279 research outputs found

    Continuous detection of small-scale changes in Scots pine dominated stands using dense Sentinel-2 time series

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    Climate change and severe extreme events, i.e., changes in precipitation and higher drought frequency, have a large impact on forests. In Poland, particularly Norway spruce and Scots pine forest stands are exposed to disturbances and have, thus experienced changes in recent years. Considering that Scots pine stands cover approximately 58% of forests in Poland, mapping these areas with an early and timely detection of forest cover changes is important, e.g., for forest management decisions. A cost-efficient way of monitoring forest changes is the use of remote sensing data from the Sentinel-2 satellites. They monitor the Earth’s surface with a high temporal (2–3 days), spatial (10–20 m), and spectral resolution, and thus, enable effective monitoring of vegetation. In this study, we used the dense time series of Sentinel-2 data from the years 2015–2019, (49 images in total), to detect changes in coniferous forest stands dominated by Scots pine. The simple approach was developed to analyze the spectral trajectories of all pixels, which were previously assigned to the probable forest change mask between 2015 and 2019. The spectral trajectories were calculated using the selected Sentinel-2 bands (visible red, red-edge 1–3, near-infrared 1, and short-wave infrared 1–2) and selected vegetation indices (Normalized Difference Moisture Index, Tasseled Cap Wetness, Moisture Stress Index, and Normalized Burn Ratio). Based on these, we calculated the breakpoints to determine when the forest change occurred. Then, a map of forest changes was created, based on the breakpoint dates. An accuracy assessment was performed for each detected date class using 861 points for 46 classes (45 dates and one class representing no changes detected). The results of our study showed that the short-wave infrared 1 band was the most useful for discriminating Scots pine forest stand changes, with the best overall accuracy of 75%. The evaluated vegetation indices underperformed single bands in detecting forest change dates. The presented approach is straightforward and might be useful in operational forest monitoring

    Early Detection of Bark Beetle Attack Using Remote Sensing and Machine Learning: A Review

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    Bark beetle outbreaks can result in a devastating impact on forest ecosystem processes, biodiversity, forest structure and function, and economies. Accurate and timely detection of bark beetle infestations is crucial to mitigate further damage, develop proactive forest management activities, and minimize economic losses. Incorporating remote sensing (RS) data with machine learning (ML) (or deep learning (DL)) can provide a great alternative to the current approaches that rely on aerial surveys and field surveys, which are impractical over vast geographical regions. This paper provides a comprehensive review of past and current advances in the early detection of bark beetle-induced tree mortality from three key perspectives: bark beetle & host interactions, RS, and ML/DL. We parse recent literature according to bark beetle species & attack phases, host trees, study regions, imagery platforms & sensors, spectral/spatial/temporal resolutions, spectral signatures, spectral vegetation indices (SVIs), ML approaches, learning schemes, task categories, models, algorithms, classes/clusters, features, and DL networks & architectures. This review focuses on challenging early detection, discussing current challenges and potential solutions. Our literature survey suggests that the performance of current ML methods is limited (less than 80%) and depends on various factors, including imagery sensors & resolutions, acquisition dates, and employed features & algorithms/networks. A more promising result from DL networks and then the random forest (RF) algorithm highlighted the potential to detect subtle changes in visible, thermal, and short-wave infrared (SWIR) spectral regions.Comment: Under review, 33 pages, 5 figures, 8 Table

    Derivation of forest inventory parameters from high-resolution satellite imagery for the Thunkel area, Northern Mongolia. A comparative study on various satellite sensors and data analysis techniques.

