Revista de Teledetección http://ojs.upv.es/index.php/raet <p class="default" style="text-align: justify; text-justify: inter-ideograph; margin: 0cm 0cm 6.0pt 0cm;"><em>Spanish Journal of Remote Sensing / Revista de Teledetección (RAET)</em> is a biannual scientific journal that publishes original research papers related to a wide range of methods and applications in remote sensing. The official publication languages are both, Spanish and English. The journal is open access and there are no charges for publication..</p> en-US <p><a href="https://creativecommons.org/licenses/by-nc-sa/4.0" target="_blank" rel="noopener"><img src="https://polipapers.upv.es/public/site/images/ojsadmin/CC_by_nc_sa.png" alt="" /></a></p> <p>This journal is licensed under a <a href="https://creativecommons.org/licenses/by-nc-sa/4.0/" target="_blank" rel="license noopener">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International</a></p> laruiz@cgf.upv.es (Luis Ángel Ruiz Fernández) ojsadmin@upvnet.upv.es (Administrador PoliPapers) Mon, 31 Jan 2022 12:40:25 +0100 OJS 3.3.0.8 http://blogs.law.harvard.edu/tech/rss 60 Annual trend, anomalies and prediction of vegetation cover behavior with Landsat and MOD13Q1 images, Apacheta micro-basin, Ayacucho Region http://ojs.upv.es/index.php/raet/article/view/15672 <p>Climate variability in the Apacheta micro-basin has an impact on vegetation behavior. The objective is to analyze the annual trend, anomalies and predict the behavior of vegetation cover (CV) with Landsat images and the MOD13Q1 product in the Apacheta micro-basin of the Ayacucho Region. For this purpose, the CV was classified and validated with the Kappa index (<em>p</em>-value=0,032; &lt;0.05), obtaining a good agreement between the values observed <em>in situ </em>and the estimated in the Landsat images. The CV data were subjected to the Lilliefors normality test (<em>p</em>-value=0,0014; &lt;0,05) indicating that they do not come from a normal distribution. CV forecasting was performed with the <em>auto.arima</em>, <em>forecast </em>and <em>prophet </em>packages, in R, according to the Box-Jenkins and ARIMA approaches, whose two-year future scenario is acceptable, but with higher bias. The results show an anual increasing CV trend of 3,378.96 ha with Landsat imagery and 3,451.95 ha with the MOD13Q1 product, by the end of 2020. The anomalies and the CV forecast also show a significant increase in the last 9 years, becoming higher in the forecasted years, 2021 and 2022.</p> Wilmer Moncada, Bram Willems, Alex Pereda, Cristhian Aldana, Jhony Gonzales Copyright (c) 2022 Wilmer Moncada, Alex Pereda, Bram Willems, Cristhian Aldana, Jhony Gonzáles http://ojs.upv.es/index.php/raet/article/view/15672 Mon, 31 Jan 2022 00:00:00 +0100 Estimation of the subsidence around the trace of the San Ramón Chile fault, using the SBAS DInSAR technique through TerraSAR-X images http://ojs.upv.es/index.php/raet/article/view/15640 <p>Chile is one of the countries with the highest seismicity in the world and is affected by three types of seismogenic sources; interplate, intraplate and superficial or cortical intraplate. In this context, in the eastern sector of the city of Santiago, capital of Chile, the Falla San Ramón (FSR) is located. It is a cortical seismogenic source, which threatens its habitants and the various economic activities that are located in that sector, geological hazards such as earthquakes and mass removals. In relation to the above, this study aims to identify and establish the subsidence areas in a longitudinal strip of the Santiago mountain front and its impact on the neighboring communes to the FSR trace during the years 2011 to 2017. To do this, The DInSAR technique was used with the Small Baseline Subset (SBAS) algorithm through a time series of images from the TerraSAR-X (TSX) satellite. The results show subsidence zones, with average displacements ranging from -13.11 mm to +9.89 mm, with an average annual speed rate of -2.19 to +1.65 mm/year.</p> Patricio Lamperein-Polo, Paulina Vidal-Páez, Waldo Pérez-Martínez Copyright (c) 2022 Patricio Alberto Lamperein Polo http://ojs.upv.es/index.php/raet/article/view/15640 Mon, 31 Jan 2022 00:00:00 +0100 AmazonCRIME: a Geospatial Artificial Intelligence dataset and benchmark for the classification of potential areas linked to Transnational Environmental Crimes in the Amazon Rainforest http://ojs.upv.es/index.php/raet/article/view/15710 <p>In this article the challenge of detecting areas linked to transnational environmental crimes in the Amazon rainforest is addressed using Geospatial Intelligence data, open access Sentinel-2 imagery provided by the Copernicus programme, as well as the cloud processing capabilities of the Google Earth Engine platform. For this, a dataset consisting of 6 classes with a total of 30,000 labelled and geo-referenced 13-band multispectral images was generated, which is used to feed advanced Geospatial Artificial Intelligence models (deep convolutional neural networks) specialised in image classification tasks. With the dataset presented in this paper it is possible to obtain a classification overall accuracy of 96.56%. It is also demonstrated how the results obtained can be used in real applications to support decision making aimed at preventing Transnational Environmental Crimes in the Amazon rainforest. The AmazonCRIME Dataset is made publicly available in the repository: https://github.com/jp-geoAI/AmazonCRIME.git.