Comparing the accuracy of MODIS data products for vegetation detection between two environmentally dissimilar ecoregions: the Chocó-Darien of South America and the Great Basin of North America

Fagua, J. Camilo; Ramsey, R. Douglas

doi:10.1080/15481603.2019.1611024

Cited by 5 publications

(3 citation statements)

References 51 publications

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“…(2) The use of annual metrics reduced the errors produced when data of specific dates are used as we showed in our analysis of annual curves (Figure 3), where lower and upper CHs had similar band and vegetation indices values during some parts of the year while X-means were different. Other work has also demonstrated improved vegetation characterization when annual metrics of multispectral sensors are used, supporting our results [36,64,81,82].…”

Section: Improving Ch Mappingsupporting

confidence: 90%

Integrating LiDAR, Multispectral and SAR Data to Estimate and Map Canopy Height in Tropical Forests

et al. 2019

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Developing accurate methods to map vegetation structure in tropical forests is essential to protect their biodiversity and improve their carbon stock estimation. We integrated LIDAR (Light Detection and Ranging), multispectral and SAR (Synthetic Aperture Radar) data to improve the prediction and mapping of canopy height (CH) at high spatial resolution (30 m) in tropical forests in South America. We modeled and mapped CH estimated from aircraft LiDAR surveys as a ground reference, using annual metrics derived from multispectral and SAR satellite imagery in a dry forest, a moist forest, and a rainforest of tropical South America. We examined the effect of the three forest types, five regression algorithms, and three predictor groups on the modelling and mapping of CH. Our CH models reached errors ranging from 1.2–3.4 m in the dry forest and 5.1–7.4 m in the rainforest and explained variances from 94–60% in the dry forest and 58–12% in the rainforest. Our best models show higher accuracies than previous works in tropical forests. The average accuracy of the five regression algorithms decreased from dry forests (2.6 m +/− 0.7) to moist (5.7 m +/− 0.4) and rainforests (6.6 m +/− 0.7). Random Forest regressions produced the most accurate models in the three forest types (1.2 m +/− 0.05 in the dry, 4.9 m +/− 0.14 in the moist, and 5.5 m +/− 0.3 the rainforest). Model performance varied considerably across the three predictor groups. Our results are useful for CH spatial prediction when GEDI (Global Ecosystem Dynamics Investigation lidar) data become available.

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Section: Improving Ch Mappingsupporting

confidence: 90%

Integrating LiDAR, Multispectral and SAR Data to Estimate and Map Canopy Height in Tropical Forests

et al. 2019

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“…Land cover classification using remote sensing data is the task of classifying pixels or objects whose spectral characteristics are similar and allocating them to the designated classification classes, such as forests, grasslands, wetlands, barren lands, cultivated lands, and built-up areas. Various techniques have been applied to land cover classification, including traditional statistical algorithms and recent machine learning approaches, such as random forest and support vector machines [6][7][8][9][10][11].Deep learning is a subset of machine learning that yields high-level abstractions by compositing multiple non-linear transformations [12]. Among deep learning algorithms, convolutional neural networks (CNNs) have gained popularity in computer vision and remote sensing fields, especially for image classification [13][14][15][16][17].…”

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confidence: 99%

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confidence: 99%

Different Spectral Domain Transformation for Land Cover Classification Using Convolutional Neural Networks with Multi-Temporal Satellite Imagery

et al. 2020

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This study compares some different types of spectral domain transformations for convolutional neural network (CNN)-based land cover classification. A novel approach was proposed, which transforms one-dimensional (1-D) spectral vectors into two-dimensional (2-D) features: Polygon graph images (CNN-Polygon) and 2-D matrices (CNN-Matrix). The motivations of this study are that (1) the shape of the converted 2-D images is more intuitive for human eyes to interpret when compared to 1-D spectral input; and (2) CNNs are highly specialized and may be able to similarly utilize this information for land cover classification. Four seasonal Landsat 8 images over three study areas—Lake Tapps, Washington, Concord, New Hampshire, USA, and Gwangju, Korea—were used to evaluate the proposed approach for nine land cover classes compared to several other methods: Random forest (RF), support vector machine (SVM), 1-D CNN, and patch-based CNN. Oversampling and undersampling approaches were conducted to examine the effect of the sample size on the model performance. The CNN-Polygon had better performance than the other methods, with overall accuracies of about 93%–95 % for both Concord and Lake Tapps and 80%–84% for Gwangju. The CNN-Polygon particularly performed well when the training sample size was small, less than 200 per class, while the CNN-Matrix resulted in similar or higher performance as sample sizes became larger. The contributing input variables to the models were carefully analyzed through sensitivity analysis based on occlusion maps and accuracy decreases. Our result showed that a more visually intuitive representation of input features for CNN-based classification models yielded higher performance, especially when the training sample size was small. This implies that the proposed graph-based CNNs would be useful for land cover classification where reference data are limited.

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The spatiotemporal scale effect on vegetation interannual trend estimates based on satellite products over Qinghai-Tibet Plateau

Wang

et al. 2023

J. Geogr. Sci.

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Comparing the accuracy of MODIS data products for vegetation detection between two environmentally dissimilar ecoregions: the Chocó-Darien of South America and the Great Basin of North America

Cited by 5 publications

References 51 publications

Integrating LiDAR, Multispectral and SAR Data to Estimate and Map Canopy Height in Tropical Forests

Integrating LiDAR, Multispectral and SAR Data to Estimate and Map Canopy Height in Tropical Forests

Different Spectral Domain Transformation for Land Cover Classification Using Convolutional Neural Networks with Multi-Temporal Satellite Imagery

The spatiotemporal scale effect on vegetation interannual trend estimates based on satellite products over Qinghai-Tibet Plateau

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