scholarly journals Cartografía del aguacate en el sur del estado de México mediante tratamiento digital de imágenes sentinel-2

2020 ◽  
Vol 11 (4) ◽  
pp. 865-879
Author(s):  
Dulce Karen Figueroa-Figueroa ◽  
Jose Francisco Ramírez Dávila ◽  
Xanat Antonio-Némiga ◽  
Andrés González Huerta

El cultivo de aguacate (Persea americana Mill.) es uno de los más importantes en México, entre los estados con mayor producción se encuentra el Estado de México, que es el tercer estado productor a nivel nacional. Coatepec Harinas y Donato Guerra son dos de los municipios más representativos en lo respectivo a esta actividad; sin embargo, no existe un censo que especifique la superficie del cultivo, por lo que el objetivo de esta investigación fue probar métodos de índices de vegetación, algoritmos spectral angle mapper (SAM) y spectral information divergence (SID) y la combinación de estos en las imágenes del sensor Sentinel-2 para evaluar su desempeño en la identificación de áreas plantadas con el cultivo de aguacate. Los resultados se validaron con una matriz de confusión y la comparación de los datos de referencia de entrenamiento y validación. El algoritmo SID alcanzó una precisión de 97.5% para detectar aguacate, mientras que el tratamiento SAM obtuvo una precisión de 63.1%. La combinación de SID con el índice Anthocyanin Reflectance Index 1 (ARI1), proporcionó un mejor resultado sobre la cartografía de validación regional con 85% de precisión. Otras combinaciones de índices y tratamientos dieron resultados inferiores al 50% de la precisión por lo que no se recomiendan. Esta metodología podría ser probada para la detección de otros cultivos de interés comercial, dado que Sentinel-2 muestra ser una alternativa viable para este tipo de estudios, teniendo una buena resolución espectral, además de ser de fácil acceso y manipulación.

2020 ◽  
Vol 12 (13) ◽  
pp. 2154 ◽  
Author(s):  
Ke Wang ◽  
Ligang Cheng ◽  
Bin Yong

Spectral similarity measures can be regarded as potential metrics for kernel functions, and can be used to generate spectral-similarity-based kernels. However, spectral-similarity-based kernels have not received significant attention from researchers. In this paper, we propose two novel spectral-similarity-based kernels based on spectral angle mapper (SAM) and spectral information divergence (SID) combined with the radial basis function (RBF) kernel: Power spectral angle mapper RBF (Power-SAM-RBF) and normalized spectral information divergence-based RBF (Normalized-SID-RBF) kernels. First, we prove these spectral-similarity-based kernels to be Mercer’s kernels. Second, we analyze their efficiency in terms of local and global kernels. Finally, we consider three hyperspectral datasets to analyze the effectiveness of the proposed spectral-similarity-based kernels. Experimental results demonstrate that the Power-SAM-RBF and SAM-RBF kernels can obtain an impressive performance, particularly the Power-SAM-RBF kernel. For example, when the ratio of the training set is 20 % , the kappa coefficient of Power-SAM-RBF kernel (0.8561) is 1.61 % , 1.32 % , and 1.23 % higher than that of the RBF kernel on the Indian Pines, University of Pavia, and Salinas Valley datasets, respectively. We present three conclusions. First, the superiority of the Power-SAM-RBF kernel compared to other kernels is evident. Second, the Power-SAM-RBF kernel can provide an outstanding performance when the similarity between spectral signatures in the same hyperspectral dataset is either extremely high or extremely low. Third, the Power-SAM-RBF kernel provides even greater benefits compared to other commonly used kernels when the sizes of the training sets increase. In future work, multiple kernels combining with the spectral-similarity-based kernel are expected to be provide better hyperspectral classification.


2021 ◽  
Vol 14 (6) ◽  
pp. 3577
Author(s):  
Celso Voos Vieira ◽  
Pedro Apolonid Viana

O objetivo deste trabalho foi a avaliação da acurácia de algoritmos de classificação do uso e cobertura do solo, quando aplicados a uma imagem orbital de média resolução espacial. Para esse estudo foram utilizadas as bandas espectrais da faixa do visível e infravermelho próximo, do sensor Operational Land Imager – OLI na Baía da Babitonga/SC. Foram propostas nove classes de cobertura do solo, que serviram como controle para testar 11 algoritmos classificadores: Binary Encoding, Example Based Feature Extraction, IsoData, K-Means, Mahalanobis Distance, Maximum Likelihood, Minimum Distance, Neural Net, Parallelepiped, Spectral Angle Mapper e Spectral Information Divergence. O classificador Maximum Likelihood foi o que apresentou o melhor desempenho, obtendo um índice Kappa de 0,89 e acurácia global de 95,5%, sendo capaz de distinguir as nove classes de cobertura do solo propostas. Evaluation of the Accuracy of Orbital Image Classification Algorithms in Babitonga Bay, northeast of Santa Catarina A B S T R A C TThe objective of this work was to evaluate the classification algorithms accuracy of the soil use and cover when applied to a spatial mean orbital image. For this study we used the visible and near infrared spectral bands of the Operational Land Imager - OLI sensor in Babitonga Bay / SC. Nine classes of soil cover were proposed, which served as control to test 11 classifier algorithms: Binary Encoding, Example Based Feature Extraction, IsoData, K-Means, Mahalanobis Distance, Maximum Likelihood, Minimum Distance, Neural Net, Parallelepiped, Spectral Angle Mapper and Spectral Information Divergence. The Maximum Likelihood classifier presented the best performance, obtaining a Kappa index of 0.89 and a global accuracy of 95.5%, being able to distinguish the nine proposed classes of soil cover.Keywords: Algorithms Accuracy, Babitonga Bay, Orbital image, Remote sensing, Soil Use and Cover. 