</p> Jairo J. Pinto-Hidalgo, Jorge A. Silva-Centeno Copyright (c) 2022 Jairo Jesus Pinto Hidalgo http://ojs.upv.es/index.php/raet/article/view/15710 Mon, 31 Jan 2022 00:00:00 +0100 Bottom-up estimates of atmospheric emissions of COâ‚‚, NOâ‚‚, CO, NH₃, and Black Carbon, generated by biomass burning in the north of South America http://ojs.upv.es/index.php/raet/article/view/15594 <p>Biomass burning is an important source of greenhouse gases (GHG) and air pollutants (AP) in developing countries. In this research, a bottom-up method was implemented for the estimation of emissions, emphasizing the validation process of aerial biomass products (AGB), which it has not been sufficiently approached from the point of view of the quantification of emissions. The most recent results on the validation of burned area (AQ) products and the analysis of uncertainty were also incorporated into the process of estimating the emissions of gases that directly or indirectly promote the greenhouse effect, such as COâ‚‚, NOâ‚‚, CO, NH₃, and Black Carbon (BC). In total, 87.60 Mha were burned in the region between 2001 and 2016, represented in a 57% by pasture lands a 23% by savannas, an 8% by savanna woodlands, an 8% by mixed soils with crops and natural vegetation, a 3% by evergreen broadleaf forests, and a 1 % in the region´s remaining types of land cover. With 35480 reference polygons, a model based on the uncertainty of AQ was generated, which served to find the calibration factor of the FireCCI5.0 in all the studied species. The total emissions (minimum and maximum) and the average of the same in the study period were the following: 1760 Tg COâ‚‚ (765.07-2552.88; average 110 Tg), 68.12 Tg of CO (27.11-98.87; average 4.26 Tg), 3.05 Tg of NOâ‚‚ (1.27-4.40; average 0.19 Tg), 0.76 Tg of NH₃ (0.33-1.12; average 0.05 Tg), and 0.44 Tg of Black Carbon (0.015-0.64; average 0.03 Tg).</p> Germán M. Valencia, Jesús A. Anaya, Francisco J. Caro-Lopera Copyright (c) 2022 Germán Mauricio Valencia Hernández, Jesús Adolfo Anaya Acevedo, Francisco José Caro-Lopera https://creativecommons.org/licenses/by-nc-sa/4.0 http://ojs.upv.es/index.php/raet/article/view/15594 Mon, 31 Jan 2022 00:00:00 +0100 Cartography of citrus crops abandonment using altimetric data: LiDAR and SfM photogrammetry http://ojs.upv.es/index.php/raet/article/view/16698 <p>The Comunitat Valenciana region (Spain) is the largest citrus producer in Europe. However, it has suffered an accelerated land abandonment in recent decades. Agricultural land abandonment is a global phenomenon with environmental and socio-economic implications. The small size of the agricultural parcels, the highly fragmented landscape and the low spectral separability between productive and abandoned parcels make it difficult to detect abandoned crops using moderate resolution images. In this work, an approach is applied to monitor citrus crops using altimetric data. The study uses two sources of altimetry data: LiDAR from the National Plan for Aerial Orthophotography (PNOA) and altimetric data obtained through an unmanned aerial system applying photogrammetric processes (Structure from Motion). The results showed an overall accuracy of 67,9% for the LiDAR data and 83,6% for the photogrammetric data. The high density of points in the photogrammetric data allowed to extract texture features from the Gray Level Co-Occurrence Matrix derived from the Canopy Height Model. The results indicate the potential of altimetry information for monitoring abandoned citrus fields, especially high-density point clouds. Future research should explore the fusion of spectral, textural and altimetric data for the study of abandoned citrus crops.</p> Sergio Morell-Monzó, María-Teresa Sebastiá-Frasquet, Javier Estornell Copyright (c) 2022 Sergio Morell-Monzó, María-Teresa Sebastiá-Frasquet, Javier Estornell http://ojs.upv.es/index.php/raet/article/view/16698 Mon, 31 Jan 2022 00:00:00 +0100 Estimation of barley yield from Sentinel-1 and Sentinel-2 imagery and climatic variables http://ojs.upv.es/index.php/raet/article/view/15099 <p>A precise estimation of agricultural production provides relevant information for upcoming seasons, and helps in the assessment of crop losses before harvest in case of adverse situations. The objective of this work is to explore the development of a model capable of estimating barley production of a small agricultural production (127 ha) in Belchite, Spain. Variables adapted to the crop calendar of the growing barley are used to achieve that purpose. The variables have been created with weather data and remote sensing images. These images are acquired in two ranges of the electromagnetic spectrum, i.e., microwaves and optical spectral range, obtained from Sentinel-1 and Sentinel-2, respectively. Models are defined with a multiple linear regression method using all combinations of the independent variables correlated with production. The best linear regression model has a prediction error of 57.38 kg/ha (4%). The use of spectral variables, derived from radar vegetation index Cross Ratio (CR) and optical Inverted Red Edge Chlorophyll Index (IRECI), and climatic variables adapted to the crop calendar and climatic conditioning is revealed as an adequate strategy to obtain adjusted models.</p> Cristian Iranzo, Raquel Montorio, Alberto García-Martín Copyright (c) 2022 Cristian Iranzo, Raquel Montorio, Alberto García-Martín http://ojs.upv.es/index.php/raet/article/view/15099 Mon, 31 Jan 2022 00:00:00 +0100