EO-data and remote sensing integration for water erosion modelling and mapping in North Tunisia: a case study of Medjerda watershed DOI Creative Commons
Dhouha Ben Othman,

Ershad Ahmed,

Taoufik Hermassi

et al.

Geomatics Natural Hazards and Risk, Journal Year: 2024, Volume and Issue: 15(1)

Published: Nov. 29, 2024

Understanding, mapping and modelling of water erosion process become a serious concern for soil conservation practitioners, as well decision-makers concerned with natural resource management agricultural policies. The current research aims to map quantify rates in the Upper-valley Medjerda Watershed Northern Tunisia. A systematic method incorporating three models (RUSLE: revised loss equation, FAO: food organization, EPM: potential model) was adopted. Indeed, multi-sources earth observation data (EO-data), geographic information systems (GIS), remote sensing (RS) techniques were integrated into process. Mean annual estimated by RUSLE, FAO, EPM vary between 18 71 t/ha/yr. Examination methods reveals that values both FAO EMP are more consistence than RUSLE estimates. about 51% 78% study area is affected moderate very high erosive dynamic. Moreover, six depending on drainage morphometric characteristics adopted calculate sediment delivery ratio (SDR). Key results indicate Maner's SDR model best one yield estimation. findings this work may be helpful mitigation purposes.

Language: Английский

Integrating Google Earth Engine and GIS for RUSLE-based soil erosion and sediment yield assessment in Borkena Watershed, Ethiopia DOI
Asmare Belay Nigussie,

Gebiaw T. Ayeled,

Andualem Endalew

et al.

Journal of Sedimentary Environments, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 10, 2025

Language: Английский

Citations

1
Predicting Soil Erosion Using RUSLE and GeoSOS-FLUS Models: A Case Study in Kunming, China DOI Open Access

Jinlin Lai,

Jiashun Li, Li Liu

et al.

Forests, Journal Year: 2024, Volume and Issue: 15(6), P. 1039 - 1039

Published: June 16, 2024

Revealing the relationship between land use changes and soil erosion provides a reference for formulating future strategies. This study simulated historical based on RULSE GeoSOS-FLUS models used random forest model to explain relative importance of natural anthropogenic factors erosion. The main conclusions are as follows: (1) From 1990 2020, significant in occurred Kunming, with continuous reduction woodland, grassland, cropland, being converted into construction land, which grew by 195.18% compared 1990. (2) During this period, modulus decreased from 133.85 t/(km²·a) 130.32 loss 74,485.46 t/a, mainly due conversion cropland ecological lands (woodland, grassland). (3) expansion will continue, it is expected that 2050, decrease 3.77 t/(km²·a), 4.27 3.27 under development, rapid protection scenarios, respectively. However, scenario, increased 0.26 2020. (4) spatial pattern influenced both factors, human activities intensify future, influence further increase. Traditionally, thought increase loss. Our may offer new perspective provide planning management Kunming.

Language: Английский

Citations

6
GIS-Based Integrated Multi-Hazard Vulnerability Assessment in Makedonska Kamenica Municipality, North Macedonia DOI Creative Commons
Bojana Aleksova, Ivica Milevski, Slavoljub Dragićević

et al.

Atmosphere, Journal Year: 2024, Volume and Issue: 15(7), P. 774 - 774

Published: June 28, 2024

This study presents a comprehensive analysis of natural hazard susceptibility in the Makedonska Kamenica municipality North Macedonia, encompassing erosion assessment, landslides, flash floods, and forest fire vulnerability. Employing advanced GIS remote sensing (RS) methodologies, models were meticulously developed integrated to discern areas facing concurrent vulnerabilities. Findings unveil substantial vulnerabilities prevalent across area, notably along steep terrain gradients, river valleys, deforested landscapes. Erosion assessment reveals elevated rates, with mean coefficient (Z) 0.61 an annual production 182,712.9 m3, equivalent specific rate 961.6 m3/km2/year. Landslide identifies 31.8% exhibiting very high probability while flood depict 3.3% area prone potential. Forest mapping emphasizes slightly less than one-third municipality’s forested is highly or susceptible fires. Integration these elucidates multi-hazard zones, revealing that 11.0% territory faces from excessive erosion, These zones are predominantly located upstream areas, valleys tributaries, estuary region. The identification underscores critical need for targeted preventive measures robust land management strategies mitigate potential disasters safeguard both human infrastructure ecosystems. Recommendations include implementation enhanced monitoring systems, validation community engagement initiatives bolster preparedness response capabilities effectively.

