International journal of applied earth observation and geoinformation : ITC journal最新文献

{"title":"An intercomparison of national and global land use and land cover products for Fiji","authors":"Kevin P. Davies ,&nbsp;John Duncan ,&nbsp;Renata Varea ,&nbsp;Diana Ralulu ,&nbsp;Solomoni Nagaunavou ,&nbsp;Nathan Wales ,&nbsp;Eleanor Bruce ,&nbsp;Bryan Boruff","doi":"10.1016/j.jag.2024.104260","DOIUrl":"10.1016/j.jag.2024.104260","url":null,"abstract":"<div><div>Here, a methodology to generate national-scale annual 10 m spatial resolution land use and land cover maps for Fiji (Fiji LULC) is presented. A training dataset of 13,419 points with a LULC label across three years from 2019 to 2021 was generated alongside a nationally representative test dataset of 834 points. These data were used to train a random forests model to convert an image stack of pre-processed Sentinel-2 surface reflectance data and topographic spatial layers into an annual categorical LULC map. When evaluated against the test dataset, the model has an overall accuracy of 83 % (SE: 2.1 %).</div><div>The Fiji LULC map was compared to three global 10 m spatial resolution land cover products: Google’s Dynamic World, ESRI LULC, and ESA’s WorldCover v200. These maps were compared statistically using the independent test dataset and in several case study applications (e.g. agricultural monitoring and disaster impacts mapping). The Fiji LULC had a higher overall accuracy than the three global LULC products and aligned more closely with a high-quality field survey of over 2500 rice fields (i.e. Fiji LULC classified 88 % of the rice fields as agricultural compared to 60.6–15.7 % in the global LULC products). A comparison of the overlap between the agricultural class of the four LULC maps with a flood mask following Tropical Cyclone Yasa indicated that dataset choice has a substantial impact on estimates of the area of flooded croplands. The Fiji LULC map tends to capture agricultural land covers and smaller scale landscape features with more accuracy than the global products. This analysis illustrates the importance of assessing the performance of global LULC products in particular locations and for specific applications. As demonstrated here, the choice of LULC product could impact subsequent analysis and monitoring tasks. To support these LULC product comparisons, an open-source Python package for computing performance metrics for LULC maps when reference data have different strata to map classes has been published. Further, the training data, test data, and national-scale maps for Fiji have been produced for 2019 to 2022 and are available as open source products on the Pacific Data Hub.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"135 ","pages":"Article 104260"},"PeriodicalIF":7.6,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142651762","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":"Combining readily available population and land cover maps to generate non-residential built-up labels to train Sentinel-2 image segmentation models","authors":"Diogo Duarte ,&nbsp;Cidália C. Fonte","doi":"10.1016/j.jag.2024.104272","DOIUrl":"10.1016/j.jag.2024.104272","url":null,"abstract":"<div><div>The localization of non-residential buildings over wide geographical areas is used as input within several contexts such as disaster management, regional and national planning, policy making and evaluation, among others. While the built-up environment has been continuously and globally mapped, given the efforts on producing synoptic land cover information; little attention has been given to the land use component of such built-up. This is due to, for example, difficulties in distinguishing built-up land use in non-commercial satellite imagery (e.g., Sentinel-2, with spatial resolution of up to 10 m), difficulties in collecting training data for supervised classification approaches, and the fact that variations in features of the built-up environment not always translate to a specific land use. This is even more critical when considering nadir viewing satellite or aerial imagery. However, map producers have been addressing this issue. For example, the Copernicus program (European Commission), through their pan-European CORINE Land Cover (CLC), and Urban Atlas restricted to several European metropolitan areas, have been making available land use information of the built-up cover, with 6-year intervals. The Global Human Settlement Layer (Copernicus program) has been providing built-up land use information by distinguishing residential from non-residential built-up since 2023 (GHSL_NRES). Currently these are also provided with a time interval of 5 years. National map producers often provide this information but usually with an interval between editions of several years. In this paper we combine readily available population counts and land cover maps to generate non-residential training labels that can be used to train a Sentinel-2 image segmentation model capable of distinguishing non-residential built-up from the remaining built-up. Leveraging two publicly available datasets, population counts (WorldPop) and built-up land cover (ESA WorldCover), allowed to produce training data from which an image segmentation model was able to learn relevant features to distinguish non-residential areas from other built-up in Sentinel-2 images. The results within a study area of 4 Sentinel-2 tiles shown that it improves the detection of non-residential built-up areas when comparing with CLC and GHSL_NRES (F1-score of 32 %, 25 % and 29 %, respectively), which are the products providing pan-European information regarding the built-up land use. These results indicate that the combination of publicly available geospatial datasets may be used to produce higher quality geospatial information.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"135 ","pages":"Article 104272"},"PeriodicalIF":7.6,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142651761","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 illusion of success: Test set disproportion causes inflated accuracy in remote sensing mapping research","authors":"Yuanjun Xiao ,&nbsp;Zhen Zhao ,&nbsp;Jingfeng Huang ,&nbsp;Ran Huang ,&nbsp;Wei Weng ,&nbsp;Gerui Liang ,&nbsp;Chang Zhou ,&nbsp;Qi Shao ,&nbsp;Qiyu Tian","doi":"10.1016/j.jag.2024.104256","DOIUrl":"10.1016/j.jag.2024.104256","url":null,"abstract":"<div><div>In remote sensing mapping studies, selecting an appropriate test set to accurately evaluate