38 research outputs found

    Detection of forest windthrows with bitemporal COSMO-SkyMed and Sentinel-1 SAR data

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    Wind represents a primary source of disturbances in forests, necessitating an assessment of the resulting damage to ensure appropriate forest management. Remote sensing, encompassing both active and passive techniques, offers a valuable and efficient approach for this purpose, enabling coverage of large areas while being costeffective. Passive remote sensing data could be affected by the presence of clouds, unlike active systems such as Synthetic Aperture Radar (SAR) which are relatively less affected. Therefore, this study aims to explore the utilization of bitemporal SAR data for windthrow detection in mountainous regions. Specifically, we investigated how the detection outcomes vary based on three factors: i) the SAR wavelength (X-band or C-band), ii) the acquisition period of the pre- and post-event images (summer, autumn, or winter), and iii) the forest type (evergreen vs. deciduous). Our analysis considers two SAR satellite constellations: COSMO-SkyMed (band-X, with a pixel spacing of 2.5 m and 10 m) and Sentinel-1 (band-C, with a pixel spacing of 10 m). We focused on three study sites located in the Trentino-South Tyrol region of Italy, which experienced significant forest damage during the Vaia storm from 27th to 30th October 2018. To accomplish our objectives, we employed a detailpreserving, scale-driven approach for change detection in bitemporal SAR data. The results demonstrate that: i) the algorithm exhibits notably better performance when utilizing X-band data, achieving a highest kappa accuracy of 0.473 and a balanced accuracy of 76.1%; ii) the pixel spacing has an influence on the accuracy, with COSMO-SkyMed data achieving kappa values of 0.473 and 0.394 at pixel spacings of 2.5 m and 10 m, respectively; iii) the post-event image acquisition season significantly affects the algorithm’s performance, with summer imagery yielding superior results compared to winter imagery; and iv) the forest type (evergreen vs. deciduous) has a noticeable impact on the results, particularly when considering autumn/winter dat

    Continuous Forest Monitoring Using Cumulative Sums of Sentinel-1 Timeseries

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    Forest degradation is recognized as a major environmental threat on a global scale. The recent rise in natural and anthropogenic destruction of forested ecosystems highlights the need for developing new, rapid, and accurate remote sensing monitoring systems, which capture forested land transformations. In spite of the great technological advances made in airborne and spaceborne sensors over the past decades, current Earth observation (EO) change detection methods still need to overcome numerous limitations. Optical sensors have been commonly used for detecting land use and land cover changes (LULCC), however, the requirement of certain technical and environmental conditions (e.g., sunlight, not cloud-coverage) restrict their use. More recently, synthetic aperture radar (SAR)-based change detection approaches have been used to overcome these technical limitations, but they commonly rely on static detection approaches (e.g., pre and post disturbance scenario comparison) that are slow to monitor change. In this context, this paper presents a novel approach for mapping forest structural changes in a continuous and near-real-time manner using dense Sentinel-1 image time-series. Our cumulative sum−spatial mean corrected (CUSU-SMC) algorithm approach is based on cumulative sum statistical analysis, which allows the continuous monitoring of radar signal variations, derived from forest structural change. Taking advantage of the high data availability offered by the Sentinel-1 (S-1) C-band constellation, we used an S-1 ground range detected (GRD) dual (VV, VH) polarization timeseries, formed by a total of 84 images, to monitor clear-cutting operations carried out in a Scottish forest during 2019. The analysis showed a user’s accuracy of 82% for the (conservative) detection approach. The use of a post-processing neighbor filter increased the detection performance to a user’s accuracy of 86% with an overall accuracy of 77% for areas of a minimum extent of 0.4ha. To further validate the detection performance of the method, the CUSU-SMC change detector was tested against commonly-used pairwise change detection approaches for the same period. These results emphasize the capabilities of dense SAR time-series for environmental monitoring and provide a useful tool for optimizing national forest inventories

