Remote Sensing of Environment最新文献

{"title":"Glacier mass change and evolution of Petrov Lake in the Ak-Shyirak massif, central Tien Shan, from 1973 to 2023 using multisource satellite data","authors":"Yingzheng Wang ,&nbsp;Donghai Zheng ,&nbsp;Yushan Zhou ,&nbsp;Yanyun Nian ,&nbsp;Shanshan Ren ,&nbsp;Weiwei Ren ,&nbsp;Zhongzheng Zhu ,&nbsp;Zhiguang Tang ,&nbsp;Xin Li","doi":"10.1016/j.rse.2024.114437","DOIUrl":"10.1016/j.rse.2024.114437","url":null,"abstract":"<div><div>Warming in the Third Pole region accelerates glacier and snow melt, leading to a rise in glacial lake numbers and sizes. However, accurately measuring their water level changes poses challenges, hindering precise volume assessments and evaluation of glacier mass balance contributions. Here, we took the Ak-Shyirak glaciers and the largest Petrov proglacial lake in the Central Tien Shan as a case study to investigate these phenomena. Specifically, firstly, we conducted mass balance assessments for the Ak-Shyirak massif for six sub-periods from 1973 to 2023 using KH-9 DEMs, SRTM DEM, and ASTER DEMs. The results indicate that glaciers were in a state of rapid melting for 1980–2000 and 2005–2012, with rates of −0.46 m w.e./a and − 0.37 m w.e./a; moderate melting during 1973–1980 and 2012–2018, with rates of −0.26 m w.e./a and − 0.28 m w.e./a, while slower melting during 2000–2005 and 2018–2023, with rates of −0.08 m w.e./a and − 0.18 m w.e./a. Subsequently, we conducted assessments of the area change of Petrov Lake for 1973–2023 using KH-9 and Landsat images. The results reveal a significant increase in the glacial lake area by 2.81 km² (150.25 %), corresponding to a rate of 0.054 km²/a over the entire study period. Furthermore, we conducted monitoring of Petrov Lake's water level from 2019 to 2023 by utilizing ICESat-2 laser altimetry and Sentinel-3 radar altimetry data. Our findings indicate that the glacial lake level shows intra-annual fluctuations and inter-annual change, with amplitudes of 0.67 ± 0.09 m and increase rate of 0.30 ± 0.05 m/a, respectively, as determined by a periodic fluctuation model. Finally, after a comprehensive analysis of ERA5-Land meteorological data, topography, glacier mass balance, lake area, and water level, we can draw the following conclusions: (1) glacier mass balance is predominantly influenced by the air temperature and snowfall; (2) changes in glacial lake area are driven by factors such as the lake basin, glacier surface elevation, and drainage event; (3) intra-annual fluctuations and inter-annual change in glacial lake levels are both primarily influenced by precipitation and glacier mass balance; (4) glacier mass balance accounts for (36.19 ± 8.47)% of the water supply contributing to changes in glacial lake volume change, while precipitation represents (63.81 ± 5.08)%. Glacier mass balance measurements reveal changing patterns in the Ak-Shyirak massif, Central Tien Shan, due to climate change. Inaugural proglacial lake level measurements provide unique insights into both intra-annual and inter-annual changes, serving as a reference for Third Pole region-wide glacial lake monitoring. Additionally, quantifying glacier meltwater contributions to lake volumes will aid future glacial lake evaluation and potential outburst flood impacts.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114437"},"PeriodicalIF":11.1,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142444363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coupling sun-induced chlorophyll fluorescence (SIF) with soil-plant-atmosphere research (SPAR) chambers to advance applications of SIF for crop stress research","authors":"C.Y. Chang ,&nbsp;M.A. Hassan ,&nbsp;T. Julitta ,&nbsp;A. Burkart","doi":"10.1016/j.rse.2024.114462","DOIUrl":"10.1016/j.rse.2024.114462","url":null,"abstract":"<div><div>Sun-induced chlorophyll fluorescence (SIF) has recently emerged as a proxy for canopy photosynthesis of vegetation and offers a promising approach for scalable remote crop monitoring. Effective application of SIF for crop monitoring requires better understanding of the processes that cause SIF-photosynthesis decoupling at leaf and canopy scales. To answer this challenge, we developed a novel automated multi-targeting hyperspectral spectrometer (OctoFlox). First, we evaluated the performance of OctoFlox and found high stability and cross-channel comparability. Second, we performed an evaluation of different SIF retrieval methods to identify the best suited retrieval method for our system