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    With the demise of the Soviet Union and the transition to a market economy starting in the 1990s, Mongolia has been experiencing dramatic changes resulting in social and economic disparities and an increasing strain on its natural resources. The situation is exacerbated by a changing climate, the erosion of forestry related administrative structures, and a lack of law enforcement activities. Mongolia’s forests have been afflicted with a dramatic increase in degradation due to human and natural impacts such as overexploitation and wildfire occurrences. In addition, forest management practices are far from being sustainable. In order to provide useful information on how to viably and effectively utilise the forest resources in the future, the gathering and analysis of forest related data is pivotal. Although a National Forest Inventory was conducted in 2016, very little reliable and scientifically substantiated information exists related to a regional or even local level. This lack of detailed information warranted a study performed in the Thunkel taiga area in 2017 in cooperation with the GIZ. In this context, we hypothesise that (i) tree species and composition can be identified utilising the aerial imagery, (ii) tree height can be extracted from the resulting canopy height model with accuracies commensurate with field survey measurements, and (iii) high-resolution satellite imagery is suitable for the extraction of tree species, the number of trees, and the upscaling of timber volume and basal area based on the spectral properties. The outcomes of this study illustrate quite clearly the potential of employing UAV imagery for tree height extraction (R2 of 0.9) as well as for species and crown diameter determination. However, in a few instances, the visual interpretation of the aerial photographs were determined to be superior to the computer-aided automatic extraction of forest attributes. In addition, imagery from various satellite sensors (e.g. Sentinel-2, RapidEye, WorldView-2) proved to be excellently suited for the delineation of burned areas and the assessment of tree vigour. Furthermore, recently developed sophisticated classifying approaches such as Support Vector Machines and Random Forest appear to be tailored for tree species discrimination (Overall Accuracy of 89%). Object-based classification approaches convey the impression to be highly suitable for very high-resolution imagery, however, at medium scale, pixel-based classifiers outperformed the former. It is also suggested that high radiometric resolution bears the potential to easily compensate for the lack of spatial detectability in the imagery. Quite surprising was the occurrence of dark taiga species in the riparian areas being beyond their natural habitat range. The presented results matrix and the interpretation key have been devised as a decision tool and/or a vademecum for practitioners. In consideration of future projects and to facilitate the improvement of the forest inventory database, the establishment of permanent sampling plots in the Mongolian taigas is strongly advised.2021-06-0

    Data assimilation of forest variables predicted from remote sensing data

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    Forest information for management planning is today gathered through a combination of field inventories and remote sensing, but the available flow of remote sensing data over time is not yet utilized for continuously improving predictions of forest variables. In the thesis, the utility of data assimilation, in particular the Extended Kalman filter, for forest variable prediction is investigated. This is an iterative algorithm, where data are repeatedly merged and forecasted. The test site was a forest estate in southern Sweden (Lat. 58°N Long. 13°E). Data assimilation of remote sensing predictions of canopy surface models from digital aerial photogrammetry in paper I and predictions based on interferometric synthetic aperture radar in paper II provided a marginally improved accuracy. This gain was, however, far from the theoretical potential of data assimilation. The reason for this was suggested to be correlation of errors of subsequent predictions across time, i.e. residuals from different predictions over a certain forest area had a similar size and sign. In paper III these error correlations were quantified, and an example of the importance of considering them was given. In paper IV, it was shown that classical calibration could be applied to counteract regression toward the mean, and thus reduce the error correlations. In paper V, it was shown that data assimilation applied to a time series of data from various remote sensing sensors could be used to, over time, improve initial predictions based on aerial laser scanning data. It was also shown how the combination of classical calibration and a suggested modified version of the extended Kalman filter, that accounted for error correlations, contributed to these promising results

    Operationalization of Remote Sensing Solutions for Sustainable Forest Management

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    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

    Forests for a Better Future Sustainability, Innovation and Interdisciplinarity

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    This book highlights the role of research in innovation and sustainability in the forest sector. The contributions included fall within the broad thematic areas of forest science and cover crucial topics such as biocontrol, forest fire risk, harvesting and logging practices, quantitative and qualitative assessments of forest products, urban forests, and wood treatments—topics that have also been addressed from an interdisciplinary perspective. The contributions also have practical applications, as they deal with the ecological and economic importance of forests and new technologies for the conservation, monitoring, and improvement of services and forest value

    Remote sensing for the Spanish forests in the 21st century: a review of advances, needs, and opportunities

