scholarly journals Analysis of Combining SAR and Optical Optimal Parameters to Classify Typhoon-Invasion Lodged Rice: A Case Study Using the Random Forest Method

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7346
Author(s):  
Jinning Wang ◽  
Kun Li ◽  
Yun Shao ◽  
Fengli Zhang ◽  
Zhiyong Wang ◽  
...  
Keyword(s):  
Random Forest ◽  
Large Scale ◽  
Remote Sensing Data ◽  
High Sensitivity ◽  
Spectral Indices ◽  
Optimal Parameters ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Typhoon Event ◽  
Sentinel 2

Lodging, a commonly occurring rice crop disaster, seriously reduces rice quality and production. Monitoring rice lodging after a typhoon event is essential for evaluating yield loss and formulating suitable remedial policies. The availability of Sentinel-1 and Sentinel-2 open-access remote sensing data provides large-scale information with a short revisit time to be freely accessed. Data from these sources have been previously shown to identify lodged crops. In this study, therefore, Sentinel-1 and Sentinel-2 data after a typhoon event were combined to enable monitoring of lodging rice to be quickly undertaken. In this context, the sensitivity of synthetic aperture radar (SAR) features (SF) and spectral indices (SI) extracted from Sentinel-1 and Sentinel-2 to lodged rice were analyzed, and a model was constructed for selecting optimal sensitive parameters for lodging rice (OSPL). OSPL has high sensitivity to lodged rice and strong ability to distinguish lodged rice from healthy rice. After screening, Band 11 (SWIR-1) and Band 12 (SWIR-2) were identified as optimal spectral indices (OSI), and VV, VV + VH and Shannon Entropy were optimal SAR features (OSF). Three classification results of lodging rice were acquired using the Random Forest classification (RFC) method based on OSI, OSF and integrated OSI–OSF stack images, respectively. Results indicate that an overall level of accuracy of 91.29% was achieved with the combination of SAR and optical optimal parameters. The result was 2.91% and 6.05% better than solely using optical or SAR processes, respectively.

scholarly journals Estimation of Wheat Area using Sentinel-1 and Sentinel-2 Datasets (A Comparative Analysis)

2019 ◽  
Author(s):  
Ayesha Behzad ◽  
Muneeb Aamir ◽  
Syed Ahmed Raza ◽  
Ansab Qaiser ◽  
Syeda Yuman Fatima ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Random Forest ◽  
Water Body ◽  
Supervised Classification ◽  
Wheat Crop ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Wheat Cultivation ◽  
Erdas Imagine ◽  
Sentinel 2

Wheat is the basic staple food, largely grown, widely used and highly demanded. It is used in multiple food products which are served as fundamental constituent to human body. Various regional economies are partially or fully dependent upon wheat production. Estimation of wheat area is essential to predict its contribution in regional economy. This study presents a comparative analysis of optical and active imagery for estimation of area under wheat cultivation. Sentinel-1 data was downloaded in Ground Range Detection (GRD) format and applied the Random Forest Classification using Sentinel Application Platform (SNAP) tools. We obtained a Sentinel-2 image for the month of March and applied supervised classification in Erdas Imagine 14. The random forest classification results of Sentinel-1 show that the total area under investigation was 1089km2 which was further subdivided in three classes including wheat (551km2), built-up (450 km2) and the water body (89 km2). Supervised classification results of Sentinel-2 data show that the area under wheat crop was 510 km2, however the built-up and waterbody were 477 km2, 102 km2 respectively. The integrated map of Sentinel-1 and Sentinel-2 show that the area under wheat was 531 km2 and the other features including water body and the built-up area were 95 km2 and 463 km2 respectively. We applied a Kappa coefficient to Sentinel-2, Sentinel-1 and Integrated Maps and found an accuracy of 71%, 78% and 85% respectively. We found that remotely sensed algorithms of classifications are reliable for future predictions.

scholarly journals Application of random forest classification and remotely sensed data in geological mapping on the Jebel Meloussi area (Tunisia)

