24 research outputs found

    ANALIZA RGB I MULTISPEKTRALNE KAMERE NA BESPILOTNOME ZRAKOPLOVU ZA KLASIFIKACIJU KUKURUZA STROJNIM UČENJEM

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    This study investigated a crop and soil classification applying the Random Forest machine learning algorithm based on the red-green-blue (RGB) and multispectral sensor imaging deploying an unmanned aerial vehicle (UAV). The study area covered two 10 x 10 m subsets of a maize-sown agricultural parcel near Koška. The highest overall accuracy was obtained in the combination of the red edge (RE), near-infrared (NIR), and normalized difference vegetation index (NDVI) in both subsets, with a 99.8% and 91.8% overall accuracy, respectively. The conducted analysis proved that the RGB camera obtained sufficient accuracy and was an acceptable solution to the soil and vegetation classification. Additionally, a multispectral camera and spectral analysis allowed for a more detailed analysis, primarily of the spectrally similar areas. Thus, this procedure represents a basis for both the crop density calculation and weed detection while deploying an unmanned aerial vehicle. To ensure crop classification effectiveness in practical application, it is necessary to further integrate the weed classes in the current vegetation class and separate them into crop and weed classes.U ovoj studiji istražena je klasifikacija usjeva i tla korištenjem algoritma strojnoga učenja Random Forest, temeljenoga na crveno-zeleno-plavoj (RGB) i multispektralnoj kameri integriranoj na bespilotnome zrakoplovu. Područje istraživanja obuhvaćalo je dva podskupa poljoprivredne čestice kukuruza dimenzija 10 x 10 m u blizini Koške. Najveća ukupna točnost klasifikacije postignuta je u kombinaciji rubnoga crvenog (RE), bliskoga infracrvenog (NIR) kanala i indeksa normalizirane vegetacijske razlike (NDVI) u oba podskupa, s ukupnom točnošću od 99,8 %, odnosno 91,8 %. Provedena analiza pokazala je da je RGB kamera postigla dovoljnu točnost i da je prihvatljivo rješenje za klasifikaciju tla i vegetacije. Međutim, multispektralna kamera i spektralna analiza omogućile su detaljniju analizu, prvenstveno za spektralno slična područja. Ovaj je postupak temelj i za izračun gustoće usjeva i za otkrivanje korova s pomoću bespilotnih zrakoplova. Kako bi se osigurala učinkovitost klasifikacije usjeva u praktičnoj primjeni, potrebno je dodatno uključiti klase korova u trenutačnu klasu vegetacije i podijeliti ih na klase usjeva i korova

    Aplikasi Citra Drone untuk Klasifikasi Vegetasi di Cagar Alam Curah Manis Sempolan 1 Menggunakan Metode Manual, Object Base Image Analysis (OBIA), dan K-Means

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    Nowadays, vegetation classification can be used to find out the latest information about the characteristics and distribution of vegetation in an area. However, a conservative process to differentiate vegetation was ineffective. Some of those limitations are poor accessibility that does work less safety, time-consuming, and needs a lot of human resources. On the other hand, remote sensing offers solutions that cannot be done by the simple method, such as how to take the data, time-consuming are less, and human resource needs are less as well. The purpose of this study was to classify, measured the area of each vegetation, and compared the effectiveness of the unsupervised used K-Means algorithm and supervised used Object Base Image Analysis algorithm methods vegetation classification. For accuracy calculation with confusion matrix, the classification results of the two methods were compared with the manual digitization method. Data was taken using drones in the area of the Curah Manis Sempolan Nature Reserve 1. Classification of vegetation consists of 5 vegetation types, which was apak, bush, pine, bendo, and dadap. The total area of the study area was 1.633 ha, and area vegetation of each classification was apak 0.224 ha; bush 0.748 ha; pine 0.394 ha; bendo 0.222 ha; and dadap 0.045 ha. The results of the calculation of accuracy showed that the unsupervised method had a value for overall accuracy of 80% and kappa accuracy of 73.58%. Then, in the supervised for overall accuracy is 68% and kappa accuracy of 58.72%. Keywords: classification, drone, remote sensing, satellit

    Comparations of Supervised Machine Learning Techniques in Predicting the Classification of the Household’s Welfare Status

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    Poverty has been a major problem for most countries around the world, including Indonesia. One approach to eradicate poverty is through equitable distribution of social assistance for target households based on Integrated Database of social assistance. This study has compared several well-known supervised machine learning techniques, namely: Naïve Bayes Classifier, Support Vector Machines, K-Nearest Neighbor Classification, C4.5 Algorithm, and Random Forest Algorithm to predict household welfare status classification by using an Integrated Database as a study case. The main objective of this study was to choose the best-supervised machine learning approach in predicting the classification of household’s welfare status based on attributes in the Integrated Database. The results showed that the Random Forest Algorithm was the best

