Federico Villarreal-Guerrero

Vegetation landscape analysis due to land use changes on arid lands

ABSTRACT Fast land use changes have strongly affected arid and semi-arid regions at a global scale, affecting food security of the inhabitants of these regions. This study evaluated the fragmentation degree in the Chihuahua´s desert region of Mexico by using data from the Landsat TM sensor. Nine scenes, taken with Landsat TM5 sensor from the years 1990, 2000, and 2012, were used for the analysis. The coverage of seven land uses (grasslands, shrubland, croplands, sandy desert vegetation, forest, water bodies, and urban areas) was obtained under supervised classification techniques and the accuracy level was evaluated through the Kappa multi-varied discrete index. The classification showed a good reliance level having global accuracies of 93, 93.2 and 90.3% for the years 1990, 2000 and 2012, respectively. The fragmentation analysis showed an increase in the number of patches, an indicator of the ecosystem degradation process. The patches number increased from 8,354.23 in 1990 to 9,658.36 in 2000 and to 11,469 in 2012. Simpson and Shannon diversity indexes proved a clear fragmentation process. During the period of 1990−2012, grasslands were the most affected vegetation type with a reduction of 30.7% in its area. Such reduction was mainly attributed to invasions of shrubland communities and to an increase in cropland areas.

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

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.

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

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 R2 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.