Recent global land cover dynamics and implications for soil erosion and carbon losses from deforestation

Hu, Xiangping; Næss, Jan Sandstad; Iordan, Cristina Maria; Huang, Bo; Zhao, Wenwu; Cherubini, Francesco

doi:10.1016/j.ancene.2021.100291

Cited by 64 publications

(28 citation statements)

References 96 publications

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“…En México (Tamaulipas), se determinó que las áreas de cultivos agrícolas aumentaron 7,61% durante el periodo 1987-2017 siendo la cobertura que más incrementó su área (Salinas et al, 2020). Asimismo, Hu et al (2021), obtuvieron que en Brasil, África, Asia central, China oriental y Asia sudoriental las áreas agrícolas se han incrementado. Por el contrario las áreas agrícolas disminuyeron en lugares como en India, Europa, el este de China y el sudeste de Brasil donde es probable que la vegetación natural se incrementó (Mousivand y Jokar, 2019).…”

Section: Materiales Y Metodosunclassified

Cambio de cobertura y uso de la tierra por actividades antrópicas en el distrito de Chota, periodo 2003/2019

Cieza¹,

Gonzáles²,

Cieza-Tarrillo³

et al. 2021

Nor@ndina

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Se tuvo por objetivo evaluar el cambio de la cobertura y uso de la tierra por actividades antrópicas en el distrito de Chota-Perú, durante los años 2003 y 2019. Se usó análisis de imágenes satelitales Landsat, se utilizó la metodología Corine Land Cover, en un nivel II y III. Se identificaron nueve coberturas de la tierra: tejido urbano continuo, cultivos transitorios, pastos, bosques plantados, herbazal, arbustal, afloramientos rocosos, tierras desnudas y lagunas, lagos y ciénagas naturales permanentes. En total, el cambio producido fue de 6 804,06 ha (25,36%), mientras que 20 022,77 ha (74,64%) no experimentó cambio alguno. En el mismo periodo, las coberturas de la tierra que aumentaron su área fueron los cultivos transitorios en 10,94% (2 934,35 ha), tierras desnudas en 1,11% (298,91 ha), el tejido urbano continuo en 0,73% (196,33 ha) y los bosques plantados en 0.53% (143,34 ha); en tanto las coberturas que disminuyeron su área fueron el herbazal en 6,91% (1 853,57 ha), el arbustal en 4,79% (1 284,86 ha), los pastos en 1,55% (416,02 ha), el afloramiento rocoso en 0,06% (16,25 ha) y lagunas, lagos y ciénagas naturales permanentes en 0,01% (2,03 ha). El factor principal de los cambios ocasionados sobre la cobertura y uso de la tierra en el distrito de Chota es el crecimiento poblacional de 7%, ejerciendo presión sobre estas áreas, con una clara tendencia hacia el incremento de las áreas agrícolas y el crecimiento urbano, produciéndose un cambio de cobertura y uso de la tierra del 25,36%.

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Section: Materiales Y Metodosunclassified

Cambio de cobertura y uso de la tierra por actividades antrópicas en el distrito de Chota, periodo 2003/2019

Cieza¹,

Gonzáles²,

Cieza-Tarrillo³

et al. 2021

Nor@ndina

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“…In 2018, it was forecasted that between 2015 and 2030, about 11% of agricultural land in the European Union would be under high potential risk of abandonment 4 . Deforestation as a form of land cover change is proven to be directly linked to soil erosion and climate change 5 . Rural depopulation is becoming a central theme of research impacting both demography as well as land use 6 .…”

Section: Introductionmentioning

confidence: 99%

Integrated usage of historical geospatial data and modern satellite images reveal long-term land use/cover changes in Bursa/Turkey, 1858–2020

Osgouei

Sertel

Kabadayı

2022

Sci Rep

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Land surface of the Earth has been changing as a result of human induced activities and natural processes. Accurate representation of landscape characteristics and precise determination of spatio-temporal changes provide valuable inputs for environmental models, landscape and urban planning, and historical land cover change analysis. This study aims to determine historical land use and land cover (LULC) changes using multi-modal geospatial data, which are the cadastral maps produced in 1858, monochrome aerial photographs obtained in 1955, and multi-spectral WorldView-3 satellite images of 2020. We investigated two pilot regions, Aksu and Kestel towns in Bursa/Turkey, to analyze the long-term LULC changes quantitatively and to understand the driving forces that caused the changes. We propose methods to facilitate the preparation of historical datasets for the LULC change detection and present an object-oriented joint classification scheme for multi-source datasets to accurately map the spatio-temporal changes. Our approach minimized the amount of manual digitizing required for the boundary delineation of LULC classes from historical geospatial data. Also, our quantitative analysis of LULC maps indicates diverging developments for the selected locations in the long period of 162 years. We observed rural depopulation and gradual afforestation in Aksu; whereas, agricultural land abandonment and deforestation in Kestel.

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“…On the one hand, it can be caused by natural factors such as floods, sea level rise, or wildfires. Climate change is also causing LC changes, such as favoring tree expansion in mountain areas, or greening, or forest degradation [15][16][17]. Anthropogenic factors, such as deforestation or expansion of agricultural land, also have a significant and often dominant impact on an LC transition.…”

Section: Introductionmentioning

confidence: 99%

Unraveling the Impact of Land Cover Changes on Climate Using Machine Learning and Explainable Artificial Intelligence

Kolevatova

Riegler

Cherubini

et al. 2021

BDCC

Self Cite

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A general issue in climate science is the handling of big data and running complex and computationally heavy simulations. In this paper, we explore the potential of using machine learning (ML) to spare computational time and optimize data usage. The paper analyzes the effects of changes in land cover (LC), such as deforestation or urbanization, on local climate. Along with green house gas emission, LC changes are known to be important causes of climate change. ML methods were trained to learn the relation between LC changes and temperature changes. The results showed that random forest (RF) outperformed other ML methods, and especially linear regression models representing current practice in the literature. Explainable artificial intelligence (XAI) was further used to interpret the RF method and analyze the impact of different LC changes on temperature. The results mainly agree with the climate science literature, but also reveal new and interesting findings, demonstrating that ML methods in combination with XAI can be useful in analyzing the climate effects of LC changes. All parts of the analysis pipeline are explained including data pre-processing, feature extraction, ML training, performance evaluation, and XAI.

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Recent global land cover dynamics and implications for soil erosion and carbon losses from deforestation

Cited by 64 publications

References 96 publications

Cambio de cobertura y uso de la tierra por actividades antrópicas en el distrito de Chota, periodo 2003/2019

Cambio de cobertura y uso de la tierra por actividades antrópicas en el distrito de Chota, periodo 2003/2019

Integrated usage of historical geospatial data and modern satellite images reveal long-term land use/cover changes in Bursa/Turkey, 1858–2020

Unraveling the Impact of Land Cover Changes on Climate Using Machine Learning and Explainable Artificial Intelligence

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