    Estimating optical vegetation indices with Sentinel-1 SAR data and AutoML

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    Current optical vegetation indices (VIs) for monitoring forest ecosystems are widely used in various applications. However, continuous monitoring based on optical satellite data can be hampered by atmospheric effects such as clouds. On the contrary, synthetic aperture radar (SAR) data can offer insightful and systematic forest monitoring with complete time series due to signal penetration through clouds and day and night acquisitions. The goal of this work is to overcome the issues affecting optical data with SAR data and serve as a substitute for estimating optical VIs for forests using machine learning. Time series of four VIs (LAI, FAPAR, EVI and NDVI) were estimated using multitemporal Sentinel-1 SAR and ancillary data. This was enabled by creating a paired multi-temporal and multi-modal dataset in Google Earth Engine (GEE), including temporally and spatially aligned Sentinel-1, Sentinel-2, digital elevation model (DEM), weather and land cover datasets (MMT-GEE). The use of ancillary features generated from DEM and weather data improved the results. The open-source Automatic Machine Learning (AutoML) approach, auto-sklearn, outperformed Random Forest Regression for three out of four VIs, while a 1-hour optimization length was enough to achieve sufficient results with an R2 of 69-84% low errors (0.05-0.32 of MAE depending on VI). Great agreement was also found for selected case studies in the time series analysis and in the spatial comparison between the original and estimated SAR-based VIs. In general, compared to VIs from currently freely available optical satellite data and available global VI products, a better temporal resolution (up to 240 measurements/year) and a better spatial resolution (20 m) were achieved using estimated SAR-based VIs. A great advantage of the SAR-based VI is the ability to detect abrupt forest changes with a sub-weekly temporal accuracy.Comment: Full research article. 30 pages, 13 figures, 8 table

    Tree stand assessment before and after windthrow based on open-access biodiversity data and aerial photography

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    The ground-based surveys of areas affected by storms might be difficult or even impossible because of the limited ability to move within the damaged area. Therefore, this work was aimed to estimate storm damage based on aerial photography and open biodiversity data available via the Internet. The study was carried out in the old-growth hemiboreal forests of the Kologrivsky Forest State Nature Reserve (Kostroma Region, Russia), which was affected by a catastrophic windthrow caused by a storm on 15.05.2021. The sampling area was 100 000 m2. We used our previous ground-survey studies and open-access biodiversity data available through the Global Biodiversity Information Facility for describing the forest stands composition before the catastrophic event. The aerial photography data were used for estimating tree stands damages after the windthrow. For remote data collecting, we used an unmanned aerial vehicle – quadrocopter DJI Phantom 4. Agisoft Metashape software was used for aerial photographs processing. The obtained photogrammetric digital elevation model (DEM) and orthophoto-mosaic were processed with QGIS software. Damaged areas were detected automatically based on the DEM. Individual fallen trees were visually detected using the orthophoto-mosaic. We found before the windthrow the study area was covered by old-growth stands developed naturally over a long time. The stand structure was multi-layered and uneven-aged. The ontogenetic spectra of late-successional tree species Picea abies (hereinafter – spruce) and Tilia cordata (hereinafter – linden) were normal. The old-growth stands were heterogeneous before the windthrow: the canopy closed multi-layered and uneven-aged stands, decaying spruce stands and areas where spruce completely fell out and the tree stand was absent. In addition, old-growth linden stands were present. According to the obtained results, the stand structure was critically changed caused by the windthrow. The DEM-processing results showed the windthrow strongly damaged 33.1% stands in the study area. Using the orthophoto-mosaic, we visually detected 759 fallen trees. Among them, 82.9% were associated with strongly-damaged areas. According to the DEM classification, the rest of the visually detected fallen trees were in non-damaged areas and canopy gaps established before the windthrow. The analysis showed that these were less damaged areas with survived stands or groups of trees after the storm. Thus, our results showed that it is necessary to use both the DEM and the orthophoto-mosaic for more accurate estimates. Our exploratory analysis of different tree stand damages found that apparently, spruce stands were more affected by the storm than linden stands. It is explained by the different wind resistance of spruce and linden and differences in regrowth density and species composition in these stands

    Improved Difference Images for Change Detection Classifiers in SAR Imagery Using Deep Learning

