Improving Identification of Areas for Ecological Restoration for Conservation by Integrating USLE and MCDA in a GIS-Environment: A Pilot Study in a Priority Region Northern Mexico

Aguirre-Salado, Carlos Arturo; Miranda-Aragón, Liliana; Pompa-García, Marín; Hernández, Humberto Reyes; Soubervielle‐Montalvo, Carlos; Flores-Cano, Jorge A.; Méndez-Cortés, Heriberto

doi:10.3390/ijgi6090262

Cited by 26 publications

(18 citation statements)

References 58 publications

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“…Reyes-Cárdenas et al (2017) correlacionaron información de campo con datos espectrales y obtuvieron valores semejantes. Sin embargo, es importante considerar que dentro del área de estudio existen zonas con diferentes niveles de degradación del suelo, por lo que este factor debe integrarse dentro de las evaluaciones de biomasa a través de procedimientos como el análisis multicriterio (Aguirre-Salado et al, 2017). La variabilidad en la cantidad de biomasa también suele corresponder a las condiciones del terreno (topografía, altitud, pendiente, precipitación y temperatura), como lo señalan Olthoff, Martínez-Ruiz y Alday (2016).…”

Section: Discussionunclassified

Modelización de biomasa forestal aérea mediante técnicas deterministas y estocásticas

Reyes-Cárdenas

Garza

Jiménez-Pérez

et al. 2019

MYB

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Mediante la estimación de la biomasa forestal es posible determinar la cantidad de recursos forestales existentes en un determinado territorio, sin embargo, este es un proceso costoso y que consume gran cantidad de tiempo. Por ello, el objetivo del presente estudio fue modelar la biomasa arbórea aérea de un ecosistema forestal ubicado en la región centro sur del estado de San Luis Potosí. Para definir este proceso se compararon métodos determinísticos (distancia inversa ponderada) y estocásticos (kriging y cokriging), con los que se determinó la biomasa forestal con base en datos de campo y espectrales. Los datos de campo correspondieron a 50 conglomerados del Inventario Nacional Forestal y de Suelos, a partir de los cuales se calculó la biomasa mediante el empleo de ecuaciones alométricas. Los datos espectrales (NDVI tradicional -bandas roja e infrarroja del espectro electromagnético) se derivaron de una imagen Landsat 5TM del año 2009. Con los resultados del cálculo de la biomasa y del NDVI, se probaron semivariogramas y variogramas cruzados con los modelos esférico, exponencial y gausiano para analizar el que resultara en el mejor ajuste. Posteriormente, se seleccionó el modelo exponencial derivado de la técnica cokriging, con base en el cual se obtuvo un valor de la raíz cuadrada del error cuadrático medio de 32.01 Mg ha-1. Finalmente, con base en el modelo seleccionado, se generó un mapa de la distribución de la biomasa aérea, en el cual se presentan resultados que van de 0.85 Mg ha-1 a 157 Mg ha-1.

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Section: Discussionunclassified

Modelización de biomasa forestal aérea mediante técnicas deterministas y estocásticas

Reyes-Cárdenas

Garza

Jiménez-Pérez

et al. 2019

MYB

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“…Alternatively, statistical approaches can be used [24,34,[44][45][46]. Other approaches include empirical models (mainly Universal Soil Loss Equation-USLE, or Revised Universal Soil Loss Equation-RUSLE) [14,18,20,22,23,47], or physically-based models. either spatially distributed or spatially lumped (e.g., Soil Water Assessment Tool-SWAT [12,[48][49][50] and Water Availability in Semi-Arid environments-SEDiments-WASA-SED [51]).…”

Section: Mapping Approaches Under Constraints Of Data Unavailabilitymentioning

confidence: 99%

Mesoscale Mapping of Sediment Source Hotspots for Dam Sediment Management in Data-Sparse Semi-Arid Catchments

Smetanová

Müller

Zargar

et al. 2020

Water

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Land degradation and water availability in semi-arid regions are interdependent challenges for management that are influenced by climatic and anthropogenic changes. Erosion and high sediment loads in rivers cause reservoir siltation and decrease storage capacity, which pose risk on water security for citizens, agriculture, and industry. In regions where resources for management are limited, identifying spatial-temporal variability of sediment sources is crucial to decrease siltation. Despite widespread availability of rigorous methods, approaches simplifying spatial and temporal variability of erosion are often inappropriately applied to very data sparse semi-arid regions. In this work, we review existing approaches for mapping erosional hotspots, and provide an example of spatial-temporal mapping approach in two case study regions. The barriers limiting data availability and their effects on erosion mapping methods, their validation, and resulting prioritization of leverage management areas are discussed.

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“…Land cover is considered to be one of the most important factors affecting soil erosion and the investigation of soil losses due to differences or changes in land cover types is a popular research topic [19][20][21][22][23][24][25][26]. erefore, field experiments are an ideal method for observing the differences in hydrological characteristics (e.g., runoff) between one experimental area treated with vegetation cover and other experimental areas to assess the effect of vegetation on soil erosion [22,26,27].…”

Section: Introductionmentioning

confidence: 99%

Estimating the Impact of Land Cover Change on Soil Erosion Using Remote Sensing and GIS Data by USLE Model and Scenario Design

Cai

Sun

et al. 2021

Scientific Programming

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Great efforts have been made to curb soil erosion and restore the natural environment to Inner Mongolia in China. The purpose of this study is to evaluate the impact of returning farmland to the forest on soil erosion on a regional scale. Considering that rainfall erosivity also has an important impact on soil erosion, the effect of land use and land cover change (LUCC) on soil erosion was evaluated through scenario construction. Firstly, the universal soil loss equation (USLE) model was used to evaluate the actual soil erosion (2001 and 2010). Secondly, two scenarios (scenario 1 and scenario 2) were constructed by assuming that the land cover and rainfall-runoff erosivity are fixed, respectively, and soil erosion under different scenarios was estimated. Finally, the effect of LUCC on soil erosion was evaluated by comparing the soil erosion under actual situations with the hypothetical scenarios. The results show that both land use/cover change and rainfall-runoff erosivity change have significant effects on soil erosion. The land use and land cover change initiated by the ecological restoration projects have obviously reduced the soil erosion in this area. The results also reveal that the method proposed in this paper is helpful to clarify the influencing factors of soil erosion.

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Improving Identification of Areas for Ecological Restoration for Conservation by Integrating USLE and MCDA in a GIS-Environment: A Pilot Study in a Priority Region Northern Mexico

Cited by 26 publications

References 58 publications

Modelización de biomasa forestal aérea mediante técnicas deterministas y estocásticas

Modelización de biomasa forestal aérea mediante técnicas deterministas y estocásticas

Mesoscale Mapping of Sediment Source Hotspots for Dam Sediment Management in Data-Sparse Semi-Arid Catchments

Estimating the Impact of Land Cover Change on Soil Erosion Using Remote Sensing and GIS Data by USLE Model and Scenario Design

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