Revista de Teledetección

Spatial-temporal assessment of Uaymil Protected Area conservation status using an ecosystem quality index from 2000-2023

Mon, 12 May 2025 00:00:00 +0200

Protected areas (PAs) are crucial for conserving species and ecosystems but are still susceptible to deforestation and degradation from human and natural causes. The Uaymil Protected Area in Quintana Roo, Mexico, is a key ecological corridor facing deforestation risks due to its location. Due to this the objective of this study was to evaluate the conservation status and analyze the spatial temporal changes within vegetation type of the protected area of flora and fauna “Uaymil” using the Ecosystem Quality Index (EQI). MODIS Terra satellite data for Leaf Area Index (LAI), Gross Primary Productivity (GPP), and Fractional Vegetation Cover (FVC) were used to calculate the annual EQI over 23 years. The results showed a strong integration of LAI, GPP, and FVC into the EQI, improving the model's ability to capture ecosystem quality changes. Significant shifts occurred in 2005, 2011, 2015, and 2023, indicating both degradation and recovery. Lower EQI values were found in mangrove and marsh areas, while forests had higher ecological indicators. Overall, the Uaymil Protected Area maintains high vegetation cover and ecosystem quality, indicating a strong conservation status.

Comparing different models for fuel load estimation in rockrose shrubland in the Mediterranean region from LiDAR data

Thu, 03 Apr 2025 00:00:00 +0200

Shrub communities of Cistus ladanifer L. (gum rockrose) are one of the most characteristic, extensive, and prone to wildfire of Mediterranean ecosystems. In addition, these shrublands have a remarkable potential for the extraction of subproducts, which are highly valuable in the pharmaceutical, food and cosmetic industries. Therefore, estimating and their biomass is essential to manage and prioritize their use, calculate their carbon content and CO₂ capture as well as predict their fire behaviour and possible emissions. In this study, we aim to estimate the fuel load of gum rockrose shrublands in southern Spain based on airborne LIDAR data from PNOA. For this purpose, non-destructive field inventories were carried out with measurements of mean height and shrub cover in 143 circular plots in Andalusia region. These two fuel variables were used as inputs in an existing specific equation to estimate the fuel load for C. ladanifer.

Two different approaches were compared to estimate the fuel load of these gum rockrose by linear regression analysis: (i) direct estimation (DE), consisting of the adjustment that directly relates fuel load to ALS data; and (ii) indirect estimation in two steps (IE) based on the adjustment of equations to estimate the input variables (shrub height and cover) of the gum rockrose from LiDAR data. Better goodness-of-fit statistics were obtained in the direct estimation model than in the indirect estimation model, explaining 70% and 72% of the observed variability, respectively. These results can be valuable for the development of gum rockrose biomass mapping for use in fire prevention and suppression and in the planning of harvesting for the extraction of their products.

Fine-scale carbon stocks mapping in the mangrove forests of Tumaco, Colombia using machine learning and remote sensing approaches

Laura Lozano-Arias, Bryan Ernesto Gallego-Pérez, John Josephraj Selvaraj — Mon, 05 May 2025 00:00:00 +0200

Mangroves play a critical role in mitigating climate change, sequestering up to five times more carbon than other forests. Accurate assessment of their carbon stocks is crucial for effective planning and management in climate change strategies. This study presents an innovative approach that integrates remote sensing with field data, utilizing high-resolution imagery and evaluating two machine learning algorithms: Random Forest and Support Vector Regression. Mangrove area was mapped using supervised classification, and both aboveground and belowground biomass, along with the carbon stored in these compartments, were quantified. The classification achieved an accuracy of 87%, and mean values of 192.50±102.78 Mg/ha for aboveground biomass, 79.95±56.85 Mg/ha for belowground biomass, and 127.43±73.49 CMg/ha for stored carbon. The Random Forest model performed best, with an RMSE of 140.68 and an R² of 0.78, surpassing global models. Additionally, spectral indices significantly enhanced the model’s ability to predict aboveground biomass.

Baseflow measurement in mountain rivers using LSPIV: A case study of the Tarqui and Yanuncay rivers in the Ecuadorian Andes

Wed, 02 Apr 2025 00:00:00 +0200

This study is motivated by the difficulty of applying experimental techniques to characterize base flows in mountain rivers. Intrusive instruments are not optimal for measuring low flow rates, as they require a minimum depth to be submerged and to measure flow velocity. The LSPIV methodology was applied using an Autel Evo II RTK Series 3 UAV. The results were validated through measurements taken with a Redback current meter, showing that the flow rates and velocity fields obtained with the presented techniques are of the same order of approximation. The flow velocity fields resulting from the application of LSPIV enabled the identification of typical flow characteristics in mountain rivers with gravel and boulder beds: zones of acceleration and turbulent mixing, stagnation areas due to obstacles within the flow, flow recirculation, and shear regions caused by interaction with existing morphological structures. Thus, the LSPIV technique is presented as a valuable tool for characterizing extreme flows in mountain rivers using non-intrusive methods.

Model development for evaluating vineyard productivity and yield based on vegetation indices. Case study: Viña Arnaiz Winery

Pablo Morán, María Navalpotro, Francisco Cabrera-Torres, Cesar Cabrera — Fri, 11 Apr 2025 00:00:00 +0200

Spain is one of the largest wine producers in the world, therefore, viticulture is key to its economy. The Spanish wine industry has incorporated remote sensing techniques in the different stages of production, mostly aimed at vegetation mapping, pest detection and disease control, however, there are few studies related to the determination of production and yield in vineyards. For this reason, based on various vegetation spectral indices NDVI, NDRE, LAI, MSAVI2, TCARI, OSAVI, among others, and values of Leaf Area Index, LAI, different non-parametric models were generated, using principal component analysis and neural networks, which have been widely studied and implemented in various fields. The products obtained showed an estimation error RMSE of 16.19 t and 5.53 t/ha, in relation to productivity and yield respectively, from the analysis of principal components, and, 10.32 t and 4.23 t/ha, respectively, in the case of neural networks, showing an improvement when using this last technique. This study was carried out in the vineyards of Viña Arnaiz, located in the municipality of Haza (Burgos).