    CROP CLASSIFICATION ON SINGLE DATE SENTINEL-2 IMAGERY USING RANDOM FOREST AND SUPPOR VECTOR MACHINE

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    Mapping of the crop using satellite images is a challenging task due to complexities within field, and having the similar spectral properties with other crops in the region. Recently launched Sentinel-2 satellite has thirteen spectral bands, fast revisit time and resolution at three different level (10 m, 20 m, 60 m), as well as the free availability of data, makes it a good choice for vegetation mapping. This study aims to classify crop using single date Sentinel-2 imagery in the Roorkee, district Haridwar, Uttarakhand, India. Classification is performed by using two most popular and efficient machine learning algorithms: Random Forest (RF) and Support Vector Machine (SVM). In this study, four spectral bands, i.e., Near Infrared, Red, Green, and Blue of Sentinel-2 satellite are stacked for the classification. Results show that overall accuracy of the classification achieved by RF and SVM using Sentinel-2 imagery are 84.22% and 81.85% respectively. This study demonstrates that both classifiers performed well by setting an optimal value of tuning parameters, but RF achieved 2.37% higher overall accuracy over SVM. Analysis of the results states that the class specific accuracies of High-Density Forest attain the highest accuracy whereas Fodder class reports the lowest accuracy. Fodder achieve lowest accuracy because there is an intermixing of pixels among Wheat and Fodder crops. In this study, it is found that RF shows better potential in classifying crops more accurately in comparison to SVM and Sentinel-2 has great potential in vegetation mapping domain in remote sensing

    Inconsistencies of the permanent preservation areas of the rural environmental registry through Geobia

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    As regras de exploração e de conservação da vegetação nativa, no Brasil, estão instituídas no Novo Código Florestal. O registro das informações dos imóveis rurais é realizado a partir do Cadastro Ambiental Rural (CAR). O objetivo desse estudo édesenvolver e avaliar uma metodologia para mapear conflitos da cobertura e uso da terra em APPs averbadas no CAR, integrando imagens orbitais de multissensores e de multirresoluções com GEOBIA. Para isso, foram utilizadas imagens Sentinel 1 e 2A. As bandas ópticas do Sentinel 2A com resolução espacial de 10 m foram utilizadas no processo de segmentação, considerando os parâmetros limiar de similaridade e o Tamanho Mínimo do Objeto (TMO). A segmentação com a menor distância Euclidiana (D) foi utilizada na avaliação dos parâmetros do método Random Forest(RF). Os resultados mostram que o desvio padrão das texturas Momento da Diferença Inversa e Variância foram os mais relevantes para discriminar as classes de cobertura e uso da terra. Nas APPs averbadas em áreas menores que 1 módulo fiscal e entre 1 e 2 módulos fiscais, a maior parte foi classificada como Campo e Mata nativa. Nas APPs averbadas em propriedades entre 2 e 4 módulos fiscais e maiores que 4 módulos fiscais, observa-se aumento de Solo exposto e Agricultura. Os resultados permitiram quantificar que as maiores inconsistências entre as APPs averbadas e a classificação da cobertura e uso da terra foram identificadas em propriedades superiores a 4 módulos.In Brazil, the rules for exploration and conservation of native vegetation are established in the New Forest Code, the registration of information on rural properties is carried out through the Rural Environmental Registry (CAR). The objective of the study is to develop and evaluate a methodology for mapping the inconsistencies between land cover and land use with PermanentPreservation Areas (APPs) recorded in the CAR. For this, Sentinel 1 and 2A images were used. The optical bands of Sentinel 2A with spatial resolution of 10 m were used in the segmentation process, considering the similarity threshold parameters and Minimum Object Size (TMO). Segmentation with the shortest Euclidean distance (D) was used in the evaluation of the parameters of the Random Forest (RF) method. The standard deviation of the Moment of the Inverse Difference and Variance textures were the most relevant to discriminate the classes of land cover and use. For APPs registered with areas smaller than 1 fiscal module, for a total of 95.96 ha, most were classified as native Field and Forest. In areas between 1 and 2 fiscal modules, total of 119.62 ha, the most prevalent classes were also the native Campo and Mata classes

    INCONSISTÊNCIAS DAS ÁREAS DE PRESERVAÇÃO PERMANENTE DO CADASTRO AMBIENTAL RURAL POR MEIO DA GEOBIA: Inconsistencies of the Permanent Preservation Areas of the Rural Environmental Registry through GEOBIA

