Revista de Teledetección

Application of the METRIC model to estimate Maize crop evapotranspiration at field scale with Google Earth Engine

Victor Manuel Gordillo-Salinas — 2024-07-29

Determination of actual crop evapotranspiration (ETc) is a crucial challenge for sustainable irrigation water management. In this sense, robust and accurate estimation models of crop water consumption along with spatial tools and processing platforms in the cloud are necessary to determine the timing and amount of irrigation needed as a first step toward proposing solutions and water use efficiency. The objective of this study was to determine maize crop evapotranspiration using the algorithms of the Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) model in the Google Earth Engine (GEE) platform. The crop was monitored with 14 Landsat images during its growth period. ETc values with METRIC were compared with ETc obtained with the FAO-56 methodology, and the cumulative ETc was compared with ETc derived from a soil moisture sensor. The evaluation between the METRIC model and FAO-56 displayed a determination coefficient (R2) of 0.87, mean squared error (MSE) of 0.8 mm/day, and bias percentage (PBIAS) of -14.5. According to the cumulative ETc, the difference was 16 mm for METRIC and 63 mm for FAO-56, compared with moisture sensor values. METRIC overestimated by 3.0% (PBIAS=-3.0), and FAO-56 underestimated by 11.9% (PBIAS=11.9). The results and the programmed algorithms in this work can be the basis for future calibrations and validations of the evapotranspiration of different crops.

Supervised classification, multi-criteria assessment and location-allocation models of Cistus ladanifer essential oil distillation industries

Carlos Pérez-Izquierdo — 2024-07-29

Cistus ladanifer L. (rockrose) is a shrub species widespread in the Mediterranean region and highly valuable for the cosmetic, pharmacological and agri-food industries. Despite its value, this resource remains under-exploited and presents great spatial variability and heterogeneous extraction conditions. This study aims to develop a methodology to locate optimal areas for the installation of C. ladanifer essential oil distillation plants that will allow its extraction in an efficient and profitable way. Remote sensing techniques based on supervised classifications of pixels and objects have been applied to determine the distribution and surface of this resource. The classification was conducted using 2018 Sentinel-2 imagery, digital elevation models and the following six classification algorithms: minimum distance, Mahalanobis distance, maximum likelihood, Spectral Angle Mapper, support vector machines and neural networks. GIS tools such as multi-criteria evaluation analysis and location- allocation models allowed us to obtain and connect the supply points with the highest resource suitability and the ideal demand sites for the facilities. Maximum likelihood, support vector machines and neural networks classifiers achieved classification accuracies above 90 % in overall accuracy and Kappa coefficient. The total area of potentially exploitable rockrose obtained in the classification was 20 889 ha, from which 15 241 ha (72.96 %) were viable for harvesting. The installation of two distillation plants showed an efficient spatial coverage distribution to exploit this resource in the study area. The methodology is considered a valuable tool to efficiently and sustainably determine the optimal location of distillation plants.

Assessment of the Kinematics of the Cuenca landslide in the central Peruvian Andes using photogrammetry and geodetic techniques

Wendy Quiroz — 2024-07-29

Landslides represent a significant hazard in many mountainous regions, including the inter-Andean valleys of Peru. In this study, we evaluate the dynamics of the Cuenca landslide located in Huancavelica, central Peru, using photogrammetry and GNSS measurements. Interannual measurements were conducted at eight sites between 2016 and 2023 for GNSS, and two photogrammetric survey campaigns in 2023 to compare surface changes over time. The results show displacements ranging from 3.7 to 11.7 cm using the point cloud technique and from 2.7 to 15 cm through orthomosaic analysis, with both methods yielding consistent results. Displacements at points where GNSS measurements were taken are similar in magnitude but differ partially in direction. The study concludes that UAV techniques are applicable for analyzing landslide dynamics.

Mapping of the natural and anthropic environments of Entre Rios (Argentina) using machine learning classification

Julian Alberto Sabattini — 2024-07-29

Entre Ríos presents a distinctive landscape with numerous contrasting environments. Mapping both natural and anthropic features is a common task facilitated using remote sensing technologies alongside geographic information systems. Knowing what, how much, and where they are located is essential for designing sustainable use and conservation strategies for natural resources in a territory. The free accessibility of data and the cloud processing capability for all this information are crucial for processing and classifying the vegetation of a specific area. The aim was to create an updated map that can be easily updated in the future, using the same method for the most representative natural and anthropic environments in the province of Entre Ríos. This involves determining the best time of the year to maximize the accuracy percentage of automatic algorithm classification for each environment. Employing automatic classification learning algorithms was useful in understanding the extent of natural and anthropic ecosystems across a vast territory. Google Earth Engine tools allowed for selecting the optimal time of year to maximize accuracy percentage and minimize the probability of error with low computational and operational costs. The results obtained are indispensable for planning precise and accurate public policies for productive activities, as well as for the conservation of natural resources.

Does environmental public policy act as a slowdown for urban expansion? A 2012-2023 analysis with Landsat images

Jorge Alberto Escandón-Calderón — 2024-07-29

Urban expansion constitutes one of the main activities that modify natural environments worldwide; the most vulnerable areas to these effects are peri-urban areas. An example is 59% of the surface of Mexico City, considered a Conservation Zone (CZ). Within the CZ there are natural ecosystems, agricultural and livestock activities take place, which is why it is considered an area that provides a large number of services, including ecosystem services. This study aimed to analyze the rates of urban expansion in the CZ during two periods (2012-2018, 2018-2023) with contrasting environmental policies. Landsat 7 and 8 images were used to perform a supervised classification with Random Forest algorithm with which the surface of different land use classes was estimated for three years 2012, 2018, and 2023. The dynamics of urban expansion in two administrative periods were evaluated with different approaches and budget priorities in the environmental public policy of the CZ. Supervised classification had adequate accuracy (≥89%). The rate of urban expansion during the studied periods was 31.7% (269 ha/year) from 2012 to 2018 and 16.9% (190.3 ha/year) from 2018 to 2023. A clear deceleration of land use change for urban purposes was observed; three out of the nine municipalities comprising the SC exhibited an urban expansion rate close to 0%. Additionally, a “protective” effect was also exhibited towards the agricultural areas and primary forests of the CZ. The budget increase focused on environmental conservation activities and rural development of the CZ appears to have affected the dynamics of urban expansion in the peri-urban area of one of the most complex cities in the world.

Dynamics of environmental flooding in wetlands of the Lower Grijalva River Basin: spatiotemporal approach through Landsat images

Tania G. Núñez-Magaña — 2024-07-29

The diversity of existing methodologies to define and analyze the dynamics of water surfaces demonstrates the difficulty in investigating their behavior. This is compounded by variables that complicate their delineation, such as precipitation, evapotranspiration, and their reflective behavior. This study aimed to analyze the spatiotemporal dynamics of wetlands with high socio-environmental impact in the Lower Grijalva River Basin for the period from 1986 to 2018. For the analysis, a satellite database was integrated with 169 images from Landsat 5 and Landsat 8. Spectral indices (MNDWI and MBWI) were calculated, and thresholds characterizing water surfaces in the study area were identified. The results showed that the MBWI was superior in estimating water surfaces. Finally, maps of the spatiotemporal dynamics’ probabilities were generated for the wetlands of the greatest ecological and economic importance in the Lower Grijalva River Basin. These maps revealed the return periods of the expansion and longitudinal retreat processes in the wetlands and indicated that during La Niña periods, the formation of temporary wetlands could be associated with groundwater saturation rather than surface water contributions.