2021 ◽  
Vol 14 (21) ◽  
Author(s):  
Gáspár Albert ◽  
Seif Ammar
Keyword(s):  
Random Forest ◽  
Remotely Sensed ◽  
Geological Mapping ◽  
Vegetation Coverage ◽  
Remotely Sensed Data ◽  
Random Forest Classification ◽  
Radar Images ◽  
Forest Classification ◽  
Geological Map ◽  
Sentinel 2

Abstract Remotely sensed data such as satellite photos and radar images can be used to produce geological maps on arid regions, where the vegetation coverage does not have a significant effect. In central Tunisia, the Jebel Meloussi area has unique geological features and characteristic morphology (i.e. flat areas with dune fields in contrast with hills of folded and eroded stratigraphic sequences), which makes it an ideal area for testing new methods of automatic terrain classification. For this, data from the Sentinel 2 satellite sensor and the SRTM-based MERIT DEM (digital elevation model) were used in the present study. Using R scripts and the random forest classification method, modelling was performed on four lithological variables—derived from the different bands of the Sentinel 2 images—and two morphometric parameters for the area of the 1:50,000 geological map sheet no. 103. The four lithological variables were chosen to highlight the iron-bearing minerals since the spectral parameters of the Sentinel 2 sensors are especially useful for this purpose. The training areas of the classification were selected on the geological map. The results of the modelling identified Eocene and Cretaceous evaporite-bearing sedimentary series (such as the Jebs and the Bouhedma Formations) with the highest producer accuracy (> 60% of the predicted pixels match with the map). The pyritic argillites of the Sidi Khalif Formation were also recognized with the same accuracy, and the Quaternary sebhkas and dunes were also well predicted. The study concludes that the classification-based geological map is useful for field geologist prior to field surveys.

scholarly journals Identifying European Old-Growth Forests using Remote Sensing: A Study in the Ukrainian Carpathians

Forests ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 127 ◽  
Author(s):  
Benedict D. Spracklen ◽  
Dominick V. Spracklen
Keyword(s):  
Remote Sensing ◽  
Random Forest ◽  
Norway Spruce ◽  
Satellite Images ◽  
Textural Features ◽  
Old Growth ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Old Growth Forests ◽  
Sentinel 2

Old-growth forests are an important, rare and endangered habitat in Europe. The ability to identify old-growth forests through remote sensing would be helpful for both conservation and forest management. We used data on beech, Norway spruce and mountain pine old-growth forests in the Ukrainian Carpathians to test whether Sentinel-2 satellite images could be used to correctly identify these forests. We used summer and autumn 2017 Sentinel-2 satellite images comprising 10 and 20 m resolution bands to create 6 vegetation indices and 9 textural features. We used a Random Forest classification model to discriminate between dominant tree species within old-growth forests and between old-growth and other forest types. Beech and Norway spruce were identified with an overall accuracy of around 90%, with a lower performance for mountain pine (70%) and mixed forest (40%). Old-growth forests were identified with an overall classification accuracy of 85%. Adding textural features, band standard deviations and elevation data improved accuracies by 3.3%, 2.1% and 1.8% respectively, while using combined summer and autumn images increased accuracy by 1.2%. We conclude that Random Forest classification combined with Sentinel-2 images can provide an effective option for identifying old-growth forests in Europe.

scholarly journals Detection of Longhorned Borer Attack and Assessment in Eucalyptus Plantations Using UAV Imagery

2020 ◽  
Vol 12 (19) ◽  
pp. 3153
Author(s):  
André Duarte ◽  
Luis Acevedo-Muñoz ◽  
Catarina I. Gonçalves ◽  
Luís Mota ◽  
Alexandre Sarmento ◽  
...  
Keyword(s):  
Random Forest ◽  
Large Scale ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Forest Density ◽  
Random Forest Classification ◽  
Otsu Thresholding ◽  
Forest Classification ◽  
Density Maps ◽  
Kappa Value

