The impact of land use and land cover changes on the landscape pattern and ecosystem service value in Sanjiangyuan region of the Qinghai-Tibet Plateau

Duan, Xinyi; Chen, Yan; Wang, Lingqing; Zheng, Guodi; Liang, Tao

doi:10.1016/j.jenvman.2022.116539

Cited by 59 publications

(25 citation statements)

References 103 publications

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“…3 ). Moreover, the declining trend in ESV over the landscape was consistent with changes in land cover 54 , 55 . This implies that the declining ESV estimates during the period between 1973 and 2001 and an increment in 2024 are attributed to degradation and restoration in area coverage of plantation forest, grassland, and natural forest in the landscape, respectively 5 , 14 , 17 witnessing a success to the recently introduced environmental protection policy.…”

Section: Resultssupporting

confidence: 60%

Changes in total and per-capital ecosystem service value in response to land-use land-cover dynamics in north-central Ethiopia

Birhane,

Negash,

Getachew

et al. 2024

Sci Rep

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Ecosystems provide a wide range of services crucial for human well-being and decision-making processes at various levels. This study analyzed the major land cover types of north-central Ethiopia and their impact on total and per-capita ecosystem service value (ESV). The ESV was estimated using the benefit-transfer method along the established global and local coefficient values for the periods 1973, 1986, 2001, 2016, and 2024. The findings show that agricultural lands continued to expand at a rate of 563.4 ha year−1, at the expense of forests and grasslands. As a result, the total ESV of the study area declined from $101.4 to $61.03 million and $60.08–$43.69 million, respectively. The ESV per capita was also diminished by $152.4 (37.7%) and $257 (40.6%), respectively. However, land-cover improvement during the period 2001–2016 enhanced the total and per capita ESV in the study area. Therefore, potential future research may be required to develop a valid approach for assessing the robustness and sensitivity of value coefficients for the valuation of the ESV at the landscape level.

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Section: Resultssupporting

confidence: 60%

Changes in total and per-capital ecosystem service value in response to land-use land-cover dynamics in north-central Ethiopia

Birhane,

Negash,

Getachew

et al. 2024

Sci Rep

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“…According to previous studies, changes in landscape patterns affect various ecological processes and functions, ultimately leading to changes in ecosystem services [58]. Based on existing studies and combined with the actual situation of the study area [8,9,58,59], seven landscape pattern metrics were selected for analysis, i.e., the proportion of cropland (cropland%), proportion of forest (forest%), proportion of construction land (construction land%), patch density (PD), cohesion (COHE), landscape shape index (LSI), and Shannon's diversity index (SHDI), respectively. Among them, cropland%, forest%, and construction land% were landscape composition indicators, which represented the dominant type of regional land use.…”

Section: Selection and Calculation Of Landscape Pattern Metricsmentioning

confidence: 99%

Multiscale Analysis of the Effects of Landscape Pattern on the Trade-Offs and Synergies of Ecosystem Services in Southern Zhejiang Province, China

Ding

Yan

Zhu

et al. 2023

Land

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Identifying the trade-offs and synergies (TOSs) of ecosystem services (ESs) and their responses to landscape patterns at various scales, especially in mountainous areas, could benefit the strategies of ES management and landscape optimization. In this study, the southern Zhejiang Province, a hilly region in eastern China, was chosen as the study area. Five ESs, including food production (FP), carbon sequestration (CS), flood mitigation (FM), water conservation (WC), and soil retention (SR) in 2020 were quantified. The TOSs of these ESs were identified at four spatial scales (i.e., grid, watershed, town, and county scales) through Pearson correlation analysis and the spatial overlay method. The effects of landscape patterns on the TOSs of ESs were analyzed by applying a logistic regression model. Results showed that FP and other ESs were trade-offs, while the other ES pairs were synergies. Spatial overlay results showed that weak synergies increased significantly, while strong synergies decreased significantly with the increase of the scale. The direction of the influence of landscape pattern on TOSs did not change, but the magnitudes of the impacts were scale-dependent. Landscape composition (i.e., cropland%, forest%, construction land%) had more significant effects on the trade-offs of ESs than spatial configuration (i.e., LSI, PD, COHE, and SHDI). The magnitudes of impact of landscape composition were strengthened at larger scales, while the effects of landscape configuration on the TOSs of ESs became complex as the scale changed. The results of this study could contribute to understanding how landscape patterns affect TOSs across scales, which will promote the hierarchical governance of ESs in mountainous areas.

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“…Por otro lado, sorprende la baja importancia asignada a la variable introducida de la transición de las coberturas (Figura 3), ya que teóricamente esta es una de las variables que más incidencia tienen en el fenómeno de la erosión del suelo (Duan et al, 2023;Wang et al, 2022). Asimismo, la temperatura que se esperaba tuviese una menor relación con la explicación del fenómeno, presentó una importancia incluso mayor que las variables empleadas en el método del IVR.…”

Section: Análisis De Características Con Random Forestunclassified

Predicción de la erosión del suelo mediante random forest: caso de estudio cuenca río grande, Antioquia

Arango Carvajal

2023

rev.investig.agrar.ambient.

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Contextualización: actualmente, el conocimiento de fenómenos naturales asociados a la preservación de los sistemas es de interés tanto para investigadores de las ciencias naturales, como para las autoridades ambientales encargadas de la toma de decisiones sobre el manejo de los recursos. En ese sentido, se ha venido trabajando en la interpretación y predicción de diferentes fenómenos físicos como la erosión, a fin de crear escenarios que permitan fortalecer los criterios de respuesta frente a la conservación del capital natural del suelo. Vacío de conocimiento: la capacidad de predecir el fenómeno de la erosión es limitada en muchas ocasiones, debido a la cantidad y variabilidad de los parámetros y variables que son relacionados a la erosión. Además, en muchos casos se requiere de un alto procesamiento computacional para lograr que se asocien entre sí. Propósito: se busca implementar un modelo de machine learning como herramienta alternativa para la modelación y predicción de la erosión. Metodología: en este estudio, se desarrolla una modelación a partir del entrenamiento del método no paramétrico Random Forest, mediante el aprendizaje supervisado, para predecir la ocurrencia de la erosión en la cuenca de Río Grande, considerando las variables que previamente han sido empleadas en otros métodos para modelar este fenómeno. Resultados y conclusiones: los resultados mostraron una capacidad para predecir la erosión en la cuenca con una precisión aproximada del 77%, por lo que este método puede ser aplicado para obtener predicciones rápidas y confiables. Además, se encontró que las variables empleadas en el modelo RUSLE explican mayoritariamente la ocurrencia, o no, de la erosión. Finalmente, se resalta la importancia de la variable “temperatura” introducida en el modelo.

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The impact of land use and land cover changes on the landscape pattern and ecosystem service value in Sanjiangyuan region of the Qinghai-Tibet Plateau

Cited by 59 publications

References 103 publications

Changes in total and per-capital ecosystem service value in response to land-use land-cover dynamics in north-central Ethiopia

Changes in total and per-capital ecosystem service value in response to land-use land-cover dynamics in north-central Ethiopia

Multiscale Analysis of the Effects of Landscape Pattern on the Trade-Offs and Synergies of Ecosystem Services in Southern Zhejiang Province, China

Predicción de la erosión del suelo mediante random forest: caso de estudio cuenca río grande, Antioquia

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