Characterizing the impact of Land-Use/Land-Cover changes on a Temperate Forest using the Markov model

Ibarra‐Bonilla, Jesús S.; Villarreal‐Guerrero, Federico; Prieto‐Amparán, Jesús A.; Santellano‐Estrada, Eduardo; Pinedo-Álvarez, Alfredo

doi:10.1016/j.ejrs.2021.11.004

Cited by 8 publications

(4 citation statements)

References 24 publications

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“…Although the changes in total area and proportion of each HCA type are insignificant, the transitions between HCA types are substantial. Previous studies have shown not only changes in the overall structure of the land use and cover (LUCC) system but also that the LULC transitions affects ecosystem [42,43], biodiversity [44], morphology [45] and the Earth surface physical-chemical processes [46,47]. Therefore, future research is expected to explore the ecological consequences and the spillover effects of HCA changes on the regional human-Earth system.…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal Dynamics of the Human Critical Area (HCA) in the “Three Water Lines” Region of Northwest China and the Impact of Socioeconomic Factors between 2000 and 2020

et al. 2022

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The Human Critical Area (HCA) is an area that characterizes the surface landscape created by human beings in the Anthropocene. Based on the signatures left by major human activities over the Earth′s surface, this research demarcates an arid inland region of Northwest China, the “Three Water Lines”, into four HCA types: Agricultural Area, Built-up Area, Ecological Area, and Bare Area. This paper explores the HCA′s distribution and changes in the “Three Water Lines” region between 2000 and 2020 with land use/cover data, as well as the impact of socioeconomic factors on the HCA dynamics with statistics sourcing from authoritative yearbooks. To achieve this, the Land Use Transition Matrix is used to investigate the changes in area and distribution, while binary linear regression and stepwise multiple linear regression are applied to examine the single and joint effects of the socioeconomic factors. The main findings are as follows: (i) The four HCA types are distinguished quantitatively and by their distribution patterns. Ecological Area and Bare Area cover most (more than 90% in total) of the territory with extensive and continuous distribution. Agricultural Area is mainly found on the eastern and western parts of the region, with flat terrain, abundant water resources, and moderate temperatures. Built-up Area is the most concentrated but has an unbalanced distribution and the lowest quantity. (ii) Despite some discernible spatial and quantity changes at regional and county levels between 2000 and 2020, the general characteristics in HCA’s structure and distribution pattern have mainly remained consistent. (iii) Transitions between HCA types occur constantly, and the primary source type of the transitions differs from one another. Ecological Area and Bare Area form the sources of the most evident transitions. (iv) Agricultural Area and Built-up Area are more prone influence from some socioeconomic dynamics. By contrast, there is no evidence that socioeconomic factors directly affect Bare Area. As the first empirical study of the newly conceived concept, Human Critical Area, this paper sheds light on the renovation of geographic traditions of studying the evolution of the human-environment system through the lens of human activities-driven landscape changes.

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Section: Discussionmentioning

confidence: 99%

Spatiotemporal Dynamics of the Human Critical Area (HCA) in the “Three Water Lines” Region of Northwest China and the Impact of Socioeconomic Factors between 2000 and 2020

et al. 2022

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“…The land-use changes in 2015, 2017, and 2019 were simulated using settlement and road networks [35]. As the initial year, land-use change in 2021 was simulated and validated with the 2021 land-use map from the unsupervised classification [36].…”

Section: Lulc Simulation and Changesmentioning

confidence: 99%

Forecasting Rice Status for a Food Crisis Early Warning System Based on Satellite Imagery and Cellular Automata in Malang, Indonesia

et al. 2022

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The increasing population in Indonesia is challenging rice production to feed more people while rice fields are being converted to other land-use land cover (LULC). This study analyzes land use in 2015, 2017, 2019, 2021, and 2025 using an artificial neural network cellular automata (ANN-CA) and rice data from Statistics Indonesia to predict future rice status in Malang Districts, Indonesia. The primary LULC change driver was the rapid conversion of rice fields, which had their area reduced by 18% from 2019 to 2021 and 2% from 2021 to 2025. Rice fields are mainly being converted to settlements and buildings. The Kappa coefficient of simulation achieved 88%, with 91 accuracies. The model predicted a 2% lower rate of rice production but a 3% higher demand in 2025 compared to 2021. Lower rice production and higher demand are predicted to reduce the rice surplus by 57% in 2025, suggesting that the Malang district might lower its supply of rice to other areas by 2025. Our study provides a food crisis early warning system that decision makers can use to form adequate strategic plans and solutions to combat food insecurity.

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“…Some studies in the literature in Brazil and in the world prove the importance and applicability of the study of soil dynamics and occupation. One can cite research in modeling the dynamics of land use and land cover change for the Amazon [11]; the assessment of land use and land cover change in Pakistan [18]; land use changes in the city of Lagos [19]; and characteristics of land use change in a temperate rainforest in Mexico [20]. Thus, one can see the potential and importance of modeling for land use and land cover studies, as it is a widely used technique.…”

Section: Introductionmentioning

confidence: 99%

Modeling Dynamics in Land Use and Land Cover and Its Future Projection for the Amazon Biome

Souza

Santos

Pezzopane

et al. 2023

Forests

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The objectives were to analyze the dynamics of land use and land cover of the Amazon biome over time through spatial modeling, and project its future scenario with the Land Change Modeler (LCM) module. This analysis was based on 1985, 2014 and 2017 land cover data from the MapBiomas project, which was associated with socioeconomic explanatory variables based on the Cramer-V test. Results showed that the Forest Formation class occupied 3,844,800.75 km2 (91.20%) in 1985, and in 2014, there was a reduction to 3,452,129.25 km2 (81.89%). The pasture class had an initial area of 71,046.50 km2 (1.69%), and in 2014, there was an expressive increase to 437,670.00 km2 (10.38%). The analysis made it possible to verify that Forest Formation and Pastures were the classes that suffered the most changes, followed by the Annual and Perennial Culture and Mosaic of Agriculture and Pasture. The projected land use and coverage for 2044 suggests that there will be a reduction in Forest Formation due to a significant increase in the Pasture class. The simulations foreseen in this work are an important tool that can provide subsidies for supporting territorial planning in the region, public policies, and encouragement of best practices with a reduced impact in pasture areas.

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Characterizing the impact of Land-Use/Land-Cover changes on a Temperate Forest using the Markov model

Cited by 8 publications

References 24 publications

Spatiotemporal Dynamics of the Human Critical Area (HCA) in the “Three Water Lines” Region of Northwest China and the Impact of Socioeconomic Factors between 2000 and 2020

Spatiotemporal Dynamics of the Human Critical Area (HCA) in the “Three Water Lines” Region of Northwest China and the Impact of Socioeconomic Factors between 2000 and 2020

Forecasting Rice Status for a Food Crisis Early Warning System Based on Satellite Imagery and Cellular Automata in Malang, Indonesia

Modeling Dynamics in Land Use and Land Cover and Its Future Projection for the Amazon Biome

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