Clasificación de cultivos y de sus medidas agroambientales mediante segmentación de imágenes QuickBird

En la últimas décadas han ido creciendo considerablemente los conocimientos y la sensibilización sobre la protección al medioambiente en muy diversas áreas, entre las que se encuentra la Agricultura. El uso intensivo del laboreo ocasiona graves daños medioambientales como la erosión del suelo, la contaminación de las aguas superficiales (escorrentía y colmatación de embalses), el descenso del contenido de la materia orgánica y de la biodiversidad de los suelos labrados, y el aumento de la emisión de CO2 del suelo a la atmósfera. Actualmente, la Unión Europea sólo subvenciona a los agricultores que cumplen lo que se conoce como “Medidas Agroambientales o de Condicionalidad” cuyo diseño ha estado dentro de las competencias de las Políticas Agrarias Autonómicas, Nacionales y Europeas. Estas medidas consisten en alterar el perfil y la estructura del suelo lo menos posible, dejando éste sin labrar y permanentemente protegido por cubiertas vegetales (rastrojo) en el caso de cultivos herbáceos (ej. trigo, maíz, girasol), o por cubiertas vegetales vivas o inertes (restos de poda) en el caso de cultivos leñosos (principalmente cítricos y olivar). El seguimiento del cumplimiento de estas medidas se realiza a través de visitas presenciales a un 1% de los campos susceptibles de recibir ayudas. Este método es ineficiente y provoca muchos errores con la consiguiente presentación de un ingente número de reclamaciones. Para subsanar esta problemática, en este artículo presentamos los resultados obtenidos en la clasificación de los cultivos y las medidas agroambientales asociadas a éstos en una imagen multiespectral QuickBird tomada a principios de Julio de una zona típica de cultivos en régimen de secano de Andalucía. Se aplicaron 5 métodos de clasificación (Paralelepípedos, P; Mínima Distancia, MD; Distancia de Mahalanobis, MC; Mapeo del Ángulo Espectral, SAM; y Máxima Probabilidad, ML) para la discriminación de rastrojo de trigo quemado y sin quemar, arbolado, carreteras, olivar, cultivos herbáceos de siembra primaveral y suelo desnudo. Además, la imagen es segmentada en objetos para comparar la fiabilidad obtenida aplicando los métodos anteriores partiendo tanto de píxeles como de objetos como Unidades Mínimas de Información (MIU). El análisis de los resultados permite concluir que las clasificaciones de todos los usos de suelo basadas en objetos claramente mejoraron las basadas en píxeles, obteniéndose precisiones (overall accuracy) mayores al 85%. La elección de un método de clasificación u otro influye en gran medida en la precisión de los mapas obtenidos. Debido a que la precisión del mapa temático que necesitamos obtener ha de ser muy elevada para tomar decisiones sobre Conceder / No conceder las ayudas, sería interesante estudiar si el incremento de la resolución espacial que se obtenga gracias a la fusión de imágenes multiespectral y pancromática de QuickBird para obtener una imagen fusionada con resolución espacial de la pancromática (0.7 m) y espectral de la multiespectral (4 bandas) mejora la precisión de cualquiera de los métodos de clasificación estudiadosSoil management in crops is mainly based on intensive tillage operations, which have a great relevancy in terms of increase of atmospheric CO2, desertification, erosion and land degradation. Due to these negative environmental impacts, the European Union only subsidizes cropping systems which require the implementation of certain no-tillage systems and agro-environmental measures, such as keeping the winter cereal residues and non-burning of stubble to reduce erosion, and to increase the organic matter, the fertility of soils and the crop production. Nowadays, the follow-up of these agrarian policy actions is achieved by ground visits to sample targeted farms; however, this procedure is time-consuming and very expensive. To improve this control procedure, a study of the accuracy performance of several classification methods has been examined to verify if remote sensing can offer the ability to efficiently identify crops and their agro-environmental measures in a typical agricultural Mediterranean area of dry conditions. Five supervised classification methods based on different decision rule routines, Parallelepiped (P), Minimum Distance (MD), Mahalanobis Classifier Distance (MC), Spectral Angle Mapper (SAM), and Maximum Likelihood (ML), were examined to determine the most suitable classification algorithm for the identification of agro-environmental measures such as winter cereal stubble and burnt stubble areas and other land uses such as river side trees, vineyard, olive orchards, spring sown crops, roads and bare soil. An object segmentation of the satellite information was also added to compare the accuracy of the classification results of pixel and object as Minimum Information Unit (MIU). A multispectral QuickBird image taken in early summer was used to test these MIU and classification methods. The resulting classified images indicated that object-based analyses clearly outperformed pixel ones, yielding overall accuracies higher than 85% in most of the classifications. The choice of a classification method can markedly influence the accuracy of classification maps