Language: Английский

Citations

4
Soil erosion estimation and risk assessment based on RUSLE in Google Earth Engine (GEE) in Turkiye DOI Creative Commons

Endalamaw Dessie Alebachew,

Wudu Abiye,

Orhan Dengiz

et al.

Annals of GIS, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 19

Published: Jan. 17, 2025

Soil erosion is a critical challenge threatening agricultural productivity and environmental sustainability globally. This study presents the first estimation of water-induced soil loss in Ordu province, Turkey, using Revised Universal Loss Equation (RUSLE) integrated with Google Earth Engine (GEE) Geographic Information System (GIS) technologies. Our analysis provides spatial insights into patterns across region, revealing that rates range from 0–5 t/ha/yr stable areas to over 200 severely eroded regions. The mean rate estimated at 12.58 t/ha/yr. identified LS factor (slope length steepness) as most significant contributor erosion, followed by R (rainfall erosivity). These findings offer valuable dynamics, supporting sustainable management practices informing control strategies. results contribute land use planning policy development aimed mitigating degradation enhancing resilience province.

Language: Английский

Citations

0
Assessing soil erosion and its drivers in agricultural landscapes: a case study in southern Bahia, Brazil DOI Creative Commons
Mathurin François,

Maria Carolina Gonçalves Pontes,

Rodrigo Nogueira de Vasconcelos

et al.

Journal of Water and Climate Change, Journal Year: 2024, Volume and Issue: 15(7), P. 3312 - 3327

Published: June 12, 2024

ABSTRACT Erosion is a worldwide threat to biodiversity conservation and agricultural yield, it linked deforestation. In this study, we aim assess soil loss in landscapes of Cachoeira River watershed, southern Bahia, northeastern Brazil. We estimate the role forests diminishing erosion using Revised Universal Soil Loss Equation (RUSLE). compare real simulated scenarios which forest was replaced by use, also comparing estimates erosivity factor (R factor) derived from remote sensing climatological station data. Real annual losses varied 0 15.95 t/year 33.53 along respectively. However, only 0.04 1.67% area highly severely exposed erosion, data stations sensing, deforested scenario approximately two times higher than loss, indicating importance cover mitigate erosion. Moreover, 10.5 greater when precipitation compared stations. Conclusively, practice agroforestry can be used as an alternative avoid

Language: Английский

Citations

2
EO-data and remote sensing integration for water erosion modelling and mapping in North Tunisia: a case study of Medjerda watershed DOI Creative Commons
Dhouha Ben Othman,

Ershad Ahmed,

Taoufik Hermassi

et al.

Geomatics Natural Hazards and Risk, Journal Year: 2024, Volume and Issue: 15(1)

Published: Nov. 29, 2024

Understanding, mapping and modelling of water erosion process become a serious concern for soil conservation practitioners, as well decision-makers concerned with natural resource management agricultural policies. The current research aims to map quantify rates in the Upper-valley Medjerda Watershed Northern Tunisia. A systematic method incorporating three models (RUSLE: revised loss equation, FAO: food organization, EPM: potential model) was adopted. Indeed, multi-sources earth observation data (EO-data), geographic information systems (GIS), remote sensing (RS) techniques were integrated into process. Mean annual estimated by RUSLE, FAO, EPM vary between 18 71 t/ha/yr. Examination methods reveals that values both FAO EMP are more consistence than RUSLE estimates. about 51% 78% study area is affected moderate very high erosive dynamic. Moreover, six depending on drainage morphometric characteristics adopted calculate sediment delivery ratio (SDR). Key results indicate Maner's SDR model best one yield estimation. findings this work may be helpful mitigation purposes.

Language: Английский

Citations

0