Agricultural activities are highly related to the reduction of the availability of water resources due to the consumption of freshwater for crop irrigation, the use of fertilizers and pesticides. In this study, the water quality of the Adolfo López Mateos (ALM) reservoir was evaluated. This is one of the most important reservoirs in Mexico since the water stored is used mainly for crop irrigation in the most productive agricultural region. A comprehensive evaluation of water quality was carried out by analyzing the behavior of 23 parameters at four sampling points in the period of 2012-2019. The analysis of the spatial behavior of the water quality parameters was studied by spatial distribution graphs using the Inverse Distance Weighting interpolation. Pearson correlation was performed to better describe the behavior of all water quality parameters. This analysis revealed that many of these parameters were significantly correlated. The Principal Components Analysis (PCA) was carried out and showed the importance of water quality parameters. Ten principal components were obtained, which explained almost 90% of the total variation of the data. Additionally, the comprehensive pollution index showed a slight water quality variation in the ALM reservoir. This study also demonstrated that the main source of contamination in this reservoir occurs near sampling point one. Finally, the results obtained indicated that a contamination risk in the waterbody and further severe ecosystem degradations may occur if appropriate management is not taken.

ResumenEl objetivo de esta investigación fue estudiar la deforestación y sus causas en el estado de Sinaloa, México. Para ello, se utilizó la cartografía de Uso de Suelo y Vegetación del año 1993 y 2011 a escala 1:250 000, con esta se estimó la deforestación mediante una técnica de detección de cambios; posteriormente, se caracterizó la deforestación mediante la consulta a expertos. Por último, se aplicó la matriz de cambios para analizar las pérdidas, ganancias y transiciones y corroborar cartográficamente lo obtenido por los expertos y la detección de cambios. Los resultados indican una deforestación de 126.50 km 2 /año y una tasa media anual de 0.41%. De la consulta a expertos se determinó que las principales causas de estos procesos son la expansión agrícola y la extensión de infraestructura con un impacto de 49.40% y 18.8%, respectivamente. En cuanto a la matriz de cambios, se determinó que especialmente la categoría

The last ten years have shown that Climate Change (CC) is a major global issue to attend to. The integration of its effects into coastal impact assessments and adaptation plans has gained great attention and interest, focused on avoiding or minimizing human lives and asset losses. Future scenarios of mean sea level rises and wave energy increase rates have then been computed, but downscaling still remains necessary to assess the possible local effects in small areas. In this context, the effects of CC on the wave climate in the Gulf of California (GC), Mexico, have received little attention, and no previous studies have tackled the long-term trend of wave climate at a regional scale. In this paper, the long-term trends of the wave height, wave period and wave energy in the GC were thus investigated, using the fifth-generation climate reanalysis dataset (ERA5). The long-term shoreline evolution was also examined from historical Landsat images, so as to identify erosional hotspots where intervention can be prioritized. The results indicate that both the mean and extreme wave regimes in the GC are getting more energetic and that two-thirds of the coast is suffering chronic erosion. A discrepancy between the trends of the wave period and wave height in some regions of the Gulf was also found. Finally, the importance of natural processes, human activity and CC in the shoreline change is highlighted, while addressing the need for future permanent field observations and studies in the GC.

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.

The new generation, low-cost U-blox ZED-F9P receiver was evaluated and statistically compared by GNSS observations on a geodesic monument, through both Precise Point Positioning and Static relative positioning techniques with a distance of 33 km from the references station. This was done with the purpose of checking the use feasibility of the low-cost receiver of similar gamma in topographic-geodesic works. To that end, four scenarios were considered: in the first scenario, the static relative positioning with the low-cost equipment was applied; in the second scenario, the static relative positioning with a geodetic receiver was applied. Both scenarios were processed with commercial software. The third and fourth scenarios were processed with Precise Point Positioning techniques through the RTKLIB software. The results show that Precise Point Positioning techniques get a precision of 1 cm through the use of low-cost equipment which is suitable to apply in geodetic works. In the static relative method, the precision obtained is 7 mm, indicating the possibility of using the low-cost equipment in both survey and geodetic high precision works, considering a line base ≤30 Km, according to the Instituto Nacional de Estadística y Geografía normative.

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.

En los últimos años, se ha deforestado el 20% de manglar a nivel mundial. México es de los países con mayor pérdida de superficie de mangle, algo que contribuye a las emisiones de CO2 e impulsa el cambio climático. Sin embargo, falta conocimiento sobre los factores que influyen en la pérdida y la ganancia del manglar, las emisiones de CO2, y la dinámica de usos de suelo y cobertura vegetal a escala local y regional. Por tanto, los objetivos de este estudio fueron analizar la dinámica de uso de suelo en la zona de Marismas Nacionales (México) durante el periodo 1981–2015, determinar la tasa de deforestación y degradación anual del manglar y estimar las emisiones de CO2 derivadas de estos procesos utilizando técnicas de información geográfica. Para determinar los cambios de uso de suelo, con la matriz de tabulación cruzada, se adquirieron diversos parámetros de cambio que permitieron generar una ecuación para estimar la tasa de deforestación y degradación. Con los datos del Inventario Nacional de Emisiones de Gases y Compuestos de Efecto Invernadero (México), se estimaron las emisiones y las absorciones de CO2 (equivalente, CO2e) promovidas por deforestación, degradación, reforestación y recuperación natural de manglares. Para el periodo 1981–2005, la emisión estimada fue de 432.50 Gg de CO2e debido a una tasa anual de deforestación del 0.77%, y la degradación fue de 27.16 Gg de CO2e a una tasa anual de1 7.64%. Para el periodo 2005–2015, la emisión fue de 145.21 Gg de CO2e debido a una tasa anual de deforestación del 0.44%, y la degradación fue de 24.80 Gg de CO2e a una tasa anual del 4.94%. La mayor pérdida de manglar se debió a la transformación a suelos con categorías de agrícola-pecuario y desarrollo antrópico. La degradación sucedió por fenómenos naturales y actividades antropogénicas.

Selective logging in the tropics is a major driver of forest degradation by altering forest structure and function, including significant losses of aboveground carbon. In this study, we used a 30-year Landsat time series (1985–2015) to analyze forest degradation and carbon emissions due to selective logging in a Forest Reserve of the Venezuelan Amazon. Our work was conducted in two phases: the first, by means of a direct method we detected the infrastructure related to logging at the sub-pixel level, and for the second, we used an indirect approach using buffer areas applied to the results of the selective logging mapping. Pre- and post-logging forest inventory data, combined with the mapping analysis were used to quantify the effects of logging on aboveground carbon emissions for three different sources: hauling, skidding and tree felling. With an overall precision of 0.943, we demonstrate the potential of this method to efficiently map selective logging and forest degradation with commission and omission errors of +7.6 ± 4.5 (Mean ± SD %) and −7.5% ± 9.1 respectively. Forest degradation due to logging directly affected close to 24,480 ha, or about ~1% of the total area of the Imataca Forest Reserve. On average, with a relatively low harvest intensity of 2.8 ± 1.2 trees ha−1 or 10.5 ± 4.6 m3 ha−1, selective logging was responsible for the emission of 61 ± 21.9 Mg C ha−1. Lack of reduced impact logging guidelines contributed to pervasive effects reflected in a mean reduction of ~35% of the aboveground carbon compared to unlogged stands. This research contributes to further improve our understanding of the relationships between selective logging and forest degradation in tropical managed forests and serves as input for the potential implementation of projects for reducing emissions from deforestation and forest degradation (REDD+).