configuration for both red (SIF_Red) and far-red SIF (SIF_FR). We then deployed OctoFlox within Soil-Plant Atmosphere Research (SPAR) controlled-environment chambers that enable measurement of canopy-scale SIF and photosynthesis with matching footprints. We analyzed the effect of the SPAR chamber tops on the light environment and found minimal impact on the spectral response. Lastly, we examined the response of SIF and canopy photosynthesis using the SPAR chambers. Soybean plants were evaluated at pre-drought, drought (irrigated at 100 % field capacity vs. 33 % field capacity for 2 weeks) and after 1 week recovery from drought. During early growing season, SIF_FR and SIF_Red exhibited similar responses. At peak growing season (R2 growth stage), SIF_FR increased during afternoon depression of photosynthesis, but SIF_Red decreased. We demonstrate that pairing SIF instrumentation with SPAR chambers can accelerate understanding SIF-photosynthesis relationships from diurnal to seasonal scales in relation to crop physiological responses to abiotic stress. We provide user recommendations for future applications using OctoFlox and SPAR chambers for co-measuring SIF and GPP.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114462"},"PeriodicalIF":11.1,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142439582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Filling GRACE data gap using an innovative transformer-based deep learning approach","authors":"Longhao Wang ,&nbsp;Yongqiang Zhang","doi":"10.1016/j.rse.2024.114465","DOIUrl":"10.1016/j.rse.2024.114465","url":null,"abstract":"<div><div>The terrestrial water storage anomaly (TWSA), derived from the Gravity Recovery and Climate Experiment (GRACE) and its successor, the GRACE Follow-on (GRACE-FO) satellite, presents a remarkable opportunity for extreme weather detection and the enhancement of environmental protection. However, the practical utility of GRACE data is challenged by an 11-month data gap and several months of missing data. To address this limitation, we have developed an innovative transformer-based deep learning model for data gap-filling. This model incorporates a self-attention mechanism using causal convolution, allowing the neural network to capture the local context of GRACE time series data. It takes into account various factors such as temperature (T), precipitation (P), and evapotranspiration (ET). We trained the model using a global dataset of 10,000 time series pixels and applied it to fill all the time gaps. The validation results demonstrate its robustness, with an average root mean square error (RMSE) of 6.18 cm and Nash-Sutcliffe efficiency (NSE) of 0.906. Notably, the Transformer-based method outperforms other state-of-the-art approaches in arid regions. The incorporation of T, P, and ET has further enhanced the accuracy of gap filling, with an average RMSE decrease of 7.5 %. This study has produced a reliable gap-filling product that addresses 11-month data gaps and 24 isolated gaps, ensuring the continuity of GRACE data for various scholarly applications. Moreover, our Transformer approach holds important potential for surpassing traditional methods in predicting and filling gaps in remote sensing data and gridded observations.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114465"},"PeriodicalIF":11.1,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142439583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impacts of pine species, infection response, and data type on the detection of Bursaphelenchus xylophilus using close-range hyperspectral remote sensing","authors":"Jie Pan ,&nbsp;Xinquan Ye ,&nbsp;Fan Shao ,&nbsp;Gaosheng Liu ,&nbsp;Jia Liu ,&nbsp;Yunsheng Wang","doi":"10.1016/j.rse.2024.114468","DOIUrl":"10.1016/j.rse.2024.114468","url":null,"abstract":"<div><div>The early detection of forest pests and diseases is a primary focus of remote sensing applications for forest health monitoring. Pine Wilt Disease (PWD), which causes significant damage to pine resources in many countries and regions, has been a key area where the close-range hyperspectral remote sensing has demonstrated its advantages for early diagnosis. However, it remains unclear whether PWD can be detected during the pre-visual stage and, if so, how to achieve hyperspectral detection. This study aimed to investigate the impacts of pine species, infection responses, and data types on hyperspectral detection of PWD, particularly in the pre-visual stage. Artificial inoculation experiments were