Science of Remote Sensing最新文献

{"title":"Remote sensing reveals how armed conflict regressed woody vegetation cover and ecosystem restoration efforts in Tigray (Ethiopia)","authors":"Emnet Negash ,&nbsp;Emiru Birhane ,&nbsp;Aster Gebrekirstos ,&nbsp;Mewcha Amha Gebremedhin ,&nbsp;Sofie Annys ,&nbsp;Meley Mekonen Rannestad ,&nbsp;Daniel Hagos Berhe ,&nbsp;Amare Sisay ,&nbsp;Tewodros Alemayehu ,&nbsp;Tsegai Berhane ,&nbsp;Belay Manjur Gebru ,&nbsp;Negasi Solomon ,&nbsp;Jan Nyssen","doi":"10.1016/j.srs.2023.100108","DOIUrl":"10.1016/j.srs.2023.100108","url":null,"abstract":"<div><p>In recent years, armed conflicts are globally on the rise, causing drastic human and environmental harm. The Tigray war in Ethiopia is one of the recent violent conflicts that has abruptly reversed decades of ecosystem restoration efforts. This paper analyzes changes in woody vegetation cover during the period of armed conflict (2020–2022) using remote sensing techniques, supplemented by field testimony and secondary data. Extent of woody vegetation cover was analyzed using Normalized Difference Vegetation Index (NDVI) thresholding method from Sentinel 2 images in Google Earth Engine, and scale of de-electrification was qualitatively analyzed from Black Marble HD nighttime lights dataset, acquired from NASA's Black Marble team. The magnitude, direction as well as the mechanisms of change in woody vegetation cover varied across the region and over time. Tigray's woody vegetation cover fluctuated within 20% of the landmass. Mainly scattered to mountainous areas, the dry Afromontane forest cover declined from about 17% in 2020 to 15% in 2021, and 12% in 2022. About 17% of the overall decline was observed between 500 m and 2000 m elevation, where there is higher anthropogenic pressure. Land restoration practices meant to avert land degradation and desertification were interrupted and the area turned warfare ground. In many areas, forests were burned, the trees cut and the area became barren. The suspension of public services such as electricity for household or industrial use created heavy reliance on firewood and charcoal, further threatening to compound weather and climate. The magnitude of disturbance in a region that is already at a very high risk of desertification requires urgent national and international attention. Continued ecosystem disturbance could eventually make the domain part of a wider desert connecting the Sahel to the Afar Triangle, a scenario which may render the area uninhabitable.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"8 ","pages":"Article 100108"},"PeriodicalIF":0.0,"publicationDate":"2023-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666017223000330/pdfft?md5=b658e500bf436724e013108160162f3f&pid=1-s2.0-S2666017223000330-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135664405","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":"Computational tools for assessing forest recovery with GEDI shots and forest change maps","authors":"Amelia Holcomb,&nbsp;Simon V. Mathis,&nbsp;David A. Coomes,&nbsp;Srinivasan Keshav","doi":"10.1016/j.srs.2023.100106","DOIUrl":"https://doi.org/10.1016/j.srs.2023.100106","url":null,"abstract":"<div><p>Tropical secondary forests are ecosystems of critical importance for protecting biodiversity, buffering primary forest loss, and sequestering atmospheric carbon. Monitoring growth and carbon sequestration in secondary forests is difficult, with inventory plots sampling <span><math><mo>&lt;</mo><mn>0.001</mn><mi>%</mi></math></span> of secondary forests. The Global Ecosystem Dynamics Investigation (GEDI), a space-borne LiDAR sampler, provides billions of aboveground carbon density (ACD) estimates across the tropics. We fuse these carbon density estimates with a time series of forest change maps to identify their age since last deforestation and thus estimate the average rate of carbon sequestration in secondary forests across the Amazon biome. To our knowledge, this is the first estimate of these rates made using the new GEDI dataset. Moreover, this paper addresses key statistical and computational challenges of GEDI data fusion and analysis. We propagate both GEDI ACD and geolocation uncertainty to the regrowth rate estimate through a Monte Carlo approach, and we handle heteroskedasticity, outliers, and spatial autocorrelation using robust statistical methods. The large