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    [EN] Forest ecosystems provide a host of services and societal benefits, including carbon storage, habitat for fauna, recreation, and provision of wood or non-wood products. In a context of complex demands on forest resources, identifying priorities for biodiversity and carbon budgets require accurate tools with sufficient temporal frequency. Moreover, understanding long term forest dynamics is necessary for sustainable planning and management. Remote sensing (RS) is a powerful means for analysis, synthesis, and report, providing insights and contributing to inform decisions upon forest ecosystems. In this communication we review current applications of RS techniques in Spanish forests, examining possible trends, needs, and opportunities offered by RS in a forestry context. Currently, wall-to-wall optical and LiDAR data are extensively used for a wide range of applications-many times in combination-whilst radar or hyperspectral data are rarely used in the analysis of Spanish forests. Unmanned Aerial Vehicles (UAVs) carrying visible and infrared sensors are gaining ground in acquisition of data locally and at small scale, particularly for health assessments. Forest fire identification and characterization are prevalent applications at the landscape scale, whereas structural assessments are the most widespread analyses carried out at limited extents. Unparalleled opportunities are offered by the availability of diverse RS data like those provided by the European Copernicus programme and recent satellite LiDAR launches, processing capacity, and synergies with other ancillary sources to produce information of our forests. Overall, we live in times of unprecedented opportunities for monitoring forest ecosystems with a growing support from RS technologies.Part of this work was funded by the Spanish Ministry of Science, innovation and University through the project AGL2016-76769-C2-1-R "Influence of natural disturbance regimes and management on forests dynamics. structure and carbon balance (FORESTCHANGE)".GĂłmez, C.; Alejandro, P.; Hermosilla, T.; Montes, F.; Pascual, C.; Ruiz FernĂĄndez, LÁ.; Álvarez-Taboada, F.... (2019). Remote sensing for the Spanish forests in the 21st century: a review of advances, needs, and opportunities. Forest Systems. 28(1):1-33. https://doi.org/10.5424/fs/2019281-14221S133281Ungar S, Pearlman J, Mendenhall J, Reuter D, 2003. Overview of the Earth Observing-1 (EO-1) mission. IEEE T Geosci Remote 41: 1149−1159.Valbuena R, Mauro F, Arjonilla FJ, Manzanera JA, 2011. Comparing Airborne Laser Scanning-Imagery Fusion Methods Based on Geometric Accuracy in Forested Areas. Remote Sens Environ 115(8): 1942-1956.Valbuena R, Mauro F, RodrĂ­guez-Solano R, Manzanera JA, 2012. Partial Least Squares for Discriminating Variance Components in GNSS Accuracy Obtained Under Scots Pine Canopies. Forest Sci 58(2): 139-153.Valbuena R, De Blas A, MartĂ­n FernĂĄndez S, Maltamo M, Nabuurs GJ, Manzanera JA, 2013a. Within-Species Benefits of Back-projecting Laser Scanner and Multispectral Sensors in Monospecific P. sylvestris Forests. Eur J Remote Sens 46: 401-416.Valbuena R, Maltamo M, MartĂ­n-FernĂĄndez S, Packalen P, Pascual C, Nabuurs G-J, 2013b. Patterns of covariance between airborne laser scanning metrics and Lorenz curve descriptors of tree size inequality. Can J Remote Sens 39(1): 18-31.Valbuena R, Packalen P, GarcĂ­a-Abril A, MehtĂ€talo L, Maltamo M, 2013c. Characterizing Forest Structural Types and Shelterwood Dynamics from Lorenz-based Indicators Predicted by Airborne Laser Scanning. Can J For Res 43: 1063-1074.Valbuena R, Maltamo M, Packalen P, 2016a. Classification of Multi-Layered Forest Development Classes from Low-Density National Airborne LiDAR Datasets. Forestry 89: 392-341.Valbuena R, Maltamo M, Packalen P, 2016b. Classification of Forest Development Stages from National Low-Density LiDAR Datasets: a Comparison of Machine Learning Methods. Revista de TeledetecciĂłn 45: 15-25.Valbuena R, Hernando A, Manzanera JA, MartĂ­nez-Falero E, GarcĂ­a-Abril A, Mola-Yudego B, 2017a. Most Similar Neighbour Imputation of Forest Attributes Using Metrics Derived from Combined Airborne LIDAR and Multispectral Sensors. Int J Digit Earth 11 (12): 1205-1218.Valbuena R, Hernando A, Manzanera JA, Görgens EB, Almeida DRA, Mauro F, GarcĂ­a-Abril A, Coomes DA, 2017b. Enhancing of accuracy assessment for forest above-ground biomass estimates obtained from remote sensing via hypothesis testing and overfitting evaluation. Eco