conducted across three locations with 76 sample trees of two pine species, and hyperspectral data were collected regularly using ground-based non-imaging and UAV imaging spectrometers Five infection responses were identified: keep healthy (KH), quick infection (QI), slow recovery (SR), quick recovery (QR), and slow infection (SI). Spectral analysis revealed dynamic changes in the indices RVI (680–550,750) and NDVI (560,680), corresponding well with the spectral characteristics of the five infection responses. The infected trees with QI response could be spectrally detected starting from day 14, with over 50 % accuracy. Importance analysis using RF identified RVI (554,677) and NDVI (531,570) as consistent in detecting pre-visual stages. In contrast, the six VIs determined by PCA-S (RARSb, RVI (900, 680), RVI (800, 680), RVI (760, 500), RVI (800, 635), and REP) exhibited high consistency and played a crucial role in identifying pre-visual stage infected trees. These VIs combined with specific color bands, enabled the creation of false-color images highlighting infected trees starting from day 14 post-inoculation. The study highlighted the importance of recognizing infection response patterns for accurate PWD detection, with only QI response trees showed a stable infection cycle, making day 14 post-infection a meaningful starting point for spectral detection. Additionally, imaging and non-imaging data types did not significantly affect the detection process, and the impact of spectral resolution variations between 1 nm and 3.5 nm was negligible. Further research is required to determine the threshold for larger differences of spectral resolution and to explore detection across various pine species and growth environments.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114468"},"PeriodicalIF":11.1,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142436434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new Multivariate Drought Severity Index to identify short-term hydrological signals: case study of the Amazon River basin","authors":"Artur Lenczuk ,&nbsp;Christopher Ndehedehe ,&nbsp;Anna Klos ,&nbsp;Janusz Bogusz","doi":"10.1016/j.rse.2024.114464","DOIUrl":"10.1016/j.rse.2024.114464","url":null,"abstract":"<div><div>The Earth's climate is changing rapidly and unexpectedly, causing more frequent, longer and more severe droughts, with lasting impacts on plants, ecosystems, communities and people. Consequently, this is leading to an increased importance of monitoring the climate and water storage trends in different regions. This information on a global scale is already commonly derived using satellite-based geodetic techniques such as the Global Positioning System (GPS) and the Gravity Recovery and Climate Experiment (GRACE). The use of both techniques has significant advantages, especially in regions where changes in the hydrosphere are notable, such as the Amazon basin, where 25 GPS stations were lately classified as benchmarks for hydrogeodesy. We show that the vertical displacements obtained from GPS and GRACE have good spatio-temporal agreement with the Standardized Precipitation and Standardized Precipitation Evapotranspiration indices, abbreviated respectively as SPI and SPEI, for all these stations. Drought severity index (DSI) estimated separately from GPS-observed and GRACE-derived vertical displacements on a station-by-station basis is capable to identify dry and wet events previously reported for the Amazon basin. However, due to the weaknesses of both techniques, such as technique-related systematic errors or coarse spatial resolution, a few extreme hydrological events may not be properly captured by GPS-DSI and/or GRACE-DSI. To take full advantage of both techniques and overcome their weaknesses, we introduce a completely new methodology to combine individual GPS-DSI and GRACE-DSI indices. As a novelty, both indices are estimated using short-term changes (&lt;9 months) of monthly vertical displacements observed by GPS permanent stations and those derived by GRACE for GPS locations. Then, to capture and detect drought events that either both geodetic techniques metrics missed or incorrectly depicted, the Multivariate Drought Severity Index (MDSI) is estimated through the concept of Frank copulas. We demonstrate that the MDSI captures more hydroclimatic events reported in previous studies, which are not identified by individual series of GPS-DSI or GRACE-DSI indices, and is temporally consistent with Standardized Streamflow Index (SSI) based on the in-situ river discharge changes.