Tree Cover Estimation in Global Drylands from Space Using Deep Learning

Guirado, Emilio; Alcaraz‐Segura, Domingo; Cabello, Javier; Puertas-Ruíz, Sergio; Herrera, Francisco; Tabik, Siham

doi:10.3390/rs12030343

Cited by 20 publications

(13 citation statements)

References 83 publications

(115 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…With this method, we obtained a classification OA of 92.5%. This is a comparable result to the ones observed by different high-resolution classification studies done by Du et al [45] for crop area using Worldview-2 satellite data, De Alban et al [22] for mangrove forest cover and Guirado et al [46] for tree cover estimation, using various machine learning techniques. Furthermore, our study in some regions showed slightly higher accuracy compared to an earlier study done by Le Maire et al [23], using MODIS data.…”

Section: Discussionsupporting

confidence: 88%

Mapping of Eucalyptus in Natura 2000 Areas Using Sentinel 2 Imagery and Artificial Neural Networks

2020

View full text Add to dashboard Cite

Plantations of fast-growing Eucalyptus trees have become a common sight in the western Iberian peninsula where they are planted to exploit their economic potential. Negative side-effects of large scale plantations including the invasive behavior of Eucalyptus trees outside of regular plantations have become apparent. This study uses medium resolution, multi-spectral imagery of the Sentinel 2 satellites to map Eucalyptus across Portugal and parts of Spain with a focus on Natura 2000 areas inside Portugal, that are protected under the European birds and habitats directives. This method enables the detection of small incipient as well as mixed populations outside of regular plantations. Ground truth maps were compiled using field surveys as well as high resolution satellite imagery and were used to train Feedforward Neural Networks. These models predict Eucalyptus tree cover with a sensitivity of up to 75.7% as well as a specificity of up to 95.8%. The overall accuracy of the prediction is 92.5%. A qualitative assessment of Natura 2000 areas in Portugal has been performed and 15 areas have been found to be affected by Eucalyptus of which 9 are strongly affected. This study demonstrates the applicability of multi-spectral imagery for tree-species classification and invasive species control. It provides a probability-map of Eucalyptus tree cover for the western Iberian peninsula with 10 m spatial resolution and shows the need for monitoring of Eucalyptus in protected areas.

show abstract

Section: Discussionsupporting

confidence: 88%

Mapping of Eucalyptus in Natura 2000 Areas Using Sentinel 2 Imagery and Artificial Neural Networks

2020

View full text Add to dashboard Cite

show abstract

“…We tested the ability of human analysts and a custom DNN to detect the invasive miconia plant in visible-wavelength UAS imagery collected over complex canopy forest and found that the DNN outperformed the human analysts. While similar results have been reported in other image classification studies [91,99], we are not aware of any prior studies involving rigorous time-controlled human trials for detecting invasive species in high-resolution UAS imagery. The significance of optical contrast and SCR as factors for human recall agrees with previous studies [100], and recent work has further associated poor accuracies with low SCR for both humans and DNNs [101,102].…”

Section: Discussionsupporting

confidence: 86%

Comparing Interpretation of High-Resolution Aerial Imagery by Humans and Artificial Intelligence to Detect an Invasive Tree Species

et al. 2021

View full text Add to dashboard Cite

Timely, accurate maps of invasive plant species are critical for making appropriate management decisions to eliminate emerging target populations or contain infestations. High-resolution aerial imagery is routinely used to map, monitor, and detect invasive plant populations. While conventional image interpretation involving human analysts is straightforward, it can require high demands for time and resources to produce useful intelligence. We compared the performance of human analysts with a custom Retinanet-based deep convolutional neural network (DNN) for detecting individual miconia (Miconia calvescens DC) plants, using high-resolution unmanned aerial system (UAS) imagery collected over lowland tropical forests in Hawai’i. Human analysts (n = 38) examined imagery at three linear scrolling speeds (100, 200 and 300 px/s), achieving miconia detection recalls of 74 ± 3%, 60 ± 3%, and 50 ± 3%, respectively. The DNN achieved 83 ± 3% recall and completed the image analysis in 1% of the time of the fastest scrolling speed tested. Human analysts could discriminate large miconia leaf clusters better than isolated individual leaves, while the DNN detection efficacy was independent of leaf cluster size. Optically, the contrast in the red and green color channels and all three (i.e., red, green, and blue) signal to clutter ratios (SCR) were significant factors for human detection, while only the red channel contrast, and the red and green SCRs were significant factors for the DNN. A linear cost analysis estimated the operational use of a DNN to be more cost effective than human photo interpretation when the cumulative search area exceeds a minimum area. For invasive species like miconia, which can stochastically spread propagules across thousands of ha, the DNN provides a more efficient option for detecting incipient, immature miconia across large expanses of forested canopy. Increasing operational capacity for large-scale surveillance with a DNN-based image analysis workflow can provide more rapid comprehension of invasive plant abundance and distribution in forested watersheds and may become strategically vital to containing these invasions.

show abstract

“…Performing automatic monitoring of olive tree growth would be essential in these regions to effectively address these threats. Nowadays, the application of machine learning methods on very high spatial resolution satellite and aerial images opens the possibility of detecting isolated shrubs and trees at regional scale [ 6 , 7 , 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Olive Tree Biovolume from UAV Multi-Resolution Image Segmentation with Mask R-CNN

Safonova

Guirado

Maglinets

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

Olive tree growing is an important economic activity in many countries, mostly in the Mediterranean Basin, Argentina, Chile, Australia, and California. Although recent intensification techniques organize olive groves in hedgerows, most olive groves are rainfed and the trees are scattered (as in Spain and Italy, which account for 50% of the world’s olive oil production). Accurate measurement of trees biovolume is a first step to monitor their performance in olive production and health. In this work, we use one of the most accurate deep learning instance segmentation methods (Mask R-CNN) and unmanned aerial vehicles (UAV) images for olive tree crown and shadow segmentation (OTCS) to further estimate the biovolume of individual trees. We evaluated our approach on images with different spectral bands (red, green, blue, and near infrared) and vegetation indices (normalized difference vegetation index—NDVI—and green normalized difference vegetation index—GNDVI). The performance of red-green-blue (RGB) images were assessed at two spatial resolutions 3 cm/pixel and 13 cm/pixel, while NDVI and GNDV images were only at 13 cm/pixel. All trained Mask R-CNN-based models showed high performance in the tree crown segmentation, particularly when using the fusion of all dataset in GNDVI and NDVI (F1-measure from 95% to 98%). The comparison in a subset of trees of our estimated biovolume with ground truth measurements showed an average accuracy of 82%. Our results support the use of NDVI and GNDVI spectral indices for the accurate estimation of the biovolume of scattered trees, such as olive trees, in UAV images.

show abstract

Tree Cover Estimation in Global Drylands from Space Using Deep Learning

Cited by 20 publications

References 83 publications

Mapping of Eucalyptus in Natura 2000 Areas Using Sentinel 2 Imagery and Artificial Neural Networks

Mapping of Eucalyptus in Natura 2000 Areas Using Sentinel 2 Imagery and Artificial Neural Networks

Comparing Interpretation of High-Resolution Aerial Imagery by Humans and Artificial Intelligence to Detect an Invasive Tree Species

Olive Tree Biovolume from UAV Multi-Resolution Image Segmentation with Mask R-CNN

Contact Info

Product

Resources

About