Mapping a European spruce bark beetle outbreak using sentinel-2 remote sensing data

Insect outbreaks affect forests, causing the deaths of trees and high economic loss. In this study, we explored the detection of European spruce bark beetle (Ips typographus, L.) outbreaks at the individual tree crown level using multispectral satellite images. Moreover, we explored the possibility of tracking the progression of the outbreak over time using multitemporal data. Sentinel-2 data acquired during the summer of 2020 over a bark beetle–infested area in the Italian Alps were used for the mapping and tracking over time, while airborne lidar data were used to automatically detect the individual tree crowns and to classify tree species. Mapping and tracking of the outbreak were carried out using a support vector machine classifier with input vegetation indices extracted from the multispectral data. The results showed that it was possible to detect two stages of the outbreak (i.e., early, and late) with an overall accuracy of 83.4%. Moreover, we showed how it is technically possible to track the evolution of the outbreak in an almost bi-weekly period at the level of the individual tree crowns. The outcomes of this paper are useful from both a management and ecological perspective: it allows forest managers to map a bark beetle outbreak at different stages with a high spatial accuracy, and the maps describing the evolution of the outbreak could be used in further studies related to the behavior of bark beetle

Evaluation of Xgboost and Lgbm Performance in Tree Species Classification with Sentinel-2 Data

Los, H., Mendes, G. S., Cordeiro, D., Grosso, N., Costa, H., Benevides, P., & Caetano, M. (2021). Evaluation of Xgboost and Lgbm Performance in Tree Species Classification with Sentinel-2 Data. In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium: Proceedings (pp. 5803-5806). IEEE. https://doi.org/10.1109/IGARSS47720.2021.9553031Tree species classification with satellite data has become more and more popular since Sentinel-2 launch. We compared efficacy and effectiveness of Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LGBM) with widely used in remote sensing Random Forest (RF), Support Vector Machine (SVM) and K-Nearest Neighbour (KNN) algorithms. Analyses were performed over an area in Portugal with multi-temporal Sentinel-2 data registered in April, June, August and October 2018. The selected classes were: cork oak, holm oak, eucalyptus, other broadleaved, maritime pine, stone pine and other coniferous. Algorithm efficacy was measured through F1-score and accuracy while efficiency was measured through the median time needed for each fit. XGBoost and LGBM outperformed efficacy of other algorithms, which was already high (above 90% for the best variant of each algorithm). In terms of efficacy, LGBM overcame all algorithms, including XGBoost.authorsversionpublishe

Operationalization of Remote Sensing Solutions for Sustainable Forest Management

The great potential of remote sensing technologies for operational use in sustainable forest management is addressed in this book, which is the reprint of papers published in the Remote Sensing Special Issue “Operationalization of Remote Sensing Solutions for Sustainable Forest Management”. The studies come from three continents and cover multiple remote sensing systems (including terrestrial mobile laser scanning, unmanned aerial vehicles, airborne laser scanning, and satellite data acquisition) and a diversity of data processing algorithms, with a focus on machine learning approaches. The focus of the studies ranges from identification and characterization of individual trees to deriving national- or even continental-level forest attributes and maps. There are studies carefully describing exercises on the case study level, and there are also studies introducing new methodologies for transdisciplinary remote sensing applications. Even though most of the authors look forward to continuing their research, nearly all studies introduced are ready for operational use or have already been implemented in practical forestry

Deep neural networks with transfer learning for forest variable estimation using sentinel-2 imagery in boreal forest

Estimation of forest structural variables is essential to provide relevant insights for public and private stakeholders in forestry and environmental sectors. Airborne light detection and ranging (LiDAR) enables accurate forest inventory, but it is expensive for large area analyses. Continuously increasing volume of open Earth Observation (EO) imagery from high-resolution (|BIAS%| = 0.8%). We found 3×3 pixels to be the optimal size for the sampling window, and two to three hidden layer DNNs to produce the best results with relatively small improvement to single hidden layer networks. Including CHM features with S2 data and additional features led to reduced relative RMSE (RMSE% = 28.6–30.7%) but increased the absolute value of relative bias (|BIAS%| = 0.9–4.0%). Transfer learning was found to be beneficial mainly with training data sets containing less than 250 field plots. The performance differences of DNN and random forest models were marginal. Our results contribute to improved structural variable estimation performance in boreal forests with the proposed image sampling and input feature concept