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114464"},"PeriodicalIF":11.1,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142431850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Innovative hybrid algorithm for simultaneous land surface temperature and emissivity retrieval: Case study with SDGSAT-1 data","authors":"Mengmeng Wang ,&nbsp;Guojin He ,&nbsp;Tian Hu ,&nbsp;Mingsi Yang ,&nbsp;Zhengjia Zhang ,&nbsp;Zhaoming Zhang ,&nbsp;Guizhou Wang ,&nbsp;Hua Li ,&nbsp;Wei Gao ,&nbsp;Xiuguo Liu","doi":"10.1016/j.rse.2024.114449","DOIUrl":"10.1016/j.rse.2024.114449","url":null,"abstract":"<div><div>The split-window (SW) and temperature-and-emissivity separation (TES) algorithms have been widely used for land surface temperature (LST) estimation from thermal infrared (TIR) observations for various missions. However, the SW algorithm requires prior estimates of land surface emissivity (LSE). The TES algorithm encompasses an atmospheric correction module, which increases the complexity and uncertainty of operational LST retrieval. To address this, we proposed a split-window-driven temperature-and-emissivity separation (SWDTES) algorithm in this study to estimate LST and LSE simultaneously without the need of atmospheric correction by combining the respective advantages of SW and TES. The inputs to the SWDTES algorithm are largely simplified, which only requires atmospheric water vapor content (AWVC) apart from the top-of-atmosphere TIR radiance. The developed SWDTES algorithm was applied to the high spatial resolution Thermal Infrared Spectrometer (TIS) data from the newly launched Sustainable Development Science Satellite-1 (SDGSAT-1) mission, and its performance was assessed using the MODIS data and ground measurements. The cross validation shows that the correlation coefficient (<em>r</em>), <em>bias</em> and root mean square error (RMSE) between MODIS-converted LSE and retrieved LSE using the SWDTES algorithm for the nighttime case is 0.904, −0.033 and 0.038 for band 1; 0.677, −0.008 and 0.014 for band 2; and 0.576, −0.000 and 0.008 for band 3, indicating a good consistency between the two LSE estimates. In addition, the evaluation using ground measurements shows that the <em>r</em>, <em>bias</em> and RMSE between the <em>in-situ</em> LST and retrieved LST using the SWDTES algorithm are 0.99, −0.67 K and 2.10 K, respectively. Compared to the OSW and TES algorithms, the SWDTES algorithm reduces the RMSE by 0.34 K and 0.90 K, respectively, indicating an improvement in LST retrieval accuracy. We conclude that the proposed SWDTES algorithm can achieve high-accuracy and high-resolution LST retrieval from the SDGSAT-1 mission, supporting fine-scale applications in energy, water, and carbon cycle modeling.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114449"},"PeriodicalIF":11.1,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142431781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tracking mangrove condition changes using dense Landsat time series","authors":"Xiucheng Yang ,&nbsp;Zhe Zhu ,&nbsp;Kevin D. Kroeger ,&nbsp;Shi Qiu ,&nbsp;Scott Covington ,&nbsp;Jeremy R. Conrad ,&nbsp;Zhiliang Zhu","doi":"10.1016/j.rse.2024.114461","DOIUrl":"10.1016/j.rse.2024.114461","url":null,"abstract":"<div><div>Mangroves in tropical and subtropical coasts are subject to episodic disturbances, notably from severe storms, leading to potential widespread vegetation mortality. The ability of vegetation to recover varies, and with disturbances becoming more frequent and severe, it is vital to track and project vegetation responses to support management and policy decisions. Prior studies have largely focused on binary mangrove mapping (i.e., presence or absence), while tracking conditions and condition change have not received sufficient attention. In this paper, we demonstrate a method based on dense time series Landsat images for continuous monitoring of mangrove conditions, where we track three kinds of post-disturbance mangrove conditions, including disturbed (disturbed, with rebound to the previous state within one growing season), recovering (undergoing natural recovery in longer than one growing season), and declining (showing long-term decline after disturbance). The method starts with disturbance detection using the DEtection and Characterization Of the tiDal wEtland change (DECODE) algorithm, an existing dense time series model designed to detect disturbances in tidal wetlands with adaptation