size of the GEDI dataset combined with the proposed Monte Carlo bootstrap can be highly computationally intensive, with a naive implementation taking over a month to complete. Nevertheless, we demonstrate the feasibility of our method by developing optimized open-source code that performs this computation on the 151 million quality-filtered GEDI shots available for the Amazon biome from April 2019–August 2021 in under 25 min in benchmark tests. By resolving these statistical and computational challenges with an efficient open-source pipeline, we create a standard approach that can be used more broadly in any work seeking to combine the GEDI dataset with high-resolution classification maps. Using this approach, we identify approximately 23, 000 GEDI samples of regrowing forest at least 60 m × 60 m wide across the Amazon biome and estimate a carbon sequestration rate of 1.86 MgC/ha/yr with a 95% empirical confidence interval of 1.75–1.97 MgC/ha/yr, with rates varying from 1.27 to 1.99 MgC/ha/yr across smaller subregions.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"8 ","pages":"Article 100106"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666017223000317/pdfft?md5=c204d42ed35e17b3f90e3691a0597edf&pid=1-s2.0-S2666017223000317-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134656939","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 new variant of the optical trapezoid model (OPTRAM) for remote sensing of soil moisture and water bodies","authors":"Morteza Sadeghi ,&nbsp;Neda Mohamadzadeh ,&nbsp;Lan Liang ,&nbsp;Uditha Bandara ,&nbsp;Marcellus M. Caldas ,&nbsp;Tyler Hatch","doi":"10.1016/j.srs.2023.100105","DOIUrl":"https://doi.org/10.1016/j.srs.2023.100105","url":null,"abstract":"<div><p>Over the past few years, the Optical Trapezoid Model (OPTRAM) has been widely used as a means for high-resolution mapping of surface soil moisture using optical satellite data. In this paper, we propose a new variant of OPTRAM that can map not only soil moisture, but also water bodies such as lakes and rivers. The proposed variant was tested using laboratory experimental data as well as Landsat-8 reflectance observations. Results showed the new OPTRAM variant has greater skill than the original variant in separating land and water pixels. In addition, the new variant showed less sensitivity to the model parameters, and hence, is less user dependent. To quantitatively examine the user-dependency of the model, we analyzed OPTRAM soil moisture based on Landsat-8 satellite images in California, where we varied the model parameters in a plausible range. The correlations of the resulting maps in terms of R² between two largely different sets of parameters were found in the range of 0.47-0.52 for the original variant and 0.67-0.76 for the new variant. Because some OPTRAM parameters can be quite uncertain, particularly in wet regions, the reduced sensitivity promises more consistent soil moisture estimates across the range of parameter choices.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"8 ","pages":"Article 100105"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49896275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Remotely sensed estimation of root-zone salinity in salinized farmland based on soil-crop water relations","authors":"Guang Yang ,&nbsp;Xuejin Qiao ,&nbsp;Qiang Zuo ,&nbsp;Jianchu Shi ,&nbsp;Xun Wu ,&nbsp;Lining Liu ,&nbsp;Alon Ben-Gal","doi":"10.1016/j.srs.2023.100104","DOIUrl":"https://doi.org/10.1016/j.srs.2023.100104","url":null,"abstract":"<div><p>Accurate monitoring and evaluation of root-zone soil salt content (<em>SSC</em>) are critical for sustainable development of irrigated agriculture in arid and semi-arid areas. Based on soil-crop water relations and farmland evapotranspiration (<em>ET</em>) fused through remote sensing data, this study developed an inversion method to estimate root-zone <em>SSC</em> using a case study from cotton fields under film mulched drip irrigation (CFFMDI) in the Manas River Basin (MRB) over 21 years (2000–2020). Two hypotheses were set as: (1) relative transpiration can be approximated by relative <em>ET</em>; and (2) the soil water stress response function is linearly proportional to the ratio of relative water supply. Measured data from a field experiment and collected data from regional survey and literature retrieval were used to optimize parameters