Name
Title | ||
|---|---|---|
Deep Learning on Synthetic Data Enables the Automatic Identification of Deficient Forested Windbreaks in the Paraguayan Chaco |
Abstract | ||
|---|---|---|
The Paraguayan Chaco is one of the most rapidly deforested areas in Latin America, mainly due to cattle ranching. Continuously forested windbreaks between agricultural areas and forest patches within these areas are mandatory to minimise the impact that the legally permitted logging has on the ecosystem. Due to the large area of the Paraguayan Chaco, comprehensive in situ monitoring of the integrity of these landscape elements is almost impossible. Satellite-based remote sensing offers excellent prerequisites for large-scale land cover analyses. However, traditional methods mostly focus on spectral and texture information while dismissing the geometric context of landscape features. Since the contextual information is very important for the identification of windbreak gaps and central forests, a deep learning-based detection of relevant landscape features in satellite imagery could solve the problem. However, deep learning methods require a large amount of labelled training data, which cannot be collected in sufficient quantity in the investigated area. This study presents a methodology to automatically classify gaps in windbreaks and central forest patches using a convolutional neural network (CNN) entirely trained on synthetic imagery. In a two-step approach, we first used a random forest (RF) classifier to derive a binary forest mask from Sentinel-1 and -2 images for the Paraguayan Chaco in 2020 with a spatial resolution of 10 m. We then trained a CNN on a synthetic data set consisting of purely artificial binary images to classify central forest patches and gaps in windbreaks in the forest mask. For both classes, the CNN achieved an F1 value of over 70%. The presented method is among the first to use synthetically generated training images and class labels to classify natural landscape elements in remote sensing imagery and therewith particularly contributes to the research on the detection of natural objects such as windbreaks. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.3390/rs14174327 | REMOTE SENSING |
Keywords | DocType | Volume |
remote sensing, CNN, deep learning, random forest, Sentinel-1, Sentinel-2, windbreaks, landscape features, Paraguay | Journal | 14 |
Issue | ISSN | Citations |
17 | 2072-4292 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jennifer Kriese | 1 | 0 | 1.01 |
| Thorsten Hoeser | 2 | 0 | 0.34 |
| Sarah Asam | 3 | 0 | 1.01 |
| Patrick Kacic | 4 | 0 | 0.34 |
| Emmanuel Da Ponte | 5 | 0 | 0.34 |
| Ursula Gessner | 6 | 0 | 1.01 |