to tidal fluctuations. This algorithm is well suited for the detection of tidal wetland disturbances but does not provide satisfactory post-disturbance monitoring results, due to the substantial variability in post-disturbance Landsat observations. To better monitor post-disturbance conditions, a new time series fitting approach, DECODER (DECODE and Recovery), is proposed for the recovery stage. Additionally, for temporal segments divided by disturbance events, we built a random forest classifier with temporal-spectral variables derived from the time series model to characterize mangrove conditions. Employing this approach in Florida's mangroves, we generated condition maps, such as dieback and recovery, with an overall accuracy of approximately 97.96 ± 0.86- [95 % confidence intervals]. Comparing post-hurricane conditions in Florida revealed that the increased frequency and severity of disturbances are challenging mangrove resilience, potentially diminishing their ability to recover and sustain ecosystem functions.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114461"},"PeriodicalIF":11.1,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142405045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Monitoring Earth's atmosphere with Sentinel-5 TROPOMI and Artificial Intelligence: Quantifying volcanic SO2 emissions","authors":"Claudia Corradino ,&nbsp;Paul Jouve ,&nbsp;Alessandro La Spina ,&nbsp;Ciro Del Negro","doi":"10.1016/j.rse.2024.114463","DOIUrl":"10.1016/j.rse.2024.114463","url":null,"abstract":"<div><div>Identifying changes in volcanic unrest and tracking eruptive activity are fundamental for volcanic surveillance and monitoring. Magmatic gases, particularly sulphur dioxide (SO₂), play a crucial role in influencing eruptive styles, making the monitoring of SO₂ emissions essential. Recent advancements in satellite remote sensing technology, including higher spatial resolution and sensitivity, have enhanced our ability to detect SO₂ emissions from volcanoes worldwide. However, traditional fixed-threshold algorithms struggle to automatically distinguish volcanic SO₂ emissions from non-volcanic sources. Additionally, accurately quantifying SO₂ emissions is challenging due to their dependence on plume height, particularly when reaching high altitudes. To address these challenges, we developed an Artificial Intelligence (AI) algorithm that detects and quantifies volcanic SO₂ emissions in near real-time. Our approach utilizes a Random Forest (RF) model, a supervised Machine Learning (ML) algorithm, to identify volcanic SO₂ emissions and integrates Cloud Top Height (CTH) data to enhance the accuracy of SO₂ mass quantification during intense volcanic eruptions. This AI algorithm, fully implemented in Google Earth Engine (GEE), leverages data from the TROPOspheric Monitoring Instrument (TROPOMI) aboard the Copernicus Sentinel-5 Precursor (S5P) satellite to automatically retrieve daily volcanic SO₂ plumes and CTH. We validated the model's performance against the Radius classifier, a state-of-the-art tool, and generalized its application across various volcanoes (Etna, Villarrica, Fuego, Pacaya, and Cumbre Vieja) with differing degassing activities, SO₂ emission rates, and plume geometries. Our findings demonstrate that the proposed AI approach effectively identifies and quantifies SO₂ plumes emitted by different volcanoes, enabling the investigation of SO₂ emission time series that reflect magma dynamics.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114463"},"PeriodicalIF":11.1,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142405048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Avian diversity across guilds in North America versus vegetation structure as measured by the Global Ecosystem Dynamics Investigation (GEDI)","authors":"Jin Xu ,&nbsp;Laura Farwell ,&nbsp;Volker C. Radeloff ,&nbsp;David Luther ,&nbsp;Melissa Songer ,&nbsp;William Justin Cooper ,&nbsp;Qiongyu Huang","doi":"10.1016/j.rse.2024.114446","DOIUrl":"10.1016/j.rse.2024.114446","url":null,"abstract":"<div><div>Avian diversity, a key indicator of ecosystem health, is closely related to canopy structure. Most avian diversity models are based on either optical remote sensing or airborne lidar data, but the latter is limited to small study areas. The launch of the Global Ecosystem Dynamics Investigation (GEDI) instrument in 2018 has opened new avenues for exploring the influence of vegetation structure on avian diversity. To examine how direct measurements of