Science of Remote Sensing最新文献

{"title":"Evaluation of limiting factors for SAR backscatter based cut detection of alpine grasslands","authors":"Felix Reuß ,&nbsp;Claudio Navacchi ,&nbsp;Isabella Greimeister-Pfeil ,&nbsp;Mariette Vreugdenhil ,&nbsp;Andreas Schaumberger ,&nbsp;Andreas Klingler ,&nbsp;Konrad Mayer ,&nbsp;Wolfgang Wagner","doi":"10.1016/j.srs.2024.100117","DOIUrl":"10.1016/j.srs.2024.100117","url":null,"abstract":"<div><p>Several studies utilized C-band Synthetic Aperture Radar (SAR) backscatter time series to detect cut events of grasslands. They identified several potential factors hindering the detection: Vegetation characteristics, precipitation, and the timing of salvage of the harvested grass. This study uses a comprehensive in situ database to assess the impact of those factors on the detection rate of cut events by performing a cut detection based on Sentinel-1 backscatter time series and relating the accuracy to the potentially limiting factors. The results can be summarized in the following key findings: (i) The detection rate decreases significantly with grass heights below 35 cm and a biomass of less than 2100 kg/ha. As the grass of the first growth is typically characterized by greater height and higher biomass, first cuts achieved a higher accuracy with 85% compared to re-growth cuts with 65%. (ii) False positive cut events were related to higher precipitation amounts, but adding precipitation data to the model led only to a slight increase of the accuracy of re-growth cuts, but a decrease of the overall accuracy. (iii) No relation was found between the timing of salvage and the backscatter behaviour. These insights contribute to a better utilization of C-band backscatter for vegetation analysis and agricultural applications, including cut detection. Further research with dense in situ measurements, including Vegetation Water Content (VWC) is required to fully understand the behaviour of C-band backscatter over managed grasslands.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"9 ","pages":"Article 100117"},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666017224000014/pdfft?md5=5169e32ba027860c6ed7bc448bdc6db0&pid=1-s2.0-S2666017224000014-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139394779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The apparent effect of orbital drift on time series of MODIS MOD10A1 albedo on the Greenland ice sheet","authors":"Shunan Feng ,&nbsp;Adrien Wehrlé ,&nbsp;Joseph Mitchell Cook ,&nbsp;Alexandre Magno Anesio ,&nbsp;Jason Eric Box ,&nbsp;Liane G. Benning ,&nbsp;Martyn Tranter","doi":"10.1016/j.srs.2023.100116","DOIUrl":"10.1016/j.srs.2023.100116","url":null,"abstract":"<div><p>The NASA MODIS MOD10A1 snow albedo product has enabled numerous glaciological applications. The temporal consistency of MODIS albedo is critical to obtaining reliable results from this 22-year time series. The orbit of Terra began to drift toward earlier acquisition times after the final inclination adjustment maneuver to maintain its nominal orbit by NASA on 27 February 2020, which may introduce biases that compromise the accuracy of quantitative time series analysis as the drift continues. Here, we evaluate the impact of Terra's orbital drift by comparing the differences between the Terra MODIS albedo and albedo products derived from Aqua MODIS, harmonized Landsat and Sentinel 2, Sentinel 3, and PROMICE (Programme for Monitoring of the Greenland Ice Sheet) ground measurements over the Greenland ice sheet. Our results suggest that the influence of orbital drift on albedo is small (+0.01 in 2020), but potentially biased for time series analysis. Our analysis also finds that the drift effect that causes earlier image acquisition time may lead to more apparently cloudy pixels and thus effectively reduce the Terra MODIS temporal resolution over Greenland.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"9 ","pages":"Article 100116"},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266601722300041X/pdfft?md5=d48b9fc900f42ff4d1fc165afa50f6b2&pid=1-s2.0-S266601722300041X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139015585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Benefit of incorporating GLASS remote sensing vegetation products in improving Noah-MP land surface temperature simulations on the Tibetan Plateau","authors":"Qing He ,&nbsp;Hui Lu ,&nbsp;Kun Yang ,&nbsp;Long Zhao ,&nbsp;Mijun Zou","doi":"10.1016/j.srs.2023.100115","DOIUrl":"10.1016/j.srs.2023.100115","url":null,"abstract":"<div><p>Land Surface Temperature (LST) is important for diagnosing surface energy balance in land surface models (LSMs). However, LST simulation in current LSMs tends to show large cold biases, partially due to the reason that the model's prescribed vegetation parameters (e.g., Leaf Area Index (LAI) and Fraction of Vegetation Cover (FVC)) are misrepresented, especially in regions with complex topography and climate such as Tibetan Plateau. Recent advancements in remote sensing technologies