Sensitivity of Landsat-8 OLI and TIRS Data to Foliar Properties of Early Stage Bark Beetle (Ips typographus, L.) Infestation

Abdullah, Hewa Y.; Darvishzadeh, Roshanak; Skidmore, A.K.; Heurich, Marco

doi:10.3390/rs11040398

Cited by 40 publications

(38 citation statements)

References 92 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our study also demonstrated the usefulness of UAV imagery as a reference dataset for classification development and the evaluation of satellite imagery and, thus, its ability to replace more time-consuming ground surveys and more expensive aerial imagery. In the future, we will further use the optical and thermal information of remote sensing images to obtain RTB hazard information in the green attack stage [9,59].…”

Section: Discussionmentioning

confidence: 99%

Combining GF-2 and Sentinel-2 Images to Detect Tree Mortality Caused by Red Turpentine Beetle during the Early Outbreak Stage in North China

Zhan

et al. 2020

Forests

View full text Add to dashboard Cite

In recent years, the red turpentine beetle (RTB) (Dendroctonus valens LeConte) has invaded the northern regions of China. Due to the short invasion time, the outbreak of tree mortality corresponded to a low level of damage. Important information about tree mortality, provided by remote sensing at both single-tree and forest stand scale, is needed in forest management at the early stages of outbreak. In order to detect RTB-induced tree mortality at a single-tree scale, we evaluated the classification accuracies of Gaofen-2 (GF2) imagery at different spatial resolutions (1 and 4 m) using a pixel-based method. We also simultaneously applied an object-based method to 1 m pan-sharpened images. We used Sentinel-2 (S2) imagery with different resolutions (10 and 20 m) to detect RTB-induced tree mortality and compared their classification accuracies at a larger scale—the stand scale. Three kinds of machine learning algorithms—the classification and regression tree (CART), the random forest (RF), and the support vector machine (SVM)—were applied and compared in this study. The results showed that 1 m resolution GF2 images had the highest classification accuracy using the pixel-based method and SVM algorithm (overall accuracy = 77.7%). We found that the classification of three degrees of damage percentage within the S2 pixel (0%, <15%, and 15% < x < 50%) was not successful at a forest stand scale. However, 10 m resolution S2 images could acquire effective binary classification (<15%: overall accuracy = 74.9%; 15% < x < 50%: overall accuracy = 81.0%). Our results indicated that identifying tree mortality caused by RTB at a single-tree and forest stand scale was accomplished with the combination of GF2 and S2 images. Our results are very useful for the future exploration of the patterns of spatial and temporal changes in insect pest transmission at different spatial scales.

show abstract

Section: Discussionmentioning

confidence: 99%

Combining GF-2 and Sentinel-2 Images to Detect Tree Mortality Caused by Red Turpentine Beetle during the Early Outbreak Stage in North China

Zhan

et al. 2020

Forests

View full text Add to dashboard Cite

show abstract

“…Especially, hyperspectral data are recommended to be applied for the detection of changes in foliar biochemical concentrations [23,[25][26][27]. Thermal infrared data have also proven reliable for mapping spruce bark beetle infestation at the green attack phase, as infested trees show higher emissivity than healthy trees [28,29]. Moreover, changes in forest structure corresponding to these phases can be mapped using the close-range, air-and spaceborne RS techniques [14,17,20,21,[30][31][32][33][34][35].…”

Section: State-of-the-artmentioning

confidence: 99%

Mapping European Spruce Bark Beetle Infestation at Its Early Phase Using Gyrocopter-Mounted Hyperspectral Data and Field Measurements

et al. 2021

View full text Add to dashboard Cite

The prolonged drought of recent years combined with the steadily increasing bark beetle infestation (Ips typographus) is causing enormous damage in Germany’s spruce forests. This preliminary study investigates whether early spruce infestation by the bark beetle (green attack) can be detected using indices based on airborne spatial high-resolution (0.3 m) hyperspectral data and field spectrometer measurements. In particular, a new hyperspectral index based on airborne data has been defined and compared with other common indices for bark beetle detection. It shows a very high overall accuracy (OAA = 98.84%) when validated with field data. Field measurements and a long-term validation in a second study area serve the validation of the robustness and transferability of the index to other areas. In comparison with commonly used indices, the defined index has the ability to detect a larger proportion of infested spruces in the green attack phase (60% against 20% for commonly used indices). This index confirms the high potential of the red-edge domain to distinguish infested spruces at an early stage. Overall, our index has great potential for forest preservation strategies aimed at the detection of infested spruces in order to mitigate the outbreaks.

show abstract

“…Coniferous forests monitoring using satellite remote sensing [1,2] is preferable due to the wide coverage and the efficiency of obtaining up-to-date information [3]. There are some multispectral satellite sensors with a spatial resolution of 10-30 m, the data of which are in the public domain and usually cover large territories [4] than expensive images of very high (1-10 m) and ultra-high (less than 1 m) resolutions.…”

Section: Introductionmentioning

confidence: 99%

Remote spectral methods for detecting stress coniferous

et al. 2020

View full text Add to dashboard Cite

The article presents investigation of the possibility of drying coniferous forest areas detecting by multispectral satellite data in the visible and NIR spectral range with low spatial resolution, obtained by the imaging systems of three satellites - the Belarusian spacecraft (BS), Landsat 8 and Sentinel 2. A forest area in the south of Belarus was considered as a test site. High-resolution multispectral airborne data and, in part, ground measurements were used as reference ground data by which training samples were formed. Most of the known classical methods of supervised classification have been tested, the maximum likelihood method turned out to be the best for this task. In order to improve the accuracy of identifying the drying areas of coniferous forests on multispectral images, parametric transformations of images in the spectral space are proposed, which should lead to an increase in initial small spectral differences. The methodological issues of assessing the accuracy of the satellite images classification are considered using the result of the classification of airborne image with high spatial resolution as a ground truth image. The assessment of the classification accuracy, both visually and using the obtained confusion matrices, allows us to conclude that the images of the BS, Landsat 8 and Sentinel 2 can be used to detect drying area of coniferous forests as well as the expediency of carrying out the proposed transformations of the original images.

show abstract

Sensitivity of Landsat-8 OLI and TIRS Data to Foliar Properties of Early Stage Bark Beetle (Ips typographus, L.) Infestation

Cited by 40 publications

References 92 publications

Combining GF-2 and Sentinel-2 Images to Detect Tree Mortality Caused by Red Turpentine Beetle during the Early Outbreak Stage in North China

Combining GF-2 and Sentinel-2 Images to Detect Tree Mortality Caused by Red Turpentine Beetle during the Early Outbreak Stage in North China

Mapping European Spruce Bark Beetle Infestation at Its Early Phase Using Gyrocopter-Mounted Hyperspectral Data and Field Measurements

Remote spectral methods for detecting stress coniferous

Contact Info

Product

Resources

About