Forest Monitoring in Guatemala Using Satellite Imagery and Deep Learning

Wyniawskyj, Nina Sofia; Napiorkowska, Milena; Petit, David; Podder, Pritimoy; Marti, Paula

doi:10.1109/igarss.2019.8899782

Cited by 11 publications

(6 citation statements)

References 2 publications

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“…Landsat-8, Terra, and Sentinel-2 are the three satellites that are commonly used to develop datasets for forest monitoring systems, as used in Krasovskii et al [12], Wyniawskyj et al [13], and Torres et al [14]. The spatial resolutions of these satellites are listed in Table 1, in which they have been successfully used for various other applications, including forest monitoring systems.…”

Section: Satellite Technology In Forest Monitoring Systemsmentioning

confidence: 99%

Forest Segmentation with Spatial Pyramid Pooling Modules: A Surveillance System Based on Satellite Images

Zulkifley

Abdani

et al. 2023

Forests

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The global deforestation rate continues to worsen each year, and will eventually lead to various negative consequences for humans and the environment. It is essential to develop an effective forest monitoring system to detect any changes in forest areas, in particular, by monitoring the progress of forest conservation efforts. In general, changes in forest status are difficult to annotate manually, whereby the boundaries can be small in size or hard to discern, especially in areas that are bordering residential areas. The previously implemented forest monitoring systems were ineffective due to their use of low-resolution satellite images and the inefficiency of drone-based data that offer a limited field of view. Most government agencies also still rely on manual annotation, which makes the monitoring process time-consuming, tedious, and expensive. Therefore, the goal of this study is to overcome these issues by developing a forest monitoring system that relies on a robust deep semantic segmentation network that is capable of discerning forest boundaries automatically, so that any changes over the years can be tracked. The backbone of this system is based on satellite imaging supplied to a modified U-Net deep architecture to incorporate multi-scale modules to deliver the semantic segmentation output. A dataset of 6048 Landsat-8 satellite sub-images that were taken from eight land parcels of forest areas was collected and annotated, and then further divided into training and testing datasets. The novelty of this system is the optimal integration of the spatial pyramid pooling (SPP) mechanism into the base model, which allows the model to effectively segment forest areas regardless of their varying sizes, patterns, and colors. To investigate the impact of SPP on the forest segmentation system, a set of experiments was conducted by integrating several variants of SPP ranging from two to four parallel paths with different combinations of pooling kernel size, placed at the bottleneck layer of the U-Net model. The results demonstrated the effectiveness of the SPP module in improving the performance of the forest segmentation system by 2.57%, 6.74%, and 7.75% in accuracy (acc), intersection over union (IoU), and F1-score (F1score), respectively. The best SPP variant consists of four parallel paths with a combination of pooling kernel sizes of 2×2, 4×4, 6×6, and 8×8 pixels that produced the highest acc, IoU, and F1score of 86.71%, 75.59%, and 82.88%, respectively. As a result, the multi-scale module improved the proposed forest segmentation system, making it a highly useful system for government and private agencies in tracking any changes in forest areas.

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Section: Satellite Technology In Forest Monitoring Systemsmentioning

confidence: 99%

Forest Segmentation with Spatial Pyramid Pooling Modules: A Surveillance System Based on Satellite Images

Zulkifley

Abdani

et al. 2023

Forests

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“…For example, near real‐time on‐board processing by SmallSat computing platforms have been previously used for ship detection (Yao et al., 2019). Such processing could facilitate more rapid responses to illegal fishing events (Greidanus et al., 2017; Kurekin et al., 2019) or illegal logging in protected areas (Lynch et al., 2013; Wyniawskyj et al., 2019).…”

Section: Opportunities Associated With Smallsatsmentioning

confidence: 99%

SmallSats: a new technological frontier in ecology and conservation?

Curnick

Davies²,

Duncan

et al. 2021

Remote Sens Ecol Conserv

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We are in the midst of a revolution in satellite technology, with the rapid development and advancement of small satellites (or SmallSats, i.e., satellites <180 kg). Here, we review the opportunities and challenges that such technology might afford in the field of conservation and ecology. SmallSat constellations may yield higher resolutions than those that are currently available to scientists and practitioners, increasing opportunities to improve environmentalmonitoring and animal-tracking capabilities. They may cut access costs to end users, by reducing operational costs and bringing increased competition to the existing market. Their greater flexibility and affordability may moreover enable the development of bespoke constellations for specific conservation and ecological applications, and provide greater interoperability with ground-based sensors, such as tracking devices and camera traps. In addition, SmallSats may serve as cost-effective research and development platforms for new components and products. Combined, these benefits could significantly improve our ability to monitor threats to the environment as they unfold, while enhancing our understanding of animal ecology and ecosystem dynamics. However, significant hardware and software developments are required before such technology is able to produce, process and handle reliable and cost-effective data, and the initial research and development costs still represent a major challenge. Further, we argue that much remains to be done to ensure these new data products become accessible, equitable and sustainable.

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“…Machine learning is rapidly gaining recognition as a promising tool for many biomonitoring applications, such as identifying fish species [7], forest surveillance [8], or monitoring arXiv:2002.10420v1 [cs.CV] 12 Feb 2020 Arctic flowering seasons [9]. In this paper, we concentrate on benthic macroinvertebrate identification.…”

Section: Machine Learning In Biomonitoringmentioning

confidence: 99%

Boosting Rare Benthic Macroinvertebrates Taxa Identification With One-Class Classification

Sohrab

Raitoharju

2020

2020 IEEE Symposium Series on Computational Intelligence (SSCI)

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Insect monitoring is crucial for understanding the consequences of rapid ecological changes, but taxa identification currently requires tedious manual expert work and cannot be scaled-up efficiently. Deep convolutional neural networks (CNNs), provide a viable way to significantly increase the biomonitoring volumes. However, taxa abundances are typically very imbalanced and the amounts of training images for the rarest classes are simply too low for deep CNNs. As a result, the samples from the rare classes are often completely missed, while detecting them has biological importance. In this paper, we propose combining the trained deep CNN with one-class classifiers to improve the rare species identification. One-class classification models are traditionally trained with much fewer samples and they can provide a mechanism to indicate samples potentially belonging to the rare classes for human inspection. Our experiments confirm that the proposed approach may indeed support moving towards partial automation of the taxa identification task.

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Forest Monitoring in Guatemala Using Satellite Imagery and Deep Learning

Cited by 11 publications

References 2 publications

Forest Segmentation with Spatial Pyramid Pooling Modules: A Surveillance System Based on Satellite Images

Forest Segmentation with Spatial Pyramid Pooling Modules: A Surveillance System Based on Satellite Images

SmallSats: a new technological frontier in ecology and conservation?

Boosting Rare Benthic Macroinvertebrates Taxa Identification With One-Class Classification

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