provide a unique opportunity to improve the model's vegetation parameters at large scales. In this study, we practice two experiments to improve LST simulations in Noah-MP LSM by (1) incorporating LAI and FVC from the Global Land Surface Satellite (GLASS) remote sensing product (exp_RS); and (2) incorporating an empirical LAI and FVC parameterization scheme based on the soil temperature stress factor (exp_RL02). Results show that the effect of vegetation on simulated LST is the most significant in summer season when the model-satellite LAI and FVC differences are the largest. Compared to the default experiment that uses static LAI and FVC values from the model's look-up table (exp_CTL), the results in exp_RS and exp_RL02 show domain-wide improvement of the simulated LST. The LAI and FVC effect on LST are also well reflected in model's energy budget components (i.e., longwave emissivity, sensible and latent heat fluxes, etc). Validation of the model simulated soil temperature with in-situ observations further demonstrate the model improvements. Our study underscores the important role of vegetation in regulating surface energy transfer processes. Our study also highlights the feasibility and benefit of incorporating remote sensing data in improving land surface model simulations.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"9 ","pages":"Article 100115"},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666017223000408/pdfft?md5=8d60fa7fef48059a31dddbb6ba3f8e25&pid=1-s2.0-S2666017223000408-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139013586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimation of fine-scale vegetation distribution information from RPAS-generated imagery and structure to aid restoration monitoring","authors":"Rik J.G. Nuijten ,&nbsp;Nicholas C. Coops ,&nbsp;Dustin Theberge ,&nbsp;Cindy E. Prescott","doi":"10.1016/j.srs.2023.100114","DOIUrl":"10.1016/j.srs.2023.100114","url":null,"abstract":"<div><p>Detailed maps of vegetation composition are vital for restoration planning, implementation, and monitoring, particularly at early stages of succession. This is usually accomplished through ground surveys, which can be costly and impractical depending on extent and accessibility, or conducted at too broad a spatial scale. In this study, we propose a methodology for mapping regenerating vegetation composition at 2 × 2 m² spatial resolution, using very high spatial resolution (&lt;1 m) remote sensing imagery obtained from remotely piloted aerial systems (RPAS) in conjunction with digital aerial photogrammetry (DAP) techniques for reconstructing vegetation structure. We applied logistic regression on multispectral orthomosaics, clusters of vegetation structure, and local illumination estimates to develop presence-absence models for eight key plant groups at various taxonomic levels as well as six plant functional types (conifer tree seedlings, grasses, tall- and low-growing forbs, shrubs, and mosses). Our results show higher accuracies for plant functional types (mean F-score = 0.67) compared to lower taxonomic levels (0.57). Notably, shrubs (F-score = 0.79), low-growing forbs (0.70), and mosses (0.69) exhibited the highest accuracies, while grasses (0.46), the aster family (Asteraceae spp; 0.48), and spruce seedlings (Picea spp; 0.54) demonstrated lower accuracies. Vegetation structure variables were identified as the most influential in the models, with mean NIRv ranking highest among spectral variables. High average ranks of spectral variation metrics (<em>e.g.,</em> standard deviation of NIRv) implied the influence of environmental determinants such as plant co-occurrences and micro-habitat conditions, which drive spectral variation. Discrete composition maps were produced for three restoration sites and analogous wildfire-disturbed sites. Plant compositions found at one site pair exhibited similarity (Bray-Curtis = 0.28), however, certain key plant groups covered larger extents of the restoration site than anticipated. Willows (Salix spp; 25.4% vs. 9.3%), which are typically planted for soil stabilization and obstruction, and clovers (Trifolium spp; 11.1% vs. 3.6%), which represent non-native agronomic vegetation, were prominent. The developed methodology facilitates the generation of detailed plant composition maps, aiding evaluations of vegetation patterns that are difficult to discern visually or through conventional field sampling. This approach can effectively help assess restoration goals and guide adaptive management strategies, especially when incorporating the expertise of restoration ecologists in understanding how different vegetation types affect habitat quality.