and verify the hypotheses and method. The method was then applied to analyze the spatial and temporal distribution characteristics and cumulative effects of root-zone <em>SSC</em>. Results showed that the hypotheses and the method were reasonable and reliable in estimating root-zone <em>SSC</em> (with coefficient of determination <em>R</em>² &gt; 0.50). Along with the popularization of film-mulched drip irrigation and the expansion of CFFMDI over the past 21 years, regional-scale root-zone <em>SSC</em> declined significantly with an annual attenuation rate of about 0.09 g kg⁻¹. Due to the gradual reduction of irrigation amount per unit area, the decline was more rapid before 2011 (0.18 g kg⁻¹), but slightly slowed down or even reversed at the end of the second decade (2015–2020). By 2020, the mean regional root-zone <em>SSC</em> reached 3.93 g kg⁻¹. At the beginning of this century, MRB was mainly composed of mildly- (59.8%) and moderately-salinized CFFMDI (39.9%). However, by 2020, non- (69.7%) and mildly-salinized cotton field (28.2%) dominated the basin. The inversion method of root-zone <em>SSC</em> fully considers the water consumption mechanism of soil-crop system, thus shows great potential in effective planning and management of soil and water resources in arid salinized areas such as MRB.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"8 ","pages":"Article 100104"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49904702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The 50-year Landsat collection 2 archive","authors":"Christopher J. Crawford ,&nbsp;David P. Roy ,&nbsp;Saeed Arab ,&nbsp;Christopher Barnes ,&nbsp;Eric Vermote ,&nbsp;Glynn Hulley ,&nbsp;Aaron Gerace ,&nbsp;Mike Choate ,&nbsp;Christopher Engebretson ,&nbsp;Esad Micijevic ,&nbsp;Gail Schmidt ,&nbsp;Cody Anderson ,&nbsp;Martha Anderson ,&nbsp;Michelle Bouchard ,&nbsp;Bruce Cook ,&nbsp;Ray Dittmeier ,&nbsp;Danny Howard ,&nbsp;Calli Jenkerson ,&nbsp;Minsu Kim ,&nbsp;Tania Kleyians ,&nbsp;Steve Zahn","doi":"10.1016/j.srs.2023.100103","DOIUrl":"https://doi.org/10.1016/j.srs.2023.100103","url":null,"abstract":"<div><p>The Landsat global consolidated data archive now exceeds 50 years. In recognition of the need for consistently processed data across the Landsat satellite series, the U.S. Geological Survey (USGS) initiated collection-based processing of the entire archive that was processed as Collection 1 in 2016. In preparation for the data from the now successfully launched Landsat 9, the USGS reprocessed the Landsat archive as Collection 2 in 2020. This paper describes the rationale for, and the contents and advancements provided by Collection 2, and highlights the differences between the Collection 1 and Collection 2 products. Notably, the Collection 2 products have improved geolocation and, for the first time, the USGS provides a global inventory of Level 2 surface reflectance and surface temperature products. Also for the first time, the USGS used a commercial cloud computing architecture to efficiently process the archive and enable direct cloud access of the Landsat products. The paper concludes with discussion of likely improvements expected in Collection 3 in preparation for the Landsat Next mission that is planned for launch in the early 2030s.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"8 ","pages":"Article 100103"},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49896276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving wildland fire spread prediction using deep U-Nets","authors":"Fadoua Khennou,&nbsp;Moulay A. Akhloufi","doi":"10.1016/j.srs.2023.100101","DOIUrl":"https://doi.org/10.1016/j.srs.2023.100101","url":null,"abstract":"<div><p>Forest fires are able to cause significant damage to humans and the earth's fauna and flora. If a fire is not detected and extinguished before it spreads, it can have disastrous results. In addition to satellite images, recent studies have shown that exploring both weather and topography characteristics is crucial for effectively predicting the propagation of wildfires. In this paper, we present FU-NetCastV2, a deep learning convolutional neural network for fire spread and burned area mapping. This algorithm predicts which areas around wildfires are at high risk of future spread. With an accuracy of 94.6% and an AUC of 97.7%, the model surpassed the literature by 3.7% and exhibited a 1.9% improvement over our previous model. The proposed approach was implemented using consecutive forest wildfire perimeters, satellite images, Digital Elevation Model maps, aspect, slope and weather data.