Mod 622: 15-26.Valbuena-RabadĂĄn M, SantamarĂ­a-Pe-a J, Sanz-AdĂĄn F, 2016. Estimation of diameter and height of individual trees for Pinus sylvestris L. based on the individualising of crowns using airborne LiDAR and the National Forest Inventory data. For Sys 25(1): e046Varo-MartĂ­nez MA, Navarro-Cerrillo RM, HernĂĄndez-Clemente R, Duque-Lazo J, 2017. Semi-automated stand delineation in Mediterranean Pinus sylvestris plantations through segmentation of LiDAR data: The influence of pulse density. Int J Appl Earth Obs 56: 54-64.VĂĄzquez de la Cueva A, 2008. Structural attributes of three forest types in central Spain and Landsat ETM+ information evaluated with redundancy analysis. Int J Remote Sens 29: 5657-5676.VerdĂș F, Salas J, 2010. CartografĂ­a de ĂĄreas quemadas mediante anĂĄlisis visual de imĂĄgenes de satĂ©lite en la Espa-a peninsular para el periodo 1991–2005. Geofocus 10: 54–81.Viana-Soto A, Aguado I, MartĂ­nez S, 2017. Assessment of post-fire vegetation recovery using fire severity and geographical data in the Mediterranean region (Spain). Environments 4: 90.Vicente-Serrano SG, PĂ©rez-Cabello F, Lasanta T, 2011. Pinus halepensis regeneration after a wildfire in a semiarid environment: assessment using multitemporal Landsat images. Int J Wildland Fire 20Ñ 195-208.Viedma O, Quesada J, Torres I, De Santis A, Moreno JM, 2015. Fire severity in a large fire in a Pinus pinaster forest is highly predictable from burning conditions, stand structure, and topography. Ecosystems 18: 237-250.Yebra M, Chuvieco E, 2009. Generation of a species-specific look-up table for fuel moisture content assessment. IEEE J Selected topics in applied earth observation and RS 2 (1): 21-26.White JC, Wulder MA, Varhola A, Vastaranta M, Coops NC, Cook BD, Pitt D, Woods M, 2013. A best practices guide for generating forest inventory attributes from airborne laser scanning data using an area-based approach. Natural Resources Canada, Canadian Forest Service, Canadian Wood Fibre Centre, Victoria, BC. Information Report FI-X-010, 39 pp.White JC, Wulder MA, Hobart GW, Luther JE, Hermosilla T, Griffiths P, Coops NC, Hall RJ, Hostert P, Dyk A, Guindon L, 2014. Pixel-based image compositing for large-area dense time series applications and science. Can J Remote Sens 40 (3): 192-212.White JC, Coops NC, Wulder MA, Vastaranta M, Hilker T, Tompalski P, 2016. Remote sensing technologies for enhancing forest inventories: a review. Can J Remote Sens 42: 619-641.White JC, Wulder MA, Hermosilla T, Coops NC, Hobart GW, 2017. A nationwide characterization of 25 years of forest disturbance and recovery for Canada using Landsat time series. Remote Sens Environ 194: 303-321.Wulder MA, 1998. Optical remote-sensing techniques for the assessment of forest inventory and biophysical parameters. Progr Phys Geog 22 (4): 449-476.Wulder MA, Dymond CC, 2004. Remote sensing in survey of Mountain Pine impacts: review and recommendations. MPBI Report. Canadian Forest Service. Natural Resources Canada, Victoria, BC, Canada. 89 pp.Wulder MA, Masek JG, Cohen WB, Loveland TR, Woodcock CE, 2012. Opening the archive: how free data has enabled the science and monitoring promise of Landsat. Remote Sens Environ 122: 2-10.Wulder MA, Hilker T, White JC, Coops NC, Masek JG, Pflugmacher D, Crevier Y, 2015. Virtual constellations for global terrestrial monitoring. Remote Sens Environ 170: 62-76.Wulder MA, White JC, Loveland TR, Woodcock CE, Belward AS, Cohen WB, Fosnight EA, Shaw J, Masek JG, Roy DP, 2016. The global Landsat archive: Status, consolidation, and direction. Remote Sens Environ 185: 271-283.Xie Q, Zhu J, Wang Ch, Fu H, LĂłpez-SĂĄnchez JM, Ballester-Berman JD, 2017. A modified dual-baseline PolInSAR method for forest height estimation. Remote Sens-Basel 9 (8): 819.Xie Y, Sha Z, Yu M, 2008. Remote sensing imagery in vegetation mapping: a review. J Plant Ecol 1 (1): 9-23.Zald HSJ, Wulder MA, White JC, Hilker T, Hermosilla T, Hobart GW, Coops NC, 2016. Integrating Landsat pixel composites and change metrics with LiDAR plots to predictively map forest structure and aboveground biomass in Saskatchewan, Canada. Remote Sens Environ 176: 188-201.Zarco-Tejada PJ, Diaz-Varela R, Angileri V, Loudjani P, 2014. Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods. Eur J Agron 55: 89-99.Zarco-Tejada PJ, Hornero A, HernĂĄndez-Clemente R, Beck PSA, 2018. Understanding the temporal dimension of the red-edge spectral region for forest decline detection using high-resolution hyperspectral and Sentinel-2A imagery. ISPRS J Photogramm 137: 134-148