canopy structural characteristics explain bird diversity across North America, we analyzed 18 GEDI metrics from 2019 to 2022, along with corresponding Breeding Bird Survey (BBS) counts and AVONET morphological data, analyzing effects across broad regions and at varying spatial extents. We grouped 440 bird species into 20 ecological guilds under six guild categories and employed random forest algorithms to model avian diversity across eight spatial extents (1, 2, 3, 4, 5, 10, 20, and 39.2 km). The models predicted six diversity indices, including species richness (sRich), functional richness (fRich), evenness (fEve), dispersion (fDis), divergence (fDiv), and redundancy (fRed) across eight spatial extents. The best-predicted guilds varied for each diversity index. The most accurate models were sRich (pseudo-R² = 0.71, RMSE = 4.28) and fRed (pseudo-R² = 0.60, RMSE = 0.13) for forest specialists guilds; fRich (pseudo-R² = 0.55, RMSE = 0.18) for urban guilds; fEve (pseudo-R² = 0.28, RMSE = 0.08) for insectivore guilds; and fDiv (pseudo-R² = 0.38, RMSE = 0.12) and fDis (pseudo-R² = 0.53, RMSE = 0.87) for short distance migrants guilds. Our results highlight the critical role of canopy structure, including its horizontal and vertical distribution and variation, in predicting avian diversity, as measured by the mean number of detected modes (num_detectedmodes), the standard deviation of foliage height diversity (FHD), num_detectedmodes, canopy cover, and plant area index (PAI) across the spatial extents centered on BBS routes. Therefore, we recommend incorporating the GEDI metrics into avian diversity modeling and mapping across North America, thereby potentially enhancing bird habitat management and conservation efforts.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114446"},"PeriodicalIF":11.1,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142398391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reconstructing Tibetan Plateau lake bathymetry using ICESat-2 photon-counting laser altimetry","authors":"Xiaoran Han ,&nbsp;Guoqing Zhang ,&nbsp;Jida Wang ,&nbsp;Kuo-Hsin Tseng ,&nbsp;Jiaqi Li ,&nbsp;R. Iestyn Woolway ,&nbsp;C.K. Shum ,&nbsp;Fenglin Xu","doi":"10.1016/j.rse.2024.114458","DOIUrl":"10.1016/j.rse.2024.114458","url":null,"abstract":"<div><div>Lake bathymetry is important for quantifying and characterizing underwater morphology and its geophysical state, which is critical for hydrological and ecological studies. Due primarily to the harsh environment of the Tibetan Plateau, there is a severe lack of lake bathymetry measurements, limiting the accurate estimation of total lake volumes and their evolutions. Here, we propose a novel lake bathymetry reconstruction by combining ICESat-2/ATLAS (Advanced Topography Laser Altimetry System) data with a numerical model. An improved grid-based photon noise removal method is used to address the photon signal buried in the background noise during the local daytime. The developed model was validated for seven lakes on the Tibetan Plateau and showed good agreement between simulated and measured lake volumes, with an average absolute percentage error of 8.0 % for maximum water depth and 19.7 % for lake volume simulations. The model was then utilized to estimate the water volume of other lakes by combining it with the self-affine theory. The lake depths obtained from ICESat-2/ATLAS show good agreement (RMSE = 0.69 m; rRMSE = 10.3 %) with available in-situ measurements for lakes with depths &lt;16.5 m, demonstrating the potential of ICESat-2/ATLAS for improved reconstruction of the bathymetry of clear water inland lakes. Our study reveals for the first time, that the Tibetan Plateau has an estimated total lake water volume of 1043.69 ± 341.31 km³ for 33,477 lakes (&gt;0.01 km²) in 2022. Over 70 % (∼734.8 km³) of the lake water storage is concentrated in the Inner Plateau, with the Yellow River basin accounting for 10.9 % (∼113.9 km³), followed by the Indus River basin with 7.2 % (∼75.1 km³). Our study provides a robust method for estimating total lake volumes where in-situ measurements are scarce and can be extended to other clear water lakes, thus contributing to more accurate global assessments and towards comprehensive quantification of Earth's surface water resources distribution.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"315 ","pages":"Article 114458"},"PeriodicalIF":11.1,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142398390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}