On the use of satellite Sentinel 2 data for automatic mapping of burnt areas and burn severity

In this paper, we present and discuss the preliminary tools we devised for the automatic recognition of burnt areas and burn severity developed in the framework of the EU-funded SERV_FORFIRE project. The project is focused on the set up of operational services for fire monitoring and mitigation specifically devised for decision-makers and planning authorities. The main objectives of SERV_FORFIRE are: (i) to create a bridge between observations, model development, operational products, information translation and user uptake; and (ii) to contribute to creating an international collaborative community made up of researchers and decision-makers and planning authorities. For the purpose of this study, investigations into a fire burnt area were conducted in the south of Italy from a fire that occurred on 10 August 2017, affecting both the protected natural site of Pignola (Potenza, South of Italy) and agricultural lands. Sentinel 2 data were processed to identify and map different burnt areas and burn severity levels. Local Index for Statistical Analyses LISA were used to overcome the limits of fixed threshold values and to devise an automatic approach that is easier to re-apply to diverse ecosystems and geographic regions. The validation was assessed using 15 random plots selected from in situ analyses performed extensively in the investigated burnt area. The field survey showed a success rate of around 95%, whereas the commission and omission errors were around 3% of and 2%, respectively. Overall, our findings indicate that the use of Sentinel 2 data allows the development of standardized burn severity maps to evaluate fire effects and address post-fire management activities that support planning, decision-making, and mitigation strategies.Fil: Lasaponara, Rosa. Consiglio Nazionale delle Ricerche; ItaliaFil: Tucci, Biagio. Consiglio Nazionale delle Ricerche; ItaliaFil: Ghermandi, Luciana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina. Universidad Nacional del Comahue. Centro Regional Universitario Bariloche. Laboratorio de Ecotono; Argentin

Improving land cover classification using genetic programming for feature construction

Batista, J. E., Cabral, A. I. R., Vasconcelos, M. J. P., Vanneschi, L., & Silva, S. (2021). Improving land cover classification using genetic programming for feature construction. Remote Sensing, 13(9), [1623]. https://doi.org/10.3390/rs13091623Genetic programming (GP) is a powerful machine learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in the field of remote sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs feature construction by evolving hyperfeatures from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyperfeatures from satellite bands to improve the classification of land cover types. We add the evolved hyperfeatures to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (decision trees, random forests, and XGBoost) on multiclass classifications and no significant effect on the binary classifications. We show that adding the M3GP hyperfeatures to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI, and NBR. We also compare the performance of the M3GP hyperfeatures in the binary classification problems with those created by other feature construction methods such as FFX and EFS.publishersversionpublishe

A Comparison of Spectral Angle Mapper and Artificial Neural Network Classifiers Combined with Landsat TM Imagery Analysis for Obtaining Burnt Area Mapping

