Rice growth phase analysis in Pidie regency, Indonesia using multitemporal Sentinel-2 image data: a spectral angle mapper approach

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.

AVALIAÇÃO DA ACURÁCIA DE ALGORITMOS DE CLASSIFICAÇÃO DE IMAGENS ORBITAIS NA BAÍA DA BABITONGA, NORDESTE DE SANTA CATARINA

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.

LAND COVER CLASSIFICATION PERFORMANCE OF MULTISPECTRAL RTK UAVs

Unmanned air vehicle (UAV) became an alternative airborne remote sensing technique, due to providing very high resolution and low cost spatial data and short processing time. Particularly, optical UAVs are frequently utilized in various applications such as mapping, agriculture, and forestry. Especially for precise agriculture purposes, the UAVs were equipped with multispectral cameras which enables to classify land cover easily. In this study, the land cover classification potential of DJI Phantom IV Multispectral, one of the most preferred agricultural UAVs in the world, was investigated using spectral angle mapper, minimum distance and maximum likelihood pixel-based classification techniques and object-based classification. In the investigation, a part of Gebze Technical University (GTU) Northern Campus, includes a large variety of land cover classes, was selected as the study area. The UAV aerial photos were achieved from 70 m flight altitude and processed using structure from motion (SfM)-based image matching software Agisoft Metashape. The pixel-based and object-based land cover classification processes were completed with ENVI and eCognition software respectively. 16 independent land cover classes were classified and the results demonstrated that the accuracies are 73.46% in spectral angle mapper, 75.27% in minimum distance and 93.56% in maximum likelihood pixel-based classification techniques and 90.09% in nearest neighbour object-based classification.

An Application of the “Traffic Lights” Idea to Crop Control in Integrated Administration Control System

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.

MSNet: A Multi-Stream Fusion Network for Remote Sensing Spatiotemporal Fusion Based on Transformer and Convolution

Remote sensing products with high temporal and spatial resolution can be hardly obtained under the constrains of existing technology and cost. Therefore, the spatiotemporal fusion of remote sensing images has attracted considerable attention. Spatiotemporal fusion algorithms based on deep learning have gradually developed, but they also face some problems. For example, the amount of data affects the model’s ability to learn, and the robustness of the model is not high. The features extracted through the convolution operation alone are insufficient, and the complex fusion method also introduces noise. To solve these problems, we propose a multi-stream fusion network for remote sensing spatiotemporal fusion based on Transformer and convolution, called MSNet. We introduce the structure of the Transformer, which aims to learn the global temporal correlation of the image. At the same time, we also use a convolutional neural network to establish the relationship between input and output and to extract features. Finally, we adopt the fusion method of average weighting to avoid using complicated methods to introduce noise. To test the robustness of MSNet, we conducted experiments on three datasets and compared them with four representative spatiotemporal fusion algorithms to prove the superiority of MSNet (Spectral Angle Mapper (SAM) < 0.193 on the CIA dataset, erreur relative global adimensionnelle de synthese (ERGAS) < 1.687 on the LGC dataset, and root mean square error (RMSE) < 0.001 on the AHB dataset).

Mapeo de la cobertura vegetal en la subcuenca Quillcay (Ancash – Perú) con el clasificador de Árbol de decisiones.

La caracterización y delimitación de la cobertura vegetal existente en una determinada área geográfica es de vital importancia para una adecuada gestión de los recursos naturales. En tal sentido, esta investigación propone una metodología para realizar la delimitación de los tipos de cobertura vegetal de la subcuenca Quillcay. El área de estudio está localizada en los distritos de Huaraz e Independencia, provincia de Huaraz, Ancash; en la ladera occidental media de la Cordillera Blanca y Cuenca del Santa. La delimitación de la cobertura vegetal; inició con la identificación de los tipos de cobertura vegetal dominantes (pajonal andino, bosque, bofedal y matorral arbustivo); luego, se definieron las características geográficas (pendiente y altitud) para cada tipo de cobertura vegetal. Posteriormente, se generaron capas con el clasificador Spectral Angle Mapper (SAM) y el cálculo del Normalized Difference Moisture Index (NDMI). Finalmente, a través del clasificador de árbol de decisiones se puedo integrar todas las capas determinadas anteriormente, obteniendo de esta manera la clasificación supervisada con el uso información satelital Landsat 8 del año 2018 y DEM Alos Palsar. Los resultados indican que la aplicación del árbol de decisiones muestra una exactitud de la clasificación casi perfecta con un estadístico Kappa (K^) de 0,90. Consideramos que la metodología propuesta (árbol de decisiones) es ideal para delimitar la cobertura vegetal a escala subregional.