Does Sentinel-1A Backscatter Capture the Spatial Variability in Canopy Gaps of Tropical Agroforests? A Proof-of-Concept in Cocoa Landscapes in Cameroon

Numbisi, Frederick N.; Coillie, Frieke Van

doi:10.3390/rs12244163

Cited by 6 publications

(5 citation statements)

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“…The bootstrap technique was used to ensure reproducibility and to make sure that the classifiers are evaluated under the same conditions. Bootstrap is a random resampling technique with replacement from the primary dataset [39,62]. This technique makes it possible to estimate the empirical distribution of statistics [39,63].…”

Section: Classification Setupmentioning

confidence: 99%

“…After extracting attributes with the CNNs, four different data sets are created, df-16vh, df-16vv, df-19vh, and df-19vv, which are the results of the combinations of the two CNNs, VGG-16/VGG-19, and the VH/VV polarizations.The bootstrap technique was used to ensure reproducibility and to make sure that the classifiers are evaluated under the same conditions. Bootstrap is a random resampling technique with replacement from the primary dataset[39,62]. This technique makes it possible to estimate the empirical distribution of statistics[39,63].…”

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confidence: 99%

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Assessment of Machine Learning Techniques for Oil Rig Classification in C-Band SAR Images

et al. 2022

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This article aims at performing maritime target classification in SAR images using machine learning (ML) and deep learning (DL) techniques. In particular, the targets of interest are oil platforms and ships located in the Campos Basin, Brazil. Two convolutional neural networks (CNNs), VGG-16 and VGG-19, were used for attribute extraction. The logistic regression (LR), random forest (RF), support vector machine (SVM), k-nearest neighbours (kNN), decision tree (DT), naive Bayes (NB), neural networks (NET), and AdaBoost (ADBST) schemes were considered for classification. The target classification methods were evaluated using polarimetric images obtained from the C-band synthetic aperture radar (SAR) system Sentinel-1. Classifiers are assessed by the accuracy indicator. The LR, SVM, NET, and stacking results indicate better performance, with accuracy ranging from 84.1% to 85.5%. The Kruskal–Wallis test shows a significant difference with the tested classifier, indicating that some classifiers present different accuracy results. The optimizations provide results with more significant accuracy gains, making them competitive with those shown in the literature. There is no exact combination of methods for SAR image classification that will always guarantee the best accuracy. The optimizations performed in this article were for the specific data set of the Campos Basin, and results may change depending on the data set format and the number of images.

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Section: Classification Setupmentioning

confidence: 99%

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Assessment of Machine Learning Techniques for Oil Rig Classification in C-Band SAR Images

et al. 2022

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“…Multitemporal C-Band data can enhance wetland classification schemes, by accounting flood changes and vegetation phenology (Salvia et al, 2008;Vanama et al, 2020). However, characterizing vegetation types in high-vegetated wetlands using C-band data has been challenging due to the modest potential of this band to penetrate forest canopies (Sivasankar et al, 2018;Meyer, 2019;Numbisi and Coillie, 2020;Morandeira et al, 2021). In areas where the vegetation is sufficiently dense, backscatter mean values may be similar between different vegetation types.…”

Section: Introductionmentioning

confidence: 99%

Cover classifications in wetlands using Sentinel-1 data (Band C): a case study in the Parana river delta, Argentina

Rajngewerc

Grimson

Bali³

et al. 2022

Rev. Teledetec.

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With the launch of the Sentinel-1 mission, for the first time, multitemporal and dual-polarization C-band SAR data with a short revisit time is freely available. How can we use this data to generate accurate vegetation cover maps on a local scale? Our main objective was to assess the use of multitemporal C-Band Sentinel-1 data to generate wetland vegetation maps. We considered a portion of the Lower Delta of the Paraná River wetland (Argentina). Seventy-four images were acquired and 90 datasets were created with them, each one addressing a combination of seasons (spring, autumn, winter, summer, complete set), polarization (VV, HV, both), and texture measures (included or not). For each dataset, a Random Forest classifier was trained. Then, the kappa index values (κ) obtained by the 90 classifications made were compared. Considering the datasets formed by the intensity values, for the winter dates the achieved kappa index values (κ) were higher than 0.8, while all summer datasets achieved κ up to 0.76. Including feature textures based on the GLCM showed improvements in the classifications: for the summer datasets, the κ improvements were between 9% and 22% and for winter datasets improvements were up to 15%. Our results suggest that for the analyzed context, winter is the most informative season. Moreover, for dates associated with high biomass, the textures provide complementary information.

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“…The noninvasive evaluation of crop quality by means of multispectral imaging facilitates reform steps in agricultural management [7][8][9][10]. In the discussed field, remote sensing generally offers two functional options, namely, satellite imagery [11][12][13][14][15] and unmanned aerial vehicle (UAV) photogrammetry [16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Using UAV-Based Photogrammetry to Obtain Correlation between the Vegetation Indices and Chemical Analysis of Agricultural Crops

et al. 2021

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The optimum corn harvest time differs between individual harvest scenarios, depending on the intended use of the crop and on the technical equipment of the actual farm. It is therefore economically significant to specify the period as precisely as possible. The harvest maturity of silage corn is currently determined from the targeted sampling of plants cultivated over large areas. In this context, the paper presents an alternative, more detail-oriented approach for estimating the correct harvest time; the method focuses on the relationship between the ripeness data obtained via photogrammetry and the parameters produced by the chemical analysis of corn. The relevant imaging methodology utilizing a spectral camera-equipped unmanned aerial vehicle (UAV) allows the user to acquire the spectral reflectance values and to compute the vegetation indices. Furthermore, the authors discuss the statistical data analysis centered on both the nutritional values found in the laboratory corn samples and on the information obtained from the multispectral images. This discussion is associated with a detailed insight into the computation of correlation coefficients. Statistically significant linear relationships between the vegetation indices, the normalized difference red edge index (NDRE) and the normalized difference vegetation index (NDVI) in particular, and nutritional values such as dry matter, starch, and crude protein are evaluated to indicate different aspects of and paths toward predicting the optimum harvest time. The results are discussed in terms of the actual limitations of the method, the benefits for agricultural practice, and planned research.

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Does Sentinel-1A Backscatter Capture the Spatial Variability in Canopy Gaps of Tropical Agroforests? A Proof-of-Concept in Cocoa Landscapes in Cameroon

Cited by 6 publications

References 81 publications

Assessment of Machine Learning Techniques for Oil Rig Classification in C-Band SAR Images

Assessment of Machine Learning Techniques for Oil Rig Classification in C-Band SAR Images

Cover classifications in wetlands using Sentinel-1 data (Band C): a case study in the Parana river delta, Argentina

Using UAV-Based Photogrammetry to Obtain Correlation between the Vegetation Indices and Chemical Analysis of Agricultural Crops

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