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    In Brazil, the rules for exploration and conservation of native vegetation are established in the New Forest Code, the registration of information on rural properties is carried out through the Rural Environmental Registry (CAR). The objective of the study is to develop and evaluate a methodology for mapping the inconsistencies between land cover and land use with Permanent Preservation Areas (APPs) recorded in the CAR. For this, Sentinel 1 and 2A images were used. The optical bands of Sentinel 2A with spatial resolution of 10 m were used in the segmentation process, considering the similarity threshold parameters and Minimum Object Size (TMO). Segmentation with the shortest Euclidean distance (D) was used in the evaluation of the parameters of the Random Forest (RF) method. The standard deviation of the Moment of the Inverse Difference and Variance textures were the most relevant to discriminate the classes of land cover and use. For APPs registered with areas smaller than 1 fiscal module, for a total of 95.96 ha, most were classified as native Field and Forest. In areas between 1 and 2 fiscal modules, total of 119.62 ha, the most prevalent classes were also the native Campo and Mata classes.No Brasil, as regras de exploração e de conservação da vegetação nativa estão instituídas no Novo Código Florestal, o registro das informações dos imóveis rurais é realizado através do Cadastro Ambiental Rural (CAR). O objetivo do estudo é desenvolver e avaliar uma metodologia para o mapeamento das inconsistências entre a cobertura e o uso da terra com Áreas de Preservação Permanente (APPs) averbadas no CAR. Para isso, foram utilizadas imagens Sentinel 1 e 2A. As bandas ópticas do Sentinel 2A com resolução espacial de 10 metros foram utilizadas no processo de segmentação, considerando os parâmetros limiar de similaridade e o Tamanho Mínimo do Objeto (TMO). A segmentação com a menor distância Euclidiana (D) foi utilizada na avaliação dos parâmetros do método Random Forest (RF). O desvio padrão das texturas Momento da Diferença Inversa e Variância foram os mais relevantes para discriminar as classes de cobertura e uso da terra. Para APPs averbadas com áreas menores que 1 módulo fiscal, num total de 95,96 hectares, a maior parte foi classificada como Campo e Mata nativa. Em áreas entre 1 e 2 módulos fiscais, total de 119,62 hectares, as classes mais predominantes também foram as classes Campo e Mata nativa

    Evaluation of segmentation parameters in OBIA for classification of land covers from UAV images

