Aridity Analysis Using a Prospective Geospatial Simulation Model in This Mid-Century for the Northwest Region of Mexico

Aridity is a condition in which there is a moisture deficit in the air and soil that affects large areas of the earth’s surface worldwide. It is a global problem caused mainly by factors related to climatic events and human actions. In the arid regions of Mexico, prolonged periods of drought are very common and water scarcity is the predominant feature. The main objective of this study is to develop a prospective geospatial simulation model for arid zones in the short and medium term (2030 and 2050) for the northwestern region of Mexico. A retrospective analysis of the variables that cause aridity was conducted based on historical data from satellite information obtained from various sources between 1985 and 2020, taking 2020 as the reference year; from this information the rate of change per year was obtained, followed by the simulated rates of change for the years 2030 and 2050. A methodology used to obtain arid zones using multicriteria evaluation techniques, weighted linear combination, and Geographic Information Systems. In order to generate the prospective model for arid zones, the variables were modeled to adjust the rate of change for each of them, with the same methodology subsequently applied to obtain the base year (2020), and aridity suitability maps were obtained for the years 2030 and 2050. The main results indicate that the prospective scenarios point to an increase in arid regions of 0.38% and 0.70%, respectively, which is equivalent to an area of approximately 240,164.63 km2 and 241,760.75 km2, respectively. This will cause a decrease in the subhumid–dry and humid regions of 0.10% and 0.19%, respectively, for the projected years. Statistical and geospatial aridity indicators were also generated at different levels, which helps to better understand the problem of aridity in vulnerable regions

Geospatial Simulation Model of Deforestation and Reforestation Using Multicriteria Evaluation

Deforestation is an anthropic phenomenon that negatively affects the environment and therefore the climate, the carbon cycle, biodiversity and the sustainability of agriculture and drinking water sources. Deforestation is counteracted by reforestation processes, which is caused by the natural regeneration of forests or by the establishment of plantations. The present research is focused on generating a simulation model to predict the deforestation and reforestation for 2030 and 2050 using geospatial analysis techniques and multicriteria evaluation. The case study is the North Pacific Basin, which is one of the areas with the greatest loss of forest cover in Mexico. The results of the spatial analysis of forest dynamics determined that the forest area in 2030 would be 98,713.52 km2, while in 2050 would be 101,239.8 km2. The mean annual deforestation and reforestation expected in the study area is 115 and 193.84 km2, for the 2014–2030 period, while mean annual deforestation and reforestation values of 95 and 221.31 km2 are expected for the 2030–2050 period. Therefore, considering the forest cover predicted by the deforestation and reforestation model, a carbon capture of 16,209.67 ton/C was estimated for the 2014–2030 period and 587,596.01 ton/C for the 2030–2050

Proximate and Underlying Deforestation Causes in a Tropical Basin through Specialized Consultation and Spatial Logistic Regression Modeling

The present study focuses on identifying and describing the possible proximate and underlying causes of deforestation and its factors using the combination of two techniques: (1) specialized consultation and (2) spatial logistic regression modeling. These techniques were implemented to characterize the deforestation process qualitatively and quantitatively, and then to graphically represent the deforestation process from a temporal and spatial point of view. The study area is the North Pacific Basin, Mexico, from 2002 to 2014. The map difference technique was used to obtain deforestation using the land-use and vegetation maps. A survey was carried out to identify the possible proximate and underlying causes of deforestation, with the aid of 44 specialized government officials, researchers, and people who live in the surrounding deforested areas. The results indicated total deforestation of 3938.77 km2 in the study area. The most important proximate deforestation causes were agricultural expansion (53.42%), infrastructure extension (20.21%), and wood extraction (16.17%), and the most important underlying causes were demographic factors (34.85%), economics factors (29.26%), and policy and institutional factors (22.59%). Based on the spatial logistic regression model, the factors with the highest statistical significance were forestry productivity, the slope, the altitude, the distance from population centers with fewer than 2500 inhabitants, the distance from farming areas, and the distance from natural protected areas

Flood-Prone Area Delineation in Urban Subbasins Based on Stream Ordering: Culiacan Urban Basin as a Study Case

Urban development decreases infiltration, increases the runoff velocity, and reduces the concentration times. This situation increases the flood risk in urban watersheds, which represent a management challenge for urban communities and authorities. To increase the resilience of communities due to modifications of the hydrological cycle produced by climate change and urban development, a methodology is proposed to delineate flood-prone areas in urban basins. This methodology is implemented in an urban subbasin of Culiacan, Mexico, and is based on stream order. A high-resolution digital elevation model was used, which was validated independently through a photogrammetric flight with an unmanned aerial vehicle and ground control points obtained with GNSS (global navigation satellite systems) receivers. Morphometric parameters related to geometry, shape, relief, and drainage network aspects of the subbasin were determined and analyzed. Then, flood-prone area zonation was carried out based on stream-order classification and flow direction. Fieldwork was also carried out for the inspection of the sewage network conditions. This methodology simplifies the identification of the flood-prone areas in urban subbasins without carrying out complex hydraulic calculations

Effect of photogrammetric RPAS flight parameters on plani-altimetric accuracy of DTM

Remotely piloted aerial systems (RPASs) are gaining fast and wide application around the world due to its relative low-cost advantage in the acquisition of high-resolution imagery. However, standardized protocols for the construction of cartographic products are needed. The aim of this paper is to optimize the generation of digital terrain models (DTMs) by using different RPAS flight parameters. An orthogonal design L18 was used to measure the effect of photogrammetric flight parameters on the DTM generated. The image data were acquired using a DJI Phantom 4 Pro drone and six flight parameters were evaluated: flight mode, altitude, flight speed, camera tilt, longitudinal overlap and transversal overlap. Fifty-one ground control points were established using a global positioning system. Multivision algorithms were used to obtain ultra-high resolution point clouds, orthophotos and 3D models from the photos acquired. Root mean square error was used to measure the geometric accuracy of DTMs generated. The effect of photogrammetric flight parameters was carried out by using analysis of variance statistical analysis. Altimetric and planimetric accuracies of 0.38 and 0.11 m were achieved, respectively. Based on these results, high-precision cartographic material was generated using low-cost technology

Proximate and Underlying Deforestation Causes in a Tropical Basin through Specialized Consultation and Spatial Logistic Regression Modeling

The present study focuses on identifying and describing the possible proximate and underlying causes of deforestation and its factors using the combination of two techniques: (1) specialized consultation and (2) spatial logistic regression modeling. These techniques were implemented to characterize the deforestation process qualitatively and quantitatively, and then to graphically represent the deforestation process from a temporal and spatial point of view. The study area is the North Pacific Basin, Mexico, from 2002 to 2014. The map difference technique was used to obtain deforestation using the land-use and vegetation maps. A survey was carried out to identify the possible proximate and underlying causes of deforestation, with the aid of 44 specialized government officials, researchers, and people who live in the surrounding deforested areas. The results indicated total deforestation of 3938.77 km2 in the study area. The most important proximate deforestation causes were agricultural expansion (53.42%), infrastructure extension (20.21%), and wood extraction (16.17%), and the most important underlying causes were demographic factors (34.85%), economics factors (29.26%), and policy and institutional factors (22.59%). Based on the spatial logistic regression model, the factors with the highest statistical significance were forestry productivity, the slope, the altitude, the distance from population centers with fewer than 2500 inhabitants, the distance from farming areas, and the distance from natural protected areas