Satellite remote sensing, with its unique synoptic coverage capabilities, can provide accurate and immediately valuable information on fire analysis and post-fire assessment, including estimation of burnt areas. In this study the potential for burnt area mapping of the combined use of Artificial Neural Network (ANN) and Spectral Angle Mapper (SAM) classifiers with Landsat TM satellite imagery was evaluated in a Mediterranean setting. As a case study one of the most catastrophic forest fires, which occurred near the capital of Greece during the summer of 2007, was used. The accuracy of the two algorithms in delineating the burnt area from the Landsat TM imagery, acquired shortly after the fire suppression, was determined by the classification accuracy results of the produced thematic maps. In addition, the derived burnt area estimates from the two classifiers were compared with independent estimates available for the study region, obtained from the analysis of higher spatial resolution satellite data. In terms of the overall classification accuracy, ANN outperformed (overall accuracy 90.29%, Kappa coefficient 0.878) the SAM classifier (overall accuracy 83.82%, Kappa coefficient 0.795). Total burnt area estimates from the two classifiers were found also to be in close agreement with the other available estimates for the study region, with a mean absolute percentage difference of ∼1% for ANN and ∼6.5% for SAM. The study demonstrates the potential of the examined here algorithms in detecting burnt areas in a typical Mediterranean setting

Remote sensing-based fire frequency mapping in a savannah rangeland

Burnt area mapping and fire frequency analysis were carried out in Hwange National Park, Zimbabwe. Hwange National Park typifies a savannah ecosystem which is semi-arid and fire-prone. This paper presents a geospatial analysis to quantify the spatial distribution and fire frequency from 2000 to 2006. Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2000 to 2006 were obtained and classified for burnt area mapping. Linear pixel unmixing was used for image classification and subsequent mapping of burnt areas. The results showed that it was feasible to have discrimination of burnt areas and ‘un-burnt’ areas as well as generating a six year fire frequency map of the study area. Accuracy assessment of the classified images was carried out using field obtained information on fire occurrence to validate the classification results. An error matrix quantified accuracy of classified maps through producer's accuracy, user's accuracy and overall accuracy. High overall accuracy rates of appromately 96%, in turne, justify use of linear pixel unmixing in identifying and mapping burnt areas. Thus pixel unmixing offers a viable mapping tool for fire monitoring and management in protected areas

Assessment of Spot Satellite Data for Tropical Vegetation Inventory and Monitoring in Sumatra

Following a previous vegetation mapping in Sumatra island (Indonesia), an assessment of SPOT satellite capability to handle specific problems related to vegetation identification and monitoring from remote sensing data has been undertaken. Results of visual interpretation and multispectral analysis have shown the usefulness of SPOT data for the appraisal of tropical vegetation at medium scale. This was particularly striking for the swampy vegetation types including mangroves and for the secondary vegetation, for which significant improvements have been brought by multispectral classifications. A 20 m ground resolution is neither sufficient to provide information on primary forest patterns, nor to identify properly logged over areas. Never theless, several degrees of depletion of the forest and all the serial stages have been identified, which is a considerable progress compared with previous remote sensing means. SPOT is a very good alternative to medium scale aerial photographs for the production of medium scale (1 : 100 000 to 1 : 250 000) vegetation and land-use maps

Estimation of Burned Area in the Northeastern Siberian Boreal Forest from a Long-Term Data Record (LTDR) 1982–2015 Time Series

A Bayesian classifier mapped the Burned Area (BA) in the Northeastern Siberian boreal forest (70°N 120°E–60°N 170°E) from 1982 to 2015. The algorithm selected the 0.05° (~5 km) Long-Term Data Record (LTDR) version 3 and 4 data sets to generate 10-day BA composites. Landsat-TM scenes of the entire study site in 2002, 2010, and 2011 assessed the spatial accuracy of this LTDR-BA product, in comparison to Moderate-Resolution Imaging Spectroradiometer (MODIS) MCD45A1 and MCD64A1 BA products. The LTDR-BA algorithm proves a reliable source to quantify BA in this part of Siberia, where comprehensive BA remote sensing products since the 1980s are lacking. Once grouped by year and decade, this study explored the trends in fire activity. The LTDR-BA estimates contained a high interannual variability with a maximum of 2.42 million ha in 2002, an average of 0.78 million ha/year, and a standard deviation of 0.61 million ha. Going from 6.36 in the 1980s to 10.21 million ha BA in the 2010s, there was a positive linear BA trend of approximately 1.28 million ha/decade during these last four decades in the Northeastern Siberian boreal forest