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    [EN] Unmanned Aerial Vehicles (UAVs) have given a new boost to remote sensing and image classification techniques due to the high level of detail among other factors. Object-based image analysis (OBIA) could improve classification accuracy unlike to pixel-based, especially in high-resolution images. OBIA application for image classification consists of three stages i.e., segmentation, class definition and training polygons, and classification. However, defining the parameters: spatial radius (SR), range radius (RR) and minimum region size (MR) is necessary during the segmentation stage. Despite their relevance, they are usually visually adjusted, which leads to a subjective interpretation. Therefore, it is of utmost importance to generate knowledge focused on evaluating combinations of these parameters. This study describes the use of the mean-shift segmentation algorithm followed by Random Forest classifier using Orfeo Toolbox software. It was considered a multispectral orthomosaic derived from UAV to generate a suburban map land cover in town of El Pueblito, Durango, Mexico. The main aim was to evaluate efficiency and segmentation quality of nine parameter combinations previously reported in scientific studies.This in terms of number generated polygons, processing time, discrepancy measures for segmentation and classification accuracy metrics. Results evidenced the importance of calibrating the input parameters in the segmentation algorithms. Best combination was RE=5, RR=7 and TMR=250, with a Kappa index of 0.90 and shortest processing time. On the other hand, RR showed a strong and inversely proportional degree of association regarding the classification accuracy metrics.[ES] Los Vehículos Aéreos No Tripulados (VANT) han otorgado un nuevo auge a la teledetección y a las técnicas d clasificación de imágenes debido al alto nivel de detalle entre otros factores. El análisis de imágenes basado en objetos (OBIA) puede mejorar la precisión en la clasificación a diferencia de la basada en píxeles, especialmente en imágenes de alta resolución. La aplicación de OBIA para la clasificación de imágenes consta de tres etapas i.e., segmentación, definición de clases y polígonos de entrenamiento y clasificación. No obstante, en la etapa de segmentación es necesario definir los parámetros: radio espacial (RE), radio de rango (RR) y tamaño mínimo de la región (TMR). Los cuales, pese a su relevancia, suelen ser ajustados de manera visual, lo que conlleva a una interpretación subjetiva. Por lo anterior, es de suma importancia generar conocimiento enfocado a evaluar las combinaciones de estos parámetros. Este estudio describe el uso del algoritmo de segmentación de desplazamiento medio, seguido del clasificador Random Forest mediante el software Orfeo Toolbox. Se consideró un ortomosaico multiespectral derivado de VANT para generar un mapa de cobertura de suelo sub-urbano en la localidad El Pueblito, Durango, México. El objetivo principal fue evaluar la eficiencia y calidad de segmentación de nueve combinaciones de parámetros anteriormente reportadas en estudios científicos. Ello en términos de número de polígonos generados, tiempo de procesamiento, medidas de discrepancia de segmentación y métricas de precisión de la clasificación. Los resultados obtenidos lograron evidenciar la importancia de ajustar los parámetros de entrada en los algoritmos de segmentación. La mejor combinación fue RE=5, RR=7 y TMR=250, con un índice de Kappa de 0,90 y el menor tiempo de procesamiento. Por otro lado, el RR presentó  un grado de asociación fuerte e inversamente proporcional con las métricas de precisión de clasificación.Se agradece al Consejo Nacional de Ciencia y Tecnología (Conacyt) por el financiamiento otorgado a la primera autora para la realización de sus estudios de maestría, así como al programa de Maestría en Geomática Aplicada a Recursos Forestales y Ambientales de la Facultad de Ciencias Forestales de la UJED.Hinojosa-Espinoza, SI.; Gallardo-Salazar, JL.; Hinojosa-Espinoza, FJC.; Meléndez-Soto, A. (2021). Evaluación de parámetros de segmentación en OBIA para la clasificación de coberturas del suelo a partir de imágenes VANT. Revista de Teledetección. 0(58):89-103. https://doi.org/10.4995/raet.2021.14782OJS89103058Abburu, S., Golla, S.B. 2015. Satellite image classification methods and techniques: A review. International Journal of Computer Applications, 119(8), 20-25. https://doi.org/10.5120/21088-3779Adelabu, S., Mutanga, O., Adam, E. 2015. Testing the reliability and stability of the internal accuracy assessment of random forest for classifying tree defoliation levels using different validation methods. Geocarto International, 30(7), 810-821. https://doi.org/10.1080/10106049.2014.997303Al-Najjar, H.A.H., Kalantar, B., Pradhan, B., Saeidi, V., Halin, A.A., Ueda, N., Mansor, S. 2019. Land Cover Classification from fused DSM and UAV Images Using Convolutional Neural Networks. Remote Sensing, 11(12), 1461. https://doi.org/10.3390/rs11121461Apriyanto, D., Jaya, I.N., Puspaningsih, N. 2019. Examining the object-based and pixel-based image analyses for developing stand volume estimator model. Indonesian Journal of Electrical Engineering and Computer Science, 15(3), 1586-1596. https://doi.org/10.11591/ijeecs.v15.i3.pp1586-1596Ballari, D., Orellana, D., Acosta, E., Espinoza, A., Morocho, V. 2016. UAV monitoring for environmental management in Galapagos Islands. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 41, 1105-1111. https://doi.org/10.5194/isprsarchives- XLI-B1-1105-2016Belgiu, M., Drǎguţ, L., Strobl, J. 2014. Quantitative evaluation of variations in rule-based classifications of land cover in urban neighbourhoods using WorldView-2 imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 87, 205-215. https://doi.org/10.1016/j.isprsjprs.2013.11.007Benarchid, O., Raissouni, N. 2014. Mean-shift Segmentation Parameters Estimator (MSPE): A new tool for Very High Spatial Resolution satellite images. International Conference on Multimedia Computing and Systems -Proceedings, 357-361. https://doi.org/10.1109/ICMCS.2014.6911184Blaschke, T. 2010. Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 2-16. https://doi.org/10.1016/j.isprsjprs.2009.06.004Brooke, C., Clutterbuck, B. 2020. Mapping Heterogeneous Buried Archaeological Features Using Multisensor Data from Unmanned Aerial Vehicles. Remote Sensing, 12(1), 41. https://doi.org/10.3390/rs12010041Burdziakowski, P. 2017. Evaluation of Open Drone Map Toolkit for Geodetic Grade Aerial Drone Mapping- Case Study. En: Proceedings International Multidisciplinary Scientific GeoConference-SGEM 2017, Gdańska, Polonia. 29 Junio-5 Julio. pp. 101-110. https://doi.org/10.5593/sgem2017/23/S10.013Carvajal-Ramírez, F., Marques da Silva, J.R., Agüera- Vega, F., Martínez-Carricondo, P., Serrano, J., Moral, F.J. 2019. Evaluation of Fire Severity Indices Based on Pre- and Post-Fire Multispectral Imagery Sensed from UAV. Remote Sensing, 11(9), 993. https://doi.org/10.3390/rs11090993Chuvieco, E. 2020. Revisión histórica y perspectivas de futuro de la Teledetección: desde el ERTS hasta los Sentinels. Mapping, 29(200), 30-32.Colomina, I., Molina, P. 2014. Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 92(2014), 79-97. https://doi.org/10.1016/j.isprsjprs.2014.02.013Comert, R., Avdan, U., Gorum, T., Nefeslioglu, H.A. 2019. Mapping of shallow landslides with object- based image analysis from unmanned aerial vehicle data. Engineering Geology, 260(2019), 105264. https://doi.org/10.1016/j.enggeo.2019.105264Congalton, R.G., Mead, R.A. 1983. A quantitative method to test for consistency and correctness in photointerpretation. Photogrammetric Engineering and Remote Sensing, 49(1), 69-74.Congalton, R.G., Green, K. 2009. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices; Second CRC Press Taylor & Francis Group: Boca Raton, FL, USA, Volume 48. https://doi.org/10.1201/9781420055139Cutler, D.R., Edwards Jr, T.C., Beard, K.H., Cutler, A., Hess, K.T., Gibson, J., Lawler, J.J. 2007. Random forests for classification in ecology. Ecology, 88(11), 2783-2792. https://doi.org/10.1890/07-0539.1Dash, J.P., Pearse, G.D., Watt, M.S. 2018. UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health. Remote Sensing, 10(8), 1216. https://doi.org/10.3390/rs10081216Dash, J.P., Watt, M.S., Pearse, G.D., Heaphy, M., Dungey, H.S. 2017. Assessing very high resolution UAV imagery for monitoring forest health during a simulated disease outbreak. ISPRS Journal of Photogrammetry and Remote Sensing, 131(2017), 1-14. https://doi.org/10.1016/j.isprsjprs.2017.07.007De Castro, A.I., Torres-Sánchez, J., Peña, J.M., Jiménez- Brenes, F.M., Csillik, O., López-Granados, F. 2018. An Automatic Random Forest-OBIA Algorithm for Early Weed Mapping between and within Crop Rows Using UAV Imagery. Remote Sensing, 10(2), 285. https://doi.org/10.3390/rs10020285De Luca, G., N. Silva, J.M., Cerasoli, S., Araújo, J., Campos, J., Di Fazio, S., Modica, G. 2019. Object-Based Land Cover Classification of Cork Oak Woodlands using UAV Imagery and Orfeo ToolBox. Remote Sensing, 11(10), 1238. https://doi.org/10.3390/rs11101238Dhingra, S., Kumar, D. 2019. A review of remotely sensed satellite image classification. International Journal of Electrical & Computer Engineering, 9(3), 1720-1731. https://doi.org/10.11591/ijece.v9i3.pp1720-1731Dongping M., Tianyu C., Hongyue., Longxiang L., Cheng Q., Jinyang D., 2012. Semivariogram-Based Spatial Bandwidth Selection for Remote Sensing Image Segmentation With Mean-Shift Algorithm. IEEE Geoscience and Remote Sensing Letters, 9(5), 813-817. https://doi.org/10.1109/lgrs.2011.2182604Enderle, D.I.M., Weih Jr, R.C. 2005. Integrating supervised and unsupervised classification methods to develop a more accurate land cover classification. Journal of the Arkansas Academy of Science, 59(1), 65-73.Farfaglia, S., Lollino, G., Iaquinta, M., Sale, I., Catella, P., Martino, M., Chiesa, S. 2015. The use of UAV to monitor and manage the territory: perspectives from the SMAT project. En Engineering Geology for Society and Territory- Volume 5 (pp. 691-695). Springer, Cham. https://doi.org/10.1007/978-3-319-09048-1_134Feng, Q., Liu, J., Gong, J. 2015. UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis. Remote Sensing, 7(1), 