncertainties in its relationship with carbon emissions, posing challenges to nature-based climate mitigation. The effective establishment protection of sinks through land use management lack clarity. To address this challenge, study utilized top-down interannual grided CO2 data from satellite observations, revealing a 7 % decline mainland China 2016 2019. Faced anomaly, we proposed novel approach that combined machine learning traditional regression analysis, investigate the nonlinear between annual spatiotemporal variations net exchange LULCC. Sentinel-2 imagery was employed for high-resolution (10 m) LULC classification based on uniform rules. Particularly, time-series class probabilities were considered estimated using deep framework via Google Earth Engine (GEE) cloud platform, which allows us access effects dynamic GIS methods applied enhance interpretability, integrating multi-source remote sensing datasets, particularly capturing features aspects LULCC emissions. Threshold revealed how transformed areas associated or sources. results mapped sink shrinkage locations, highlighting correlations significant reductions snow cover (−6.25 %), changing water patterns (−1.3 urban expansion (1 mixed forest changes (regrowth 4 deforestation −1%). This research aims advance understanding emissions sensing, bridging different observation within geostatistical context, expects provide new validation method bottom-up approach.

Language: Английский

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Modeling of land use and land cover changes using google earth engine and machine learning approach: implications for landscape management

ENVIRONMENTAL SYSTEMS RESEARCH, Journal Year: 2024, Volume and Issue: 13(1)

Published: Aug. 14, 2024

A precise and up-to-date Land Use Cover (LULC) valuation serves as the fundamental basis for efficient land management. Google Earth Engine (GEE), with its numerous machine learning algorithms, is now most advanced open-source global platform rapid accurate LULC classification. Thus, this study explores dynamics of changes between 1993 2023 using Landsat imagery algorithms in (GEE) platform. Focus group discussion key informant interviews were also used to get further data regarding dynamics. Support Vector Machine (SVM), Random Forest (RF), Classification Regression Trees (CART) demonstrated Six types (agricultural land, grazingland, shrubland, built-up area, forest bareland) identified mapped 1993, 2003, 2013, 2023. The overall accuracy kappa coefficient that RF images comprising auxiliary variables (spectral indices topographic data) performed better than SVM CART. Despite being common type LULC, agricultural shows a trend shrinking during period. area bareland exhibits progressive expansion. amount shrubland has decreased over last 20 years, whereas grazinglands have exhibited expanding trends. Population growth, expansion, fuelwood collection, charcoal production, areas illegal settlement intervention are among causes shifts. This provides reliable information about patterns Robit watershed, which can be develop frameworks watershed management sustainability.

Language: Английский

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Land-Use Land-Cover Dynamics and Future Projections Using GEE, ML, and QGIS-MOLUSCE: A Case Study in Manisa

Sustainability, Journal Year: 2025, Volume and Issue: 17(4), P. 1363 - 1363

Published: Feb. 7, 2025

Urban expansion reshapes spatial patterns over time, leading to complex challenges such as environmental degradation, resource scarcity, and socio-economic inequality. It is critical anticipate these transformations in order devise proactive urban policies implement sustainable planning practices that minimize negative impacts on ecosystems human livelihoods. This study investigates LULC changes the rapidly urbanizing Manisa metropolitan area of Turkey using Sentinel-2 satellite imagery advanced machine learning algorithms. High-accuracy maps were generated for 2018, 2021, 2024 Random Forest, Support Vector Machine, k-Nearest Neighbors, Classification Regression Trees Among these, Forest algorithm demonstrated superior accuracy consistency distinguishing land-cover classes. Future scenarios 2027 2030 simulated Cellular Automata–Artificial Neural Network model QGIS MOLUSCE plugin. The results indicate significant growth, with built-up areas projected increase by 23.67% between 2030, accompanied declines natural resources bare land water bodies. highlights implications regarding ecological balance demonstrates importance integrating simulation models forecast use changes, enabling management. Overall, effective must be developed manage urbanization conduct a balanced manner.

Language: Английский

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Detection of Water Hyacinth (Eichhornia crassipes) in Lake Tana, Ethiopia, Using Machine Learning Algorithms

Water, Journal Year: 2023, Volume and Issue: 15(5), P. 880 - 880

Published: Feb. 24, 2023

Lake Tana is Ethiopia’s largest lake and infested with invasive water hyacinth (E. crassipes), which endangers the lake’s biodiversity habitat. Using appropriate remote sensing detection methods determining seasonal distribution of weed important for decision-making, resource management, environmental protection. As demand reliable estimation E. crassipes mapping from satellite data grows, comparing performance different machine learning algorithms could help in identifying most effective method lake. Therefore, this study aimed to examine ability random forest (RF), support vector (SVM), classification regression tree (CART) detect estimating spatial coverage on Google Earth Engine (GEE) platform using Landsat 8 Sentinel 2 images. Cloud-masked monthly median composite October 2021 2022, January 2022 2023, March June were used represent autumn, winter, spring, summer, respectively. Four spectral indices derived combination bands improve accuracy. All achieved greater than 95% 90% overall accuracy when images, both sets, all a 93% F1 score detection. Though difference between was small, RF accurate, while SVM CART had same The maximum area observed autumn (22.4 km2), minimum (2.2 km2) summer. Based data, decreased significantly by 62.5% winter spring increased 81.7% summer autumn. findings suggested that classifier accurate algorithm, an season Tana.

Language: Английский

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