Eucalyptus Longhorned Borers (ELB) are some of the most destructive pests in regions with Mediterranean climate. Low rainfall and extended dry summers cause stress in eucalyptus trees and facilitate ELB infestation. Due to the difficulty of monitoring the stands by traditional methods, remote sensing arises as an invaluable tool. The main goal of this study was to demonstrate the accuracy of unmanned aerial vehicle (UAV) multispectral imagery for detection and quantification of ELB damages in eucalyptus stands. To detect spatial damage, Otsu thresholding analysis was conducted with five imagery-derived vegetation indices (VIs) and classification accuracy was assessed. Treetops were calculated using the local maxima filter of a sliding window algorithm. Subsequently, large-scale mean-shift segmentation was performed to extract the crowns, and these were classified with random forest (RF). Forest density maps were produced with data obtained from RF classification. The normalized difference vegetation index (NDVI) presented the highest overall accuracy at 98.2% and 0.96 Kappa value. Random forest classification resulted in 98.5% accuracy and 0.94 Kappa value. The Otsu thresholding and random forest classification can be used by forest managers to assess the infestation. The aggregation of data offered by forest density maps can be a simple tool for supporting pest management.

scholarly journals Análise de acurácia para o mapeamento de áreas queimadas utilizando uma cena VIIRS 1Km e classificação por Random Forest

2021 ◽  
Vol 14 (6) ◽  
pp. 3225
Author(s):  
Juarez Antonio da Silva Júnior ◽  
Ubiratan Joaquim da Silva Júnior ◽  
Admilson Da Penha Pacheco
Keyword(s):  
Random Forest ◽  
Spatial Resolution ◽  
Forest Fires ◽  
Remote Sensing Data ◽  
Fire Detection ◽  
Time Interval ◽  
Validation Data ◽  
Random Forest Classification ◽  
Burned Areas ◽  
Forest Classification

A disponibilidade gratuita de dados de sensoriamento remoto em áreas atingidas por incêndios florestais em escala global oferece a oportunidade de geração sistemática de produtos terrestres de média resolução espacial, porém as conhecidas limitações de precisão é objeto de estudo em todo o mundo. Este artigo tem como objetivo analisar a acurácia da detecção de áreas queimadas utilizando o classificador Random Forest (RF) por meio de uma cena do sensor Radiômetro de Imagem Infravermelho Visível (VIIRS) (1Km) em quatro pontos da savana brasileira. Os resultados foram validados através dos produtos de referência espacial de áreas queimadas: Aq30m, Fire_cci e MCD64A1 por meio de uma abordagem estratificada possibilitando a amostragem dos dados no espaço e tempo. Os modelos de RF avaliados com seus parâmetros de entrada, em que, incluiu-se 400 árvores e um atributo, fornecendo uma taxa de erro abaixo de 4%. Os resultados mostraram que o mapeamento validado com o produto Aq30m apresentou importantes estimativas de Coeficiente de Sorensen-Dice enquanto a validação realizada entre os modelos globais, o MCD64A1 mostrou-se com maior exatidão (>50%) principalmente em feições de áreas queimadas de grandes proporções (> 200Km²). Em particular, a análise sugere que a validação de produtos de área queimada sempre deve estar ligada ao tempo mínimo da data dos dados de validação e o tamanho da área atingida pelo fogo. Os resultados mostram que esta abordagem é muito útil para ser usado para determinar áreas de floresta queimada.      Accuracy analysis for mapping burnt areas using a 1Km VIIRS scene and Random Forest classification A B S T R A C TThe availability of remote sensing data with medium spatial resolution has offered several mapping possibilities for areas affected by forest fires on the Earth's surface. In this context, the analysis of sensor spatial accuracy limitations has been the subject of global research. The objective of this study was to analyze the mapping accuracy of the VIIRS sensor on board the NOAA satellite, using the Random Forest (RF) classifier for the detection of burned areas, in four points of the Chapada dos Veadeiros National Park - Goiás, inserted in the Brazilian savanna. The methodology consisted in validating the classification using the Sorensen-Dice coefficient (SD) in a stratified approach, using as reference the products: Aq30m, Fire_cci and MCD64A1. As a result, the RF models, included 400 trees and one attribute, with an error of less than 4%. Among the global models, the MCD64A1 presented a significant accuracy, greater than 50%, especially in features of burned areas greater than 200Km². Thus, the data suggest that the quality of accuracy of the validation process of mapping products for burned areas is associated with the minimum time interval of availability of validation data and the size of the area affected by fire. Based on this, the results show effectiveness in using the RF algorithm on medium spatial resolution images for fire detection in seasonally dry forests, such as the Cerrado.Keywords: Cerrado, fires, Random Forest.

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