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"8 ","pages":"Article 100101"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49896272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive review of spatial-temporal-spectral information reconstruction techniques","authors":"Qunming Wang ,&nbsp;Yijie Tang ,&nbsp;Yong Ge ,&nbsp;Huan Xie ,&nbsp;Xiaohua Tong ,&nbsp;Peter M. Atkinson","doi":"10.1016/j.srs.2023.100102","DOIUrl":"https://doi.org/10.1016/j.srs.2023.100102","url":null,"abstract":"<div><p>Fine spatial resolution remote sensing images are crucial sources of data for monitoring the Earth's surface. Due to defects in sensors and the complicated imaging environment, however, fine spatial resolution images always suffer from various degrees of information loss. According to the basic attributes of remote sensing images, the information loss generally falls into three dimensions, that is, the spatial, temporal and spectral dimensions. In recent decades, many methods have been developed to cope with this information loss problem in the three dimensions, which are termed spatial reconstruction, temporal reconstruction and spectral reconstruction in this paper. This paper presents a comprehensive review of all three types of reconstruction. First, a systematic introduction and review of the achievements is provided, including the refined general mathematical framework and diagram for each of the three parts. Second, the applications in various areas (e.g., meteorology, ecology and environmental science) are introduced. Third, the challenges and recent advances of spatial-temporal-spectral information reconstruction are summarized, such as the efforts for dealing with abrupt land cover changes in spatial reconstruction, inconsistency in multi-scale data acquired by different sensors in temporal reconstruction, and point spread function (PSF) effect in spectral reconstruction. Finally, several thoughts are given for future prospects.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"8 ","pages":"Article 100102"},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49896274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PROSPECT-GPR: Exploring spectral associations among vegetation traits in wavelength selection for leaf mass per area and water contents","authors":"Chunmei He ,&nbsp;Jia Sun ,&nbsp;Yuwen Chen ,&nbsp;Lunche Wang ,&nbsp;Shuo Shi ,&nbsp;Feng Qiu ,&nbsp;Shaoqiang Wang ,&nbsp;Jian Yang ,&nbsp;Torbern Tagesson","doi":"10.1016/j.srs.2023.100100","DOIUrl":"https://doi.org/10.1016/j.srs.2023.100100","url":null,"abstract":"<div><p>Leaf mass per area (LMA) and equivalent water thickness (EWT) are key indicators providing information on plant growth status and agricultural management, and their retrieval is commonly done through radiative transfer models (RTMs) such as the PROSPECT model. However, the PROSPECT model is frequently hampered by the ill-posed problem as a consequence of measurement and model uncertainties. Here, we propose a wavelength selection method to improve the inversion of EWT and LMA by integrating PROSPECT with a machine learning algorithm (Gaussian process regression (GPR); PROSPECT-GPR for short). The GPR model conducted sorting of wavelengths and the PROSPECT-D was used to determine the optimal number of characteristic wavelengths. The results demonstrated that the estimation of EWT (R² = 0.80; RMSE = 0.0021) and LMA (R² = 0.71; RMSE = 0.0021) using the proposed wavelengths and PROSPECT inversion all exhibited superior accuracy in comparison with those from previous studies. The efficacy of PROSPECT-GPR in exploring the spectral linkage among vegetation traits was demonstrated by selecting wavelengths associated with leaf structure parameter N and EWT (1368 nm) that turn out to contribute to the estimation of LMA. The findings lay a strong foundation for understanding the spectral linkage among vegetation traits, and the proposed wavelength selection method provides valuable insights for selecting informative spectral wavelengths for RTMs inversion and designing future remote sensors.