    Predicting Species and Structural Diversity of Temperate Forests with Satellite Remote Sensing and Deep Learning

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    Anthropogenically-driven climate change, land-use changes, and related biodiversity losses are threatening the capability of forests to provide a variety of valuable ecosystem services. The magnitude and diversity of these services are governed by tree species richness and structural complexity as essential regulators of forest biodiversity. Sound conservation and sustainable management strategies rely on information from biodiversity indicators that is conventionally derived by field-based, periodical inventory campaigns. However, these data are usually site-specific and not spatially explicit, hampering their use for large-scale monitoring applications. Therefore, the main objective of our study was to build a robust method for spatially explicit modeling of biodiversity variables across temperate forest types using open-access satellite data and deep learning models. Field data were obtained from the Biodiversity Exploratories, a research infrastructure platform that supports ecological research in Germany. A total of 150 forest plots were sampled between 2014 and 2018, covering a broad range of environmental and forest management gradients across Germany. From field data, we derived key indicators of tree species diversity (Shannon Wiener Index) and structural heterogeneity (standard deviation of tree diameter) as proxies of forest biodiversity. Deep neural networks were used to predict the selected biodiversity variables based on Sentinel-1 and Sentinel-2 images from 2017. Predictions of tree diameter variation achieved good accuracy (r2 = 0.51) using Sentinel-1 winter-based backscatter data. The best models of species diversity used a set of Sentinel-1 and Sentinel-2 features but achieved lower accuracies (r2 = 0.25). Our results demonstrate the potential of deep learning and satellite remote sensing to predict forest parameters across a broad range of environmental and management gradients at the landscape scale, in contrast to most studies that focus on very homogeneous settings. These highly generalizable and spatially continuous models can be used for monitoring ecosystem status and functions, contributing to sustainable management practices, and answering complex ecological questions.publishedVersio
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