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"9 ","pages":"Article 100114"},"PeriodicalIF":0.0,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666017223000391/pdfft?md5=ebb50b7c82813cd3ebc87db96d786e6f&pid=1-s2.0-S2666017223000391-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138987185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transferability of a Mask R–CNN model for the delineation and classification of two species of regenerating tree crowns to untrained sites","authors":"Andrew J. Chadwick ,&nbsp;Nicholas C. Coops ,&nbsp;Christopher W. Bater ,&nbsp;Lee A. Martens ,&nbsp;Barry White","doi":"10.1016/j.srs.2023.100109","DOIUrl":"https://doi.org/10.1016/j.srs.2023.100109","url":null,"abstract":"<div><p>Following harvest, monitoring reforestation success is a crucial component of sustainable management. In Alberta, Canada, like other jurisdictions, the efficiency of the current plot-based forest regeneration monitoring regime is challenged by the cost of accessibility and the declining availability of qualified field crews. Fine spatial resolution imagery and deep learning have been proposed as alternative monitoring tools and have proven successful under experimental conditions, yet how successfully models can be applied and transferred between a range of untrained sites and conditions remains unclear.</p><p>In this research, we repurposed a mask region-based convolutional neural network (Mask R–CNN) model that was previously trained to delineate coniferous tree crowns to instead segment instances of two species of regenerating conifers. We transferred learned parameters by replacing original single-class labels with photo-interpreted species information and retraining a selection of the network's parameters. We assessed the transferability of the new model by testing on five untrained sites, representing a range of forest types and densities typical of reforestation in the region. Results yielded a mean average precision (mAP) of 72% and average class F1 scores of 69% and 78% for lodgepole pine (<em>Pinus contorta</em>) and white spruce (<em>Picea glauca</em>), respectively, demonstrating successful transferability. We then investigated an additional transfer learning scenario by iteratively adding data from four of the five sites to the training set while reserving data from the remaining site for testing. On average, this improved mAP by 5%, lodgepole pine F1 by 7%, and white spruce F1 by 3%, and demonstrated that trained models can be continuously improved as sufficiently representative data becomes available.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"9 ","pages":"Article 100109"},"PeriodicalIF":0.0,"publicationDate":"2023-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666017223000342/pdfft?md5=d963dabefedb6b6224d4016cce94dd6a&pid=1-s2.0-S2666017223000342-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138564253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ionospheric compensation in L-band InSAR time-series: Performance evaluation for slow deformation contexts in equatorial regions","authors":"Léo Marconato ,&nbsp;Marie-Pierre Doin ,&nbsp;Laurence Audin ,&nbsp;Erwan Pathier","doi":"10.1016/j.srs.2023.100113","DOIUrl":"https://doi.org/10.1016/j.srs.2023.100113","url":null,"abstract":"<div><p>Multi-temporal Synthetic Aperture Radar Interferometry (MT-InSAR) is the only geodetic technique allowing to measure ground deformation down to mm/yr over continuous areas. Vegetation cover in equatorial regions favors the use of L-band SAR data to improve interferometric coherence. However, the electron content of ionosphere, affecting the propagation of the SAR signal, shows particularly strong spatio-temporal variations near the equator, while the dispersive nature of the ionosphere makes its effect stronger on low-frequencies, such as L-band signals. To tackle this problem, range split-spectrum method can be implemented to compensate the ionospheric phase contribution. Here, we apply this technique for time-series of ALOS-PALSAR data, and propose optimizations for low-coherence areas. To evaluate the efficiency of this method to retrieve subtle deformation rates in equatorial regions, we compute time-series using four ALOS-PALSAR datasets in contexts of low to medium coherence, showing slow deformation rates (mm/yr to cm/yr). The processed tracks are located in Ecuador, Trinidad and Sumatra, and feature 15 to 19 acquisitions including very high, dominating ionospheric noise, corresponding to equivalent displacements of up to 2 m. The correction method performs well and allows to reduce drastically the noise level due to ionosphere, with significant improvement compared with a simple plane fitting method. This is due to frequent highly non-linear patterns of perturbation, characterizing equatorial TEC distribution. We use semivariograms to quantify the uncertainty of the corrected time-series, highlighting its dependence on spatial distance. Thus, using ALOS-PALSAR-like archive, one can expect a detection threshold on the Line-of-Sight velocity ranging between 3 and 6 mm/yr, depending on the spatial wavelength of the signal to be observed. These values are consistent with the accuracy derived from the comparison of velocities between two tracks in their overlapping area. In the case studies that we processed, the time-series corrected from ionosphere allows to retrieve accurately fault creep and volcanic signal but it is still too noisy for retrieving tiny long-wavelength signals such as slow (mm/yr) interseismic strain accumulation.