Revista de Teledetección

Spatiotemporal variation of glacier dynamics and its relationship with changes in high mountain ecosystems in the Cordillera Blanca, Peru

2024-11-02T17:13:21+01:00

In high mountain ecosystems, glaciers, lakes and wetlands play an important role in local water resources, where a variation in glacier dynamics can affect the rest of the ecosystem components due to their hydrological connections. The objective of the study was to analyze the relationship of glacier dynamics with lakes and wetlands in Llullan, Quillcay and Yanayacu catchments of Cordillera Blanca (Peru) in the period from 1989 to 2019. Using Geographic Information Systems (GIS) and remote sensing, a multitemporal analysis was carried out using Landsat satellite images, and subsequently linear correlation was calculated and a Principal Component Analysis (PCA) was applied. The results of glacier dynamics parameters indicated a loss of between 22 and 56% of glacier area; from 24 and 63% of glacier volume; glacier front retreat varied between 10 and 18 m yr-1; and Equilibrium Line Altitude (ELA) rose from 137 and 227 m. The lake dynamics evidenced growth between 5 and 82% of their area and between 2 and 151% of their volume; and permanent wetlands showed a tendency to increase over time (from 44 and 289%). PCA indicated that PC1 and PC2 explained between 71 and 91% of the total variance of the original 7 variables, and glacier dynamics were found to have a good-very good inverse correlation with lake dynamics (between 0.62 and 0.99) and good with wetland dynamics (from 0.64 and 0.69). The study reflected the interconnectivity of high mountain ecosystem elements, where an acceleration of glacier retreat would trigger lakes and wetlands to reach their maximum capacity in shorter periods and begin to retreat earlier than expected.

Band selection for hyperspectral image visualization and classification

2024-09-12T13:38:46+02:00

In the hyperspectral remote sensing images processing, band selection is an essential task for many specific applications, including supervised classification. The objective of this work is to compare the performance of the classical strategy, which involves variable selection as a preliminary step to classification, with new proposals of penalized algorithms that perform classification and variable selection simultaneously. For the comparison, an extract of a hyperspectral image EO-1 Hyperion, covering an area in the province of Córdoba, Argentina, was used. Additionally, a simulation study was conducted. The obtained results show that penalized algorithms are more effective in selecting relevant bands while providing good predictive properties, mainly in the context of high dimensionality, that is, when the size of the training sample is small relative to the number of variables.

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

2025-02-11T19:38:44+01:00

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

2025-03-07T10:06:48+01:00

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.

3D evolution of l’Auir Beach foredune (2008-2022)

2025-03-31T10:04:32+02:00

Dunes and beaches are highly dynamic and interrelated spaces of great environmental and social interest. Their study at large spatial and temporal scales is limited by the difficulty of obtaining accurate altimetric data efficiently. Their three-dimensional characterisation using remote sensing techniques is of great interest for quantifying sedimentary changes and analysing the beach-dune system. LiDAR flights together with the recent development of photogrammetric methods for the reuse of aerial photographs make it possible to obtain highly accurate historical series of three-dimensional data of the coastline. The evolution of the dune front of l'Ahuir beach in Gandia (Valencia) has been characterised from 9 digital surface models between 2008 and 2022 based on volumetric changes and morphometry of cross-shore profiles. The results show a phase of significant sedimentary gains during 2009-2015 (more than 12000 m³), which appears to be associated with a period of relatively low intensity in coastal storms. Subsequently, and despite the alternation of slight gains and losses of sediment on the emerged beach between the annual SDMs, the dune front is in a phase of stability and slight gains, presenting a net gain of about 26000 m³ during 2008-2022. In contrast to other dune ridges along the coast, it is worth noting that the dune front is in a phase of stability and slight gains, and it has not been eroded associated with Storm Gloria. The analysis provides morphological information of great interest for characterising the sedimentary state of l’Ahuir Beach and monitoring its changes, demonstrating its potential to provide highly accurate data with large spatial and temporal coverage.

Landsat Collection 2: Key Information and recommendations for data users and product developers

2025-03-30T18:47:32+02:00

The United States Geological Survey (USGS) effort to provide coherent data for the Landsat series from various perspectives (e.g., geometric, radiometric, or metadata) is, without a doubt, admirable, especially considering the vast volume of data and the continuous scientific and technical challenges over many decades. Landsat Collection 2, initiated in 2020, represents the latest effort in this direction. This paper presents a detailed explanation of some important changes compared to previous distributions. The text highlights aspects of good practices (e.g., the choice of distribution format or the explicit coding of saturated pixels), radiometric inconsistencies (e.g., in areas of scene overlap or images taken on close dates), and decisions that pose difficulties for the user community (e.g., termination of the distribution of lower processing level products, exclusion of level 2 products for the first three Landsat satellites, inclusion of metadata that can lead to confusion, inconsistency of NoData values). It also addresses the significant differences in radiances between data processed by ESA (CEOS) and provides justification for the decision to change the meaning of the traditional DN, resulting in shifts in radiance rescaling factors (scale and offset) throughout the year. Furthermore, the paper offers alternatives for some problematic aspects of thermal infrared data processing. The aim is to assist other users and contribute to the debate on best practices in remote sensing image processing.

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

2025-02-16T18:57:56+01:00

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.

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

2025-02-18T17:46:08+01:00

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

2025-03-07T21:38:53+01:00

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).