Vegetation Landscape Analysis Due to Land Use Changes on Arid Lands

Manjarrez-Domínguez, Carlos; Pinedo-Álvarez, Alfredo; Pinedo-Alvarez, Carmelo; Villarreal‐Guerrero, Federico; Cortés-Palacios, Leonor

doi:10.3161/15052249pje2015.63.2.001

Cited by 8 publications

(10 citation statements)

References 35 publications

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“…However, grasslands have experienced a notorious degradation in recent years due to long droughts, climate variability, fragmentation and anthropic intervention. The application of improper management practices has been one of the main causes of such degradation [3,4]. Specifically, grasslands in northern Mexico are extensively used for cattle grazing and have also experienced intensive land-use change due to agricultural activities, which has resulted in changes of the species composition on such ecosystems [4,5].…”

Section: Introductionmentioning

confidence: 99%

Atmospheric and Radiometric Correction Algorithms for the Multitemporal Assessment of Grasslands Productivity

et al. 2018

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A key step in the processing of satellite imagery is the radiometric correction of images to account for reflectance that water vapor, atmospheric dust, and other atmospheric elements add to the images, causing imprecisions in variables of interest estimated at the earth's surface level. That issue is important when performing spatiotemporal analyses to determine ecosystems' productivity. In this study, three correction methods were applied to satellite images for the period 2010-2014. These methods were Atmospheric Correction for Flat Terrain 2 (ATCOR2), Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), and Dark Object Substract 1 (DOS1). The images included 12 sub-scenes from the Landsat Thematic Mapper (TM) and the Operational Land Imager (OLI) sensors. The images corresponded to three Permanent Monitoring Sites (PMS) of grasslands, 'Teseachi', 'Eden', and 'El Sitio', located in the state of Chihuahua, Mexico. After the corrections were applied to the images, they were evaluated in terms of their precision for biomass estimation. For that, biomass production was measured during the study period at the three PMS to calibrate production models developed with simple and multiple linear regression (SLR and MLR) techniques. When the estimations were made with MLR, DOS1 obtained an R 2 of 0.97 (p < 0.05) for 2012 and values greater than 0.70 (p < 0.05) during 2013-2014. The rest of the algorithms did not show significant results and DOS1, which is the simplest algorithm, resulted in the best biomass estimator. Thus, in the multitemporal analysis of grassland based on spectral information, it is not necessary to apply complex correction procedures. The maps of biomass production, elaborated from images corrected with DOS1, can be used as a reference point for the assessment of the grassland condition, as well as to determine the grazing capacity and thus the potential animal production in such ecosystems.

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Section: Introductionmentioning

confidence: 99%

Atmospheric and Radiometric Correction Algorithms for the Multitemporal Assessment of Grasslands Productivity

et al. 2018

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“…This suggests that urban development will mainly occur on the plains and small slopes. Grasslands are one of the most threatened ecosystems worldwide [13]. This ecosystem possess a high degree of endemism in the region [38] and provide us with ecosystem services such as water harvest, carbon sequestration, soil retention, and contributions to weather stability, just to name a few [52].…”

Section: Discussionmentioning

confidence: 99%

“…Other effects of urbanization include alteration of the biogeochemical cycles [12] and the reduction of areas dedicated to agricultural crops, grasslands, forests and in general of the ecosystems located nearby. This has resulted in land fragmentation and degradation [13]. Therefore, the understanding of the growth dynamics of urban areas is of great importance to elaborate better and more environmentally friendly urban growth plans, and to take actions for the preservation of the natural resources [14].…”

Section: Introductionmentioning

confidence: 99%

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Past and Future Spatial Growth Dynamics of Chihuahua City, Mexico: Pressures for Land Use

Prieto‐Amparán

Pinedo-Álvarez

Villarreal‐Guerrero

et al. 2016

IJGI

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Abstract:In this study, the transitions of land use that occurred in the urban and peripheral areas of Chihuahua City, Mexico, were determined for the period 1989-2014. Landsat TM and OLI scenes, as well as the method of Markov Chains (MC) were used. Grasslands and Shrublands were the land uses that experienced the highest pressures for land use. Grasslands occupied 23.5% of the area in 1989, decreasing to 16.01% in 2014. Likewise, Shrublands were reduced from 54.53% to 48.06%. The areas occupied by Croplands, Oak forest, Water bodies and Riparian vegetation stayed in general constant. In contrast, the urban area increased from 13.6% to 28.6% of the total area studied. In addition, projections of land use for 2019 and 2024 were generated through the method of MC and Cellular Automata (CA). According to the projections, validated with an agreement of 0.90, the Human settlements would continue to expand, occupying 38.57% by 2019 and almost half of the studied territory (47.33%) by 2024. The ecosystems with the highest pressure for land use change will continue to be the Grasslands and Shrublands. By 2024, the former would lose 15.8% while the latter would lose 16.7% of the area. These methods are valuable for urban planning and the results could support growth plans for Chihuahua City, Mexico, with a sustainable approach.

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“…They also allow comparisons of different times and even help recreate future scenarios [58,59]. Consequently, landscape metrics have been widely used to study large natural areas, the evolution of grasslands, urban expansion, among others [15].…”

Section: Scenarios Comparison Through Landscape Indexesmentioning

confidence: 99%

GIS-Based Multicriteria Evaluation of Land Suitability for Grasslands Conservation in Chihuahua, Mexico

et al. 2019

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This study developed a GIS-based framework for the zoning of land suitability for grassland conservation (LSGC) in the Central Valleys of Chihuahua, México. For that, a Geographic Information Systems (GIS)-based multicriteria evaluation techniques with weighted overlay (MCE-WO), and a fragmentation analysis were performed. The framework for LSGC consisted in the development of four scenarios: Nonintensive Agriculture, Intensive Agriculture, Urban, and Rural. The LSGC classes defined with the MCE-WO technique were: Very high, High, Moderate, Low, and Very low land suitability. Results showed that the zone with a high suitability covered the largest area in the four scenarios with a surface of 44,264 km2. The zones with low and very low suitability were concentrated mainly in the central region of the study area. At the landscape level, fragmentation of LSGC showed the Nonintensive Agriculture and the Rural scenarios with the highest Number of patches (54,640 and 46,210, respectively). The fragmentation of LSGC, under the scenarios evaluated, was mainly due to land opening for agriculture and to the influence of rural communities. The integration of GIS with MCE-WO is useful and effective for the evaluation of LSGC. This tool can provide a solid source of information for decision-makers regarding planning of land use to mitigate grasslands degradation.

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Vegetation Landscape Analysis Due to Land Use Changes on Arid Lands

Cited by 8 publications

References 35 publications

Atmospheric and Radiometric Correction Algorithms for the Multitemporal Assessment of Grasslands Productivity

Atmospheric and Radiometric Correction Algorithms for the Multitemporal Assessment of Grasslands Productivity

Past and Future Spatial Growth Dynamics of Chihuahua City, Mexico: Pressures for Land Use

GIS-Based Multicriteria Evaluation of Land Suitability for Grasslands Conservation in Chihuahua, Mexico

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