Commercial forest species discrimination and mapping using cost effective multispectral remote sensing in midlands region of KwaZulu-Natal province, South Africa.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg, 2018.Discriminating forest species is critical for generating accurate and reliable information necessary for sustainable management and monitoring of forests. Remote sensing has recently become a valuable source of information in commercial forest management. Specifically, high spatial resolution sensors have increasingly become popular in forests mapping and management. However, the utility of such sensors is costly and have limited spatial coverage, necessitating investigation of cost effective, timely and readily available new generation sensors characterized by larger swath width useful for regional mapping. Therefore, this study sought to discriminate and map commercial forest species (i.e. E. dunii, E.grandis, E.mix, A.mearnsii, P.taedea and P.tecunumanii, P.elliotte) using cost effective multispectral sensors. The first objective of this study was to evaluate the utility of freely available Landsat 8 Operational Land Imager (OLI) in mapping commercial forest species. Using Partial Least Square Discriminant Analysis algorithm, results showed that Landsat 8 OLI and pan-sharpened version of Landsat 8 OLI image achieved an overall classification accuracy of 79 and 77.8%, respectively, while WorldView-2 used as a benchmark image, obtained 86.5%. Despite low spatial of resolution 30 m, result show that Landsat 8 OLI was reliable in discriminating forest species with reasonable and acceptable accuracy. This freely available imagery provides cheaper and accessible alternative that covers larger swath-width, necessary for regional and local forests assessment and management. The second objective was to examine the effectiveness of Sentinel-1 and 2 for commercial forest species mapping. With the use of Linear Discriminant Analysis, results showed an overall accuracy of 84% when using Sentinel 2 raw image as a standalone data. However, when Sentinel 2 was fused with Sentinel’s 1 Synthetic Aperture Radar (SAR) data, the overall accuracy increased to 88% using Vertical transmit/Horizontal receive (VH) polarization and 87% with Vertical transmit/Vertical receive (VV) polarization datasets. The utility of SAR data demonstrates capability for complementing Sentinel-2 multispectral imagery in forest species mapping and management. Overall, newly generated and readily available sensors demonstrated capability to accurately provide reliable information critical for mapping and monitoring of commercial forest species at local and regional scales

Tree species classification using Sentinel-2 imagery and Bayesian inference

The increased temporal frequency of optical satellite data acquisitions provides a data stream that has the potential to improve land cover mapping, including mapping of tree species. However, for large area operational mapping, partial cloud cover and different image extents can pose challenges. Therefore, methods are needed to assimilate new images in a straightforward way without requiring a total spatial coverage for each new image. This study shows that Bayesian inference applied sequentially has the potential to solve this problem. To test Bayesian inference for tree species classification in the boreo-nemoral zone of southern Sweden, field data from the study area of Remningstorp (58?27?18.35?N, 13?39?8.03?E) were used. By updating class likelihood with an increasing number of combined Sentinel-2 images, a higher and more stable cross-validated overall accuracy was achieved. Based on a Mahalanobis distance, 23 images were automatically chosen from the period of 2016 to 2018 (from 142 images total). An overall accuracy of 87% (a Cohen?s kappa of 78.5%) was obtained for four tree species classes: Betula spp., Picea abies, Pinus sylvestris, and Quercus robur. This application of Bayesian inference in a boreo-nemoral forest suggests that it is a practical way to provide a high and stable classification accuracy. The method could be applied where data are not always complete for all areas. Furthermore, the method requires less reference data than if all images were used for classification simultaneously

Land cover and forest segmentation using deep neural networks

Tiivistelmä. Land Use and Land Cover (LULC) information is important for a variety of applications notably ones related to forestry. The segmentation of remotely sensed images has attracted various research subjects. However this is no easy task, with various challenges to face including the complexity of satellite images, the difficulty to get hold of them, and lack of ready datasets. It has become clear that trying to classify on multiple classes requires more elaborate methods such as Deep Learning (DL). Deep Neural Networks (DNNs) have a promising potential to be a good candidate for the task. However DNNs require a huge amount of data to train including the Ground Truth (GT) data. In this thesis a DL pixel-based approach backed by the state of the art semantic segmentation methods is followed to tackle the problem of LULC mapping. The DNN used is based on DeepLabv3 network with an encoder-decoder architecture. To tackle the issue of lack of data the Sentinel-2 satellite whose data is provided for free by Copernicus was used with the GT mapping from Corine Land Cover (CLC) provided by Copernicus and modified by Tyke to a higher resolution. From the multispectral images in Sentinel-2 Red Green Blue (RGB), and Near Infra Red (NIR) channels were extracted, the 4th channel being extremely useful in the detection of vegetation. This ended up achieving quite good accuracy on a DNN based on ResNet-50 which was calculated using the Mean Intersection over Union (MIoU) metric reaching 0.53MIoU. It was possible to use this data to transfer the learning to a data from Pleiades-1 satellite with much better resolution, Very High Resolution (VHR) in fact. The results were excellent especially when compared on training right away on that data reaching an accuracy of 0.98 and 0.85MIoU