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"8 ","pages":"Article 100100"},"PeriodicalIF":0.0,"publicationDate":"2023-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49896273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accuracy and consistency of space-based vegetation height maps for forest dynamics in alpine terrain","authors":"Yuchang Jiang ,&nbsp;Marius Rüetschi ,&nbsp;Vivien Sainte Fare Garnot ,&nbsp;Mauro Marty ,&nbsp;Konrad Schindler ,&nbsp;Christian Ginzler ,&nbsp;Jan D. Wegner","doi":"10.1016/j.srs.2023.100099","DOIUrl":"https://doi.org/10.1016/j.srs.2023.100099","url":null,"abstract":"<div><p>Monitoring and understanding forest dynamics is essential for environmental conservation and management. This is why the Swiss National Forest Inventory (NFI) provides countrywide vegetation height maps at a spatial resolution of 0.5 <em>m</em>. Its long update time of 6 years, however, limits the temporal analysis of forest dynamics. This can be improved by using spaceborne remote sensing and deep learning to generate large-scale vegetation height maps in a cost-effective way. In this paper, we present an in-depth analysis of these methods for operational application in Switzerland. We generate annual, countrywide vegetation height maps at a 10-m ground sampling distance for the years 2017–2020 based on Sentinel-2 satellite imagery. In comparison to previous works, we conduct a large-scale and detailed stratified analysis against a precise Airborne Laser Scanning reference dataset. This stratified analysis reveals a close relationship between the model accuracy and the topology, especially slope and aspect. We assess the potential of deep learning-derived height maps for change detection and find that these maps can indicate changes as small as 250 <em>m</em>². Larger-scale changes caused by a winter storm are detected with an F1-score of 0.77. Our results demonstrate that vegetation height maps computed from satellite imagery with deep learning are a valuable, complementary, cost-effective source of evidence to increase the temporal resolution for national forest assessments.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"8 ","pages":"Article 100099"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49896277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of a forest radiative transfer model using an extensive boreal forest inventory database","authors":"Ranjith Gopalakrishnan ,&nbsp;Lauri Korhonen ,&nbsp;Matti Mõttus ,&nbsp;Miina Rautiainen ,&nbsp;Aarne Hovi ,&nbsp;Lauri Mehtätalo ,&nbsp;Matti Maltamo ,&nbsp;Heli Peltola ,&nbsp;Petteri Packalen","doi":"10.1016/j.srs.2023.100098","DOIUrl":"https://doi.org/10.1016/j.srs.2023.100098","url":null,"abstract":"<div><p>The forest reflectance and transmittance model (FRT) is applicable over a wide swath of boreal forest landscapes mainly because its stand-specific inputs can be generated from standard forest inventory variables. We quantified the accuracy of this model over an extensive region for the first time. This was done by carrying out a simulation study over a large number (12,369) of georeferenced forest plots from operational forest management inventories conducted in Southern Finland. We compared the FRT simulated bidirectional reflectance factors (BRF) with those measured by Landsat 8 satellite Operational Land Imager (OLI). We also quantified the relative importance of several explanatory factors that affected the magnitude of the discrepancy between the measured and simulated BRFs using a linear mixed effects modelling framework. A general trend of FRT overestimating BRFs is seen across all tree species and spectral bands examined: up to ∼0.05 for the red band, and ∼0.10 for the near infrared band. The important explanatory factors associated with the overestimations included the dominant tree species, understory type of the forest plot, timber volume (acts as a proxy for stand maturity), vegetation heterogeneity and time of the year. Our analysis suggests that approximately 20% of the error is caused by the non-representative spectra of canopy foliage and understory. Our results demonstrate the importance of collecting representative spectra from a diverse set of forest stands, and over the full range of seasons.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"8 ","pages":"Article 100098"},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49845051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}