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"9 ","pages":"Article 100113"},"PeriodicalIF":0.0,"publicationDate":"2023-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266601722300038X/pdfft?md5=aaf2f17c83d11f22172dc067333abb6f&pid=1-s2.0-S266601722300038X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138548949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quadtree decomposition-based Deep learning method for multiscale coastline extraction with high-resolution remote sensing imagery","authors":"Shuting Sun ,&nbsp;Lin Mu ,&nbsp;Ruyi Feng ,&nbsp;Yifu Chen ,&nbsp;Wei Han","doi":"10.1016/j.srs.2023.100112","DOIUrl":"https://doi.org/10.1016/j.srs.2023.100112","url":null,"abstract":"<div><p>As one of the most critical features on the earth's surface, coastal zone mandates high-quality extraction of its representative feature, the coastline. Prior methodologies primarily emphasize on edge and small-scale information. However, during large-scale image processing, misclassification might occur due to the difficulty in determining whether a local area belongs to the land or sea. To address this, we propose a deep learning-based multiscale coastline extraction algorithm in this study. It comprises a multiscale coastal zone dataset built upon a tile map service structure and a scene classification-based multiscale coastal zone classifier, employing quadtree decomposition to identify coastal zones from low to high levels. Contrasting with conventional semantic segmentation, the scene classification network, owing to its larger receptive field, can accurately discern land and sea. This accuracy is further enhanced by using quadtree decomposition to process images with lower resolution and larger coverage. The results suggest that our proposed method effectively eliminates confusing features, with the overall experimental classification accuracy attesting to the effectiveness of our approach, yielding a 6% improvement. Moreover, the screening process in this study significantly reduces the number of input samples for the segmentation network, thus boosting computational speed.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"9 ","pages":"Article 100112"},"PeriodicalIF":0.0,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666017223000378/pdfft?md5=7739ed796c1dca456ac566975383dc38&pid=1-s2.0-S2666017223000378-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138564252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A fully automatic framework for sub-pixel mapping of thermokarst lakes using Sentinel-2 images","authors":"Yuanyuan Qin ,&nbsp;Chengyuan Zhang ,&nbsp;Ping Lu","doi":"10.1016/j.srs.2023.100111","DOIUrl":"https://doi.org/10.1016/j.srs.2023.100111","url":null,"abstract":"<div><p>Mapping and monitoring thermokarst lakes are crucial to understanding the impact of climate change on permafrost regions and quantifying permafrost-related carbon emissions. Several automatic methods based on remote sensing images have been developed for thermokarst lake mapping. However, mixed pixels containing both land and water characteristics in the lakeshore zones pose a significant challenge to the accuracy of these methods. Furthermore, few approaches were able to fully automate the identification of thermokarst lakes without the manual training sample selection or parameter tuning. In this study, we present a fully automatic framework for thermokarst lake mapping using moderate-resolution Sentinel-2 images. The proposed method combines multidimensional hierarchical clustering and sub-pixel mapping (SPM) based on the radial basis function (RBF) interpolation and Markov random field (MRF) (referred to as RBF-then-MRF SPM), so as to achieve thermokarst lake mapping at a spatial resolution of 3.3 m. We apply the proposed method to two representative thermokarst lake distribution regions in the Northern Hemisphere and achieve a mean Kappa coefficient of 0.89 and 0.99, and a mean <span><math><mrow><mi>Q</mi><mi>u</mi><mi>a</mi><mi>l</mi><mi>i</mi><mi>t</mi><mi>y</mi></mrow></math></span> of 89.86% and 96.60% on the central Tibetan Plateau and the northern Seward Peninsula, respectively. The results demonstrate that the proposed method significantly improves the accuracy of mixed pixel extraction, and the automatic thermokarst lake mapping is applicable to diverse permafrost regions.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"8 ","pages":"Article 100111"},"PeriodicalIF":0.0,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666017223000366/pdfft?md5=29b03562ac6bc0dd13ab7087a2a60169&pid=1-s2.0-S2666017223000366-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138413724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling tree biomass using direct and additive methods with point cloud deep learning in a temperate mixed forest","authors":"Harry Seely ,&nbsp;Nicholas C. Coops ,&nbsp;Joanne C. White ,&nbsp;David Montwé ,&nbsp;Lukas Winiwarter ,&nbsp;Ahmed Ragab","doi":"10.1016/j.srs.2023.100110","DOIUrl":"https://doi.org/10.1016/j.srs.2023.100110","url":null,"abstract":"<div><p>Airborne laser scanning (ALS) data has been widely used for total aboveground tree biomass (AGB) modelling, however, there is less research focusing on estimating specific tree biomass components (wood, branches, bark, and foliage). Knowledge about these biomass components is essential for carbon accounting, understanding forest nutrient cycling, and other applications. In this study, we compare additive AGB estimation (sum of estimated components) with direct AGB estimation using deep neural network (DNN) and random forest (RF) models. We utilise two point cloud DNNs: point-based Dynamic Graph Convolutional Neural Network (DGCNN) and Octree-based Convolutional Neural Network (OCNN). DNN and RF models were trained using a dataset comprised of 2336 sample plots from a mixed temperate forest in New Brunswick, Canada. Results indicate that additive AGB models perform similarly to direct models in terms of coefficient of determination (R²) and root-mean square error (RMSE), and reduced the mean absolute percentage error (MAPE) by 22% on average. Compared to RF, the DNNs provided a small improvement in performance, with OCNN explaining 5% more variation in the data (R² = 0.76) and reducing MAPE by 20% on average. Overall, this study showcases the effectiveness of additive tree AGB models and highlights the potential of DNNs for enhanced AGB estimation. To further improve DNN performance, we recommend using larger training datasets, implementing hyperparameter optimization, and incorporating additional data such as multispectral imagery.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"8 ","pages":"Article 100110"},"PeriodicalIF":0.0,"publicationDate":"2023-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666017223000354/pdfft?md5=a43818cd94d3610e1df7b41e142ca45c&pid=1-s2.0-S2666017223000354-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138328129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping subcanopy light regimes in temperate mountain forests from Airborne Laser Scanning, Sentinel-1 and Sentinel-2","authors":"Felix Glasmann ,&nbsp;Cornelius Senf ,&nbsp;Rupert Seidl ,&nbsp;Peter Annighöfer","doi":"10.1016/j.srs.2023.100107","DOIUrl":"https://doi.org/10.1016/j.srs.2023.100107","url":null,"abstract":"<div><p>Sunlight is the primary source of energy in forest ecosystems and subcanopy light regimes largely determine the establishment, growth and dispersal of plants and thus forest floor plant communities. Subcanopy light regimes are highly variable in both space and time, which makes monitoring them challenging. In this study, we assess the potential of Sentinel-1 and Sentinel-2 time series for predicting subcanopy light regimes in temperate mountain forests. We trained different random forest regression models predicting field-measured total site factor (TSF, proportion of potential direct and diffuse solar radiation reaching the forest floor, here defined as the transition zone between belowground and aboveground biomass) from a set of metrics derived from Sentinel-1 and Sentinel-2 time series. Model performance was benchmarked against a model based on structural metrics derived from Airborne Laser Scanning (ALS) data, serving as an empirical gold-standard in modelling subcanopy light regimes. We found that Sentinel-1 and Sentinel-2 time series performed nearly as good as the model based on high-resolution ALS data (R²/RMSE of 0.80/0.11 for Sentinel-1/2 compared to R²/RMSE of 0.90/0.08 for ALS). We furthermore tested the generalizability of the trained models to two new sites not used for training for which field data was available for validation. Prediction accuracy for the ALS model decreased substantially for the two independent test sites due to variable ALS data quality and acquisition date (ΔR²/ΔRMSE of 0.29/0.05 and 0.11/0.03 for both independent test sites). The prediction accuracy of the Sentinel-1/2 model, however, remained more stable (ΔR²/ΔRMSE of 0.13/0.02 and 0.13/0.04). We therefore conclude that a combination of Sentinel-1 and Sentinel-2 time series has the potential to map subcanopy light conditions spatially and temporally independent of the availability of high-resolution ALS data. This has important implications for the operational monitoring of forest ecosystems across large scales, which is often limited by the challenges related to acquiring airborne datasets.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"8 ","pages":"Article 100107"},"PeriodicalIF":0.0,"publicationDate":"2023-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666017223000329/pdfft?md5=53b9b222ff1a8f66976f9fdce9509eb1&pid=1-s2.0-S2666017223000329-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134656795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}