1074-1094. https://doi.org/10.3390/rs70101074Gallardo-Salazar, J., Pompa-García, M., Aguirre- Salado, C., López-Serrano, P., Meléndez-Soto, A. 2020. Drones: tecnología con futuro promisorio en la gestión forestal. Revista Mexicana de Ciencias Forestales, 11(61), 28-50. https://doi.org/10.29298/ rmcf.v11i61.794Gao, J., Liao, W., Nuyttens, D., Lootens, P., Vangeyte, J., Pižurica, A., He, Y., Pieters, J.G. 2018. Fusion of pixel and object-based features for weed mapping using unmanned aerial vehicle imagery. International Journal of Applied Earth Observation and Geoinformation, 67(2018), 43-53. https://doi. org/10.1016/j.jag.2017.12.012Geneletti, D., Gorte, B.G.H. 2003. A method for object- oriented land cover classification combining Landsat TM data and aerial photographs. International Journal of Remote Sensing, 24(6), 1273-1286. https://doi.org/10.1080/01431160210144499Hasmadi, M., Pakhriazad, H.Z., Shahrin, M.F. 2009. Evaluating supervised and unsupervised techniques for land cover mapping using remote sensing data. Geografia: Malaysian Journal of Society and Space, 5(1), 1-10.Hossain, M.D., Chen, D. 2019. Segmentation for Object-Based Image Analysis (OBIA): A review of algorithms and challenges from remote sensing perspective. ISPRS Journal of Photogrammetry and Remote Sensing, 150(2019), 115-134. https://doi. org/10.1016/j.isprsjprs.2019.02.009Immitzer, M., Vuolo, F., Atzberger, C. 2016. First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe. Remote Sensing, 8(3), 166. https://doi.org/10.3390/ rs8030166Inzunza-López, J.O., López-Ariza, B., Valdez-Cepeda, R.D., Mendoza, B., Sánchez-Cohen, I., García- Herrera, G. 2011. La variación de las temperaturas extremas en la 'Comarca Lagunera' y cercanías. Revista Chapingo Serie Ciencias Forestales y del Ambiente, 17(2011), 45-61. https://doi.org/10.5154/r. rchscfa.2010.09.071Jain, M., Tomar, P.S. 2013. Review of image classification methods and techniques. International Journal of Engineering Research and Technology, 2(8), 852-858.Jara, C., Delegido, J., Ayala, J., Lozano, P., Armas, A., Flores, V. 2019. Estudio de bofedales en los Andes ecuatorianos a través de la comparación de imágenes Landsat-8 y Sentinel-2. Revista de Teledetección, 53(2019), 45-57. https://doi.org/10.4995/ raet.2019.11715Kakooei, M., Baleghi, Y. 2017. Fusion of satellite, aircraft, and UAV data for automatic disaster damage assessment. International Journal of Remote Sensing, 38(8-10), 2511-2534. https://doi.org/10.1080/01431161.2017.1294780Khatami, R., Mountrakis, G., Stehman, S.V. 2016. A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research. Remote Sensing of Environment, 177(2016), 89-100. https://doi.org/10.1016/j.rse.2016.02.028Langhammer, J., Vacková, T. 2018. Detection and Mapping of the Geomorphic Effects of Flooding Using UAV Photogrammetry. Pure and Applied Geophysics, 175(9), 3223-3245. https://doi.org/10.1007/s00024-018-1874-1Li, M., Ma, L., Blaschke, T., Cheng, L., Tiede, D. 2016. A systematic comparison of different object- based classification techniques using high spatial resolution imagery in agricultural environments. International Journal of Applied Earth Observation and Geoinformation, 49(2016), 87-98. https://doi. org/10.1016/j.jag.2016.01.011Li, M., Zang, S., Zhang, B., Li, S., Wu, C. 2014. AReview of Remote Sensing Image Classification Techniques: the Role of Spatio-contextual Information. European Journal of Remote Sensing, 47(1), 389-411. https:// doi.org/10.5721/EuJRS20144723Li, S., Tang, H., Huang, X., Mao, T., Niu, X. 2017. Automated Detection of Buildings from Heterogeneous VHR Satellite Images for Rapid Response to Natural Disasters. Remote Sensing, 9(11), 1177. https://doi.org/10.3390/rs9111177Liu, Y., Biana, L., Menga, Y., Wanga, H., Zhanga, S., Yanga, Y., Shaoa, X., Wang, B., 2012. Discrepancy measures for selecting optimal combination of parameter values in object-based image analysis. ISPRS Journal of Photogrammetry and Remote Sensing, 68(2012), 144-156. https://doi.org/10.1016/j.isprsjprs.2012.01.007Lu, D., Weng, Q. 2007. A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5), 823-870. https://doi.org/10.1080/01431160600746456Lyons, M.B., Keith, D.A., Phinn, S.R., Mason, T.J., Elith, J. 2018. A comparison of resampling methods for remote sensing classification and accuracy assessment. Remote Sensing of Environment, 208(2018), 145-153. https://doi.org/10.1016/j. rse.2018.02.026Ma, L., Cheng, L., Li, M., Liu, Y., Ma, X. 2015. Training set size, scale, and features in Geographic Object-Based Image Analysis of very high resolution unmanned aerial vehicle imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 102(2015), 14-27. https://doi.org/10.1016/j. isprsjprs.2014.12.026Ma, L., Li, M., Ma, X., Cheng, L., Du, P., Liu, Y. 2017. A review of supervised object-based land-cover image classification. ISPRS Journal of Photogrammetry and Remote Sensing, 130(2017), 277-293. https://doi.org/10.1016/j.isprsjprs.2017.06.001Mafanya, M., Tsele, P., Botai, J., Manyama, P., Swart, B., Monate, T. 2017. Evaluating pixel and object based image classification techniques for mapping plant invasions from UAV derived aerial imagery: Harrisia pomanensis as a case study. ISPRS Journal of Photogrammetry and Remote Sensing, 129(2017), 