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    Satellite-based Synthetic Aperture Radar (SAR) images can be used as a source of remote sensed imagery regardless of cloud cover and day-night cycle. However, the speckle noise and varying image acquisition conditions pose a challenge for change detection classifiers. This paper proposes a new method of improving SAR image processing to produce higher quality difference images for the classification algorithms. The method is built on a neural network-based mapping transformation function that produces artificial SAR images from a location in the requested acquisition conditions. The inputs for the model are: previous SAR images from the location, imaging angle information from the SAR images, digital elevation model, and weather conditions. The method was tested with data from a location in North-East Finland by using Sentinel-1 SAR images from European Space Agency, weather data from Finnish Meteorological Institute, and a digital elevation model from National Land Survey of Finland. In order to verify the method, changes to the SAR images were simulated, and the performance of the proposed method was measured using experimentation where it gave substantial improvements to performance when compared to a more conventional method of creating difference images

    Monitoring Bark Beetle Forest Damage in Central Europe. A Remote Sensing Approach Validated with Field Data

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    Over the last decades, climate change has triggered an increase in the frequency of sprucebark beetle (Ips typographusL.) in Central Europe. More than 50% of forests in the Czech Republic areseriously threatened by this pest, leading to high ecological and economic losses. The exponentialincrease of bark beetle infestation hinders the implementation of costly field campaigns to prevent andmitigate its effects. Remote sensing may help to overcome such limitations as it provides frequent andspatially continuous data on vegetation condition. Using Sentinel-2 images as main input, two modelshave been developed to test the ability of this data source to map bark beetle damage and severity.All models were based on a change detection approach, and required the generation of previous forestmask and dominant species maps. The first damage mapping model was developed for 2019 and2020, and it was based on bi-temporal regressions in spruce areas to estimate forest vitality and barkbeetle damage. A second model was developed for 2020 considering all forest area, but excludingclear-cuts and completely dead areas, in order to map only changes in stands dominated by alivetrees. The three products were validated with in situ data. All the maps showed high accuracies (acc>0.80). Accuracy was higher than 0.95 and F1-score was higher than 0.88 for areas with high severity,with omission errors under 0.09 in all cases. This confirmed the ability of all the models to detectbark beetle attack at the last phases. Areas with no damage or low severity showed more complexresults. The no damage category yielded greater commission errors and relative bias (CEs=0.30-0.42,relB=0.42-0.51). The similar results obtained for 2020 leaving out clear-cuts and dead trees provedthat the proposed methods could be used to help forest managers fight bark beetle pests. These bioticdamage products based on Sentinel-2 can be set up for any location to derive regular forest vitalitymaps and inform of early damage.O

    Sentinel-1 Shadows Used to Quantify Canopy Loss from Selective Logging in Gabon

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    Selective logging is a major cause of forest degradation in the tropics, but its precise scale, location and timing are not known as wide-area, automated remote sensing methods are not yet available at this scale. This limits the abilities of governments to police illegal logging, or monitor (and thus receive payments for) reductions in degradation. Sentinel-1, a C-band Synthetic Aperture Radar satellite mission with a 12-day repeat time across the tropics, is a promising tool for this due to the known appearance of shadows in images where canopy trees are removed. However, previous work has relied on optical satellite data for calibration and validation, which has inherent uncertainties, leaving unanswered questions about the minimum magnitude and area of canopy loss this method can detect. Here, we use a novel bi-temporal LiDAR dataset in a forest degradation experiment in Gabon to show that canopy gaps as small as 0.02 ha (two 10 m × 10 m pixels) can be detected by Sentinel-1. The accuracy of our algorithm was highest when using a timeseries of 50 images over 20 months and no multilooking. With these parameters, canopy gaps in our study site were detected with a false alarm rate of 6.2%, a missed detection rate of 12.2%, and were assigned disturbance dates that were a good qualitative match to logging records. The presence of geolocation errors and false alarms makes this method unsuitable for confirming individual disturbances. However, we found a linear relationship (r2=0.74) between the area of detected Sentinel-1 shadow and LiDAR-based canopy loss at a scale of 1 hectare. By applying our method to three years’ worth of imagery over Gabon, we produce the first national scale map of small-magnitude canopy cover loss. We estimate a total gross canopy cover loss of 0.31 Mha, or 1.3% of Gabon’s forested area, which is a far larger area of change than shown in currently available forest loss alert systems using Landsat (0.022 Mha) and Sentinel-1 (0.019 Mha). Our results, which are made accessible through Google Earth Engine, suggest that this approach could be used to quantify the magnitude and timing of degradation more widely across tropical forests
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