2021 ◽  
Vol 15 (4) ◽  
pp. 129-152
Author(s):  
Beata Hejmanowska ◽  
Mariusz Twardowski ◽  
Anna Żądło

The aim of the paper is to discuss the idea of marking agricultural parcels in the control of direct payments to agriculture. The method of using remote sensing to monitor crops and mark them according to the idea of “traffic lights” is introduced. Classification into a given “traffic lights” color gives clear information about the status of the parcel. The image classification was done on Sentinel-1 and Sentinel-2 datasets by calculating the NDVI and SIGMA time series in the season from autumn 2016 to autumn 2017. Two approaches are presented: semi-automated and automated classifications. Semi-automated classification based on NDVI_index and SIGMA_index. Automated classification was performed on NDVI by Spectral Angle Mapper method and on SIGMA by Artificial Neural Network (Multilayer Perceptron, MLP method). The following overall accuracy was obtained for NDVI_SAM: 70.35%, while for SIGMA_CNN it was: 62.01%. User accuracy (UA) values were adopted for traffic lights analysis, in machine learning: positive predictive value (PPV). The UA/PPV for rapeseed were in NDVI_index method: 88.1% (6,986 plots), NDVI_SAM: 85.0% (199 plots), SIGMA_index: 61.3% (4,165 plots) and in SIGMA_CNN: 88.9% (2,035 plots). In order to present the idea of “traffic lights”, a website was prepared using data from the NDVI_index method, which is a trade-off between the number of plots and UA/PPV accuracy.


2022 ◽  
Vol 951 (1) ◽  
pp. 012068
Author(s):  
N Lisviananda ◽  
S Sugianto ◽  
M Rusdi

Abstract Remote sensing data provides fast and relatively accurate information to retrieve the plant growth phase using spectral analysis. Spectral analysis of plants is the critical point of identifying the stages of rice growth using Sentinel-2 data. Sentinel-2 satellite images were utilized for this study. This study aims to analyze the growth phase of rice in Pidie regency, Aceh Province, Indonesia, as a sample area of the rice-growing site. The Spectral Angle Mapper (SAM) approach was performed to describe the plant growth stages. The results show variations in the rice growth phase across the study area for 2019, 2020, and 2021 growing seasons from vegetative, generative, wet fallow, and dry fallow. The most extensive vegetative phase is for April 2021 data, counting for 1,278.16 Ha. The most extensive generative phase was identified of June 2020 data, counting for 1,107.55 Ha. For wet fallow, counting for 949,30 Ha is the largest in this category. A total of 1,311.94 Ha of dry fallow is identified in 2019. The different growth phases and the total area for different years indicate variation in starting for the growing season of the sample location. In this paper, multitemporal Sentinel-2 data analyzed with the SAM approach has demonstrated identifying rice-growing season phases. This finding can help predict the total area along the year for a change of the pattern of the rice-growing season in the last three years of the study area.


2021 ◽  
Vol 13 (6) ◽  
pp. 1178
Author(s):  
Jordi Cristóbal ◽  
Patrick Graham ◽  
Anupma Prakash ◽  
Marcel Buchhorn ◽  
Rudi Gens ◽  
...  

A pilot study for mapping the Arctic wetlands was conducted in the Yukon Flats National Wildlife Refuge (Refuge), Alaska. It included commissioning the HySpex VNIR-1800 and the HySpex SWIR-384 imaging spectrometers in a single-engine Found Bush Hawk aircraft, planning the flight times, direction, and speed to minimize the strong bidirectional reflectance distribution function (BRDF) effects present at high latitudes and establishing improved data processing workflows for the high-latitude environments. Hyperspectral images were acquired on two clear-sky days in early September, 2018, over three pilot study areas that together represented a wide variety of vegetation and wetland environments. Steps to further minimize BRDF effects and achieve a higher geometric accuracy were added to adapt and improve the Hyspex data processing workflow, developed by the German Aerospace Center (DLR), for high-latitude environments. One-meter spatial resolution hyperspectral images, that included a subset of only 120 selected spectral bands, were used for wetland mapping. A six-category legend was established based on previous U.S. Geological Survey (USGS) and U.S. Fish and Wildlife Service (USFWS) information and maps, and three different classification methods—hybrid classification, spectral angle mapper, and maximum likelihood—were used at two selected sites. The best classification performance occurred when using the maximum likelihood classifier with an averaged Kappa index of 0.95; followed by the spectral angle mapper (SAM) classifier with a Kappa index of 0.62; and, lastly, by the hybrid classifier showing lower performance with a Kappa index of 0.51. Recommendations for improvements of future work include the concurrent acquisition of LiDAR or RGB photo-derived digital surface models as well as detailed spectra collection for Alaska wetland cover to improve classification efforts.


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