1-11. https://doi.org/10.1016/j.isprsjprs.2017.04.009Mangiameli, M., Mussumeci, G., Candiano, A. 2018. A low cost methodology for multispectral image classification. En Computational Science and Its Applications-ICCSA 2018 (pp. 263-280). Springer, Cham. https://doi.org/10.1007/978-3-319-95174- 4_22Matese, A., Toscano, P., Di Gennaro, S.F., Genesio, L., Vaccari, F.P., Primicerio, J., Belli, C., Zaldei, A., Bianconi, R., Gioli, B. 2015. Intercomparison of UAV, Aircraft and Satellite Remote Sensing Platforms for Precision Viticulture. Remote Sensing, 7(3), 2971-2990. https://doi.org/10.3390/ rs70302971Michel, J., Youssefi, D., Grizonnet, M. 2015. Stable Mean-Shift Algorithm and Its Application to the Segmentation of Arbitrarily Large Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing, 53(2), 952-964. https://doi. org/10.1109/TGRS.2014.2330857Monserud, R.A., Leemans, R. 1992. Comparing global vegetation maps with the Kappa statistic. Ecological Modelling, 62(4), 275-293. https://doi.org/10.1016/0304-3800(92)90003-WNenmaoui, A., Torres, M.Á.A., Novelli, A., Marín, M.C.V., Torres, F.J.A., Betlej, M., Cichón, P. 2017. Mapeado de invernaderos mediante teledetección orientada a objetos: relación entre la calidad de la segmentación y precisión de la clasificación. Revista Mapping, 26(181), 4-13. ISSN: 1131-9100Raissouni, N., Benarchid, O., Sobrino, J., Ayyan, A. 2015. Aplicación del Estimador de Parámetros de Segmentación por Media-desplazada (EPSM) a las imágenes de satélite de muy alta resolución espacial: Tetuán (Marruecos). Revista de Teledetección, 43(2015), 91-96. https://doi.org/10.4995/ raet.2015.3511Ramadhan Kete, S.C., Suprihatin, Tarigan, S.D., Effendi, H. 2019. Land use classification based on object and pixel using Landsat 8 OLI in Kendari City, Southeast Sulawesi Province, Indonesia. IOP Conference Series: Earth and Environmental Science, 284(2019), 012019. https://doi.org/10.1088/1755-1315/284/1/012019Rosenfield, G.H., Fitzpatrick-Lins, K. 1986. A coefficient of agreement as a measure of thematic classification accuracy. Photogrammetric Engineering and Remote Sensing, 52(2), 223-227.Sideris, K., Colson, D., Lightfoot, P., Heeley, L., Robinson, P. 2020. Review of image segmentation algorithms for analysing Sentinel-2 data over large geographical areas. JNCC Report No. 655. Peterborough, ISSN 0963-8091.Silalahi, R., Jaya, I.N., Tiryana, T., Mulia, F. 2018. Assessing the Crown Closure of Nypa on UAV Images using Mean-Shift Segmentation Algorithm. Indonesian Journal of Electrical Engineering and Computer Science, 9(3), 722-730. https://doi.org/10.11591/ijeecs.v9.i3.pp722-730Smits, P.C., Dellepiane, S.G., Schowengerdt, R.A. 1999. Quality assessment of image classification algorithms for land-cover mapping: A review and a proposal for a cost-based approach. International Journal of Remote Sensing, 20(8), 1461-1486. https://doi.org/10.1080/014311699212560Teodoro, A.C., Araujo, R. 2014. Exploration of the OBIA methods available in SPRING non- commercial software to UAV data processing. En: Proceedings SPIE 9245, Earth Resources and Environmental Remote Sensing/GIS Applications. https://doi.org/10.1117/12.2066468V. Amsterdam, Netherlands, 10 de Octubre. https://doi.org/10.1117/12.2066468Teodoro, A.C., Araujo, R. 2016. Comparison of performance of object-based image analysis techniques available in open source software (Spring and Orfeo Toolbox/Monteverdi) considering very high spatial resolution data. Journal of Applied Remote Sensing, 10(1), 1-22. https://doi.org/10.1117/1.JRS.10.016011Torres-Sánchez, J., López-Granados, F., Peña, J.M. 2015. An automatic object-based method for optimal thresholding in UAV images: Application for vegetation detection in herbaceous crops. Computers and Electronics in Agriculture, 114(2015), 43-52. https://doi.org/10.1016/j.compag.2015.03.019Trisasongko, B.H., Panuju, D.R., Paull, D.J., Jia, X., Griffin, A.L. 2017. Comparing six pixel-wise classifiers for tropical rural land cover mapping using four forms of fully polarimetric SAR data. International Journal of Remote Sensing, 38(11), 3274-3293. https://doi.org/10.1080/01431161.2017.1292072Villanueva Díaz, J., Stahle, D.W., Cerano Paredes, J., Estrada Ávalos, J., Constante García, V. 2013. Respuesta hidrológica del sabino en bosques de galería del Río San Pedro Mezquital, Durango. Revista Mexicana de Ciencias Forestales, 4(20), 9-19. https://doi.org/10.29298/rmcf.v4i20.366Vu, T.T. 2012. Object-based remote sensing image analysis with OSGeo tools. En: Proceedings FOSS4G Southeast Asia 2012, Johor Bahru, Malaysia. 18-19 Julio. pp. 79-84.Ye, S., Pontius, R.G., Rakshit, R. 2018. A review of accuracy assessment for object-based image analysis: From per-pixel to per-polygon approaches. ISPRS Journal of Photogrammetry and Remote Sensing, 141(2018), 137-147. https://doi.org/10.1016/j. isprsjprs.2018.04.002Zaraza-Aguilera, M.A., Manrique-Chacón, L.M. 2019. Generación de datos de cambio de coberturas vegetales en la sabana de Bogotá mediante el uso de series temporales con imágenes Landsat e imágenes sintéticas MODIS-Landsat entre los años 2007 y 2013. Revista de Teledetección, 54(2019), 41-58.https://doi.org/10.4995/raet.2019.1228

    Uncertainty analysis of object-based land cover classification using time series of Sentinel-2 data

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    Recently, time-series from optical satellite data have been frequently used in object-based land-cover classification. This poses a significant challenge to object-based image analysis (OBIA) owing to the presence of complex spatio-temporal information in the time-series data. This study evaluates object-based land-cover classification in the northern suburbs of Munich using time-series from optical Sentinel data. Using a random forest classifier as the backbone, experiments were designed to analyze the impact of the segmentation scale, features (including spectral and temporal features), categories, frequency, and acquisition timing of optical satellite images. Based on our analyses, the following findings are reported: (1) Optical Sentinel images acquired over four seasons can make a significant contribution to the classification of agricultural areas, even though this contribution varies between spectral bands for the same period. (2) The use of time-series data alleviates the issue of identifying the “optimal” segmentation scale. The finding of this study can provide a more comprehensive understanding of the effects of classification uncertainty on object-based dense multi-temporal image classification

    A framework for scale-sensitive, spatially explicit accuracy assessment of binary built-up surface layers

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    To better understand the dynamics of human settlements, thorough knowledge of the uncertainty in geospatial built-up surface datasets is critical. While frameworks for localized accuracy assessments of categorical gridded data have been proposed to account for the spatial non-stationarity of classification accuracy, such approaches have not been applied to (binary) built-up land data. Such data differs from other data such as land cover data, due to considerable variations of built-up surface density across the rural-urban continuum resulting in switches of class imbalance, causing sparsely populated confusion matrices based on small underlying sample sizes. In this paper, we aim to fill this gap by testing common agreement measures for their suitability and plausibility to measure the localized accuracy of built-up surface data. We examine the sensitivity of localized accuracy to the assessment support, as well as to the unit of analysis, and analyze the relationships between local accuracy and density / structure-related properties of built-up areas, across rural-urban trajectories and over time. Our experiments are based on the multi-temporal Global Human Settlement Layer (GHSL) and a reference database for the state of Massachusetts (USA). We find strong variation of suitability among commonly used agreement measures, and varying levels of sensitivity to the assessment support. We then apply our framework to assess localized GHSL data accuracy over time from 1975 to 2014. Besides increasing accuracy along the rural-urban gradient, we find that accuracy generally increases over time, mainly driven by peri-urban densification processes in our study area. Moreover, we find that localized densification measures derived from the GHSL tend to overestimate peri-urban densification processes that occurred between 1975 and 2014, due to higher levels of omission errors in the GHSL epoch 1975.Comment: 28 pages, 17 figure
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