Uncovering Ecological Patterns with Convolutional Neural Networks

Brodrick, Philip G.; Davies, Andrew B.; Asner, Gregory P.

doi:10.1016/j.tree.2019.03.006

Cited by 128 publications

(95 citation statements)

References 58 publications

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“…Further improvements in machine learning methodology are likely to produce more robust and management‐ready tools. For example, convolutional neural networks (Brodrick, Davies, & Asner, ) may be able to leverage the strong spatial structure in ploidy level (as we identified via our minimum distance analysis) to make cleaner predictions of ploidy level boundaries.…”

Section: Discussionmentioning

confidence: 99%

Remote sensing of ploidy level in quaking aspen (Populus tremuloides Michx.)

et al. 2019

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1. Ploidy level in plants may influence ecological functioning, demography and response to climate change. However, measuring ploidy level typically requires intensive cell or molecular methods. 2. We map ploidy level variation in quaking aspen, a dominant North American tree species that can be diploid or triploid and that grows in spatially extensive clones.We identify the predictors and spatial scale of ploidy level variation using a combination of genetic and ground-based and airborne remote sensing methods.3. We show that ground-based leaf spectra and airborne canopy spectra can both classify aspen by ploidy level with a precision-recall harmonic mean of 0.75-0.95 and Cohen's kappa of c. 0.6-0.9. Ground-based bark spectra cannot classify ploidy level better than chance. We also found that diploids are more common on higher elevation and steeper sites in a network of forest plots in Colorado, and that ploidy level distribution varies at subkilometer spatial scales. 4. Synthesis. Our proof-of-concept study shows that remote sensing of ploidy level could become feasible in this tree species. Mapping ploidy level across landscapes could provide insights into the genetic basis of species' responses to climate change. K E Y W O R D S adaptation, ploidy level, polyploidy, quaking aspen, reflectance, remote sensing, spectrometry, UAS S U PP O RTI N G I N FO R M ATI O N Additional supporting information may be found online in the Supporting Information section at the end of the article. How to cite this article: Blonder B, Graae BJ, Greer B, et al. Remote sensing of ploidy level in quaking aspen (Populus tremuloides Michx.). J Ecol. 2020;108:175-188. https ://doi.

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Section: Discussionmentioning

confidence: 99%

Remote sensing of ploidy level in quaking aspen (Populus tremuloides Michx.)

et al. 2019

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“…There are ongoing community-based efforts to monitor and mitigate the impacts of gold mining, including land restoration and reforestation of degraded forests (Sanguinetti 2018). However, reforesting abandoned gold mines with native species is challenging because of poor soil quality and mercury contamination, slow tree growth rates, but acceptable survivorship of the Deep learning approaches are becoming essential tools for large-scale spatial analyses (Brodrick et al 2019). To our knowledge, the use of deep learning regression workflows to estimate ACD in tropical forests is rather limited (Asner et al 2018).…”

Section: Discussionmentioning

confidence: 99%

Aboveground carbon emissions from gold mining in the Peruvian Amazon

Csillik

Asner

2020

Environ. Res. Lett.

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In the Peruvian Amazon, high biodiversity tropical forest is underlain by gold-enriched subsurface alluvium deposited from the Andes, which has generated a clash between short-term earnings for miners and long-term environmental damage. Tropical forests sequester important amounts of carbon, but deforestation and forest degradation continue to spread in Madre de Dios, releasing carbon to the atmosphere. Updated spatially explicit quantification of aboveground carbon emissions caused by gold mining is needed to further motivate conservation efforts and to understand the effects of illegal mining on greenhouse gases. We used satellite remote sensing, airborne LiDAR, and deep learning models to create high-resolution, spatially explicit estimates of aboveground carbon stocks and emissions from gold mining in 2017 and 2018. For an area of ∼750 000 ha, we found high variations in aboveground carbon density (ACD) with mean ACD of 84.6 (±36.4 standard deviation) Mg C ha −1 and 83.9 (±36.0) Mg C ha −1 for 2017 and 2018, respectively. An alarming 1.12 Tg C of emissions occurred in a single year affecting 23,613 hectares, including in protected zones and their ecological buffers. Our methods and findings are preparatory steps for the creation of an automated, high-resolution forest carbon emission monitoring system that will track near real-time changes and will support actions to reduce the environmental impacts of gold mining and other destructive forest activities.

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“…Next, we briefly describe the network and refer to Brodrick et al (2019) for a thorough introduction to convolutional neural networks and to Diakogiannis et al (2019) for more details about the ResUNet-a framework. The UNet backbone architecture, also known as encoder-decoder, consists of two parts: the contraction part or encoder, and the symmetric expanding path or decoder.…”

Section: Methodsmentioning

confidence: 99%

Extracting field boundaries from satellite imagery with a convolutional neural network to enable smart farming at scale

Waldner¹,

Diakogiannis²

2020

Preprint

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Applications of digital agricultural services often require either farmers or their advisers to provide digital records of their field boundaries. Automatic extraction of field boundaries from satellite imagery would reduce the reliance on manual input of these records which is time consuming and error-prone, and would underpin the provision of remote products and services. The lack of current field boundary data sets seems to indicate low uptake of existing methods, presumably because of expensive image preprocessing requirements and local, often arbitrary, tuning. In this paper, we address the problem of field boundary extraction from satellite images as a multi-task semantic segmentation problem. We used ResUNet-a, a deep convolutional neural network with a fully connected UNet backbone that features dilated convolutions and conditioned inference, to assign three labels to each pixel: 1) the probability of belonging to a field; 2) the probability of being part of a boundary; and 3) the distance to the closest boundary. These labels can then be combined to obtain closed field boundaries. Using a single composite image from Sentinel-2, the model was highly accurate in mapping field extent, field boundaries, and, consequently, individual fields. Replacing the monthly composite with a single-date image close to the compositing period only marginally decreased accuracy. We then showed in a series of experiments that our model generalised well across resolutions, sensors, space and time without recalibration. Building consensus by averaging model predictions from at least four images acquired across the season is the key to coping with the temporal variations of accuracy. By minimising image preprocessing requirements and replacing local arbitrary decisions by datadriven ones, our approach is expected to facilitate the extraction of individual crop fields at scale.

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Uncovering Ecological Patterns with Convolutional Neural Networks

Cited by 128 publications

References 58 publications

Remote sensing of ploidy level in quaking aspen (Populus tremuloides Michx.)

Remote sensing of ploidy level in quaking aspen (Populus tremuloides Michx.)

Aboveground carbon emissions from gold mining in the Peruvian Amazon

Extracting field boundaries from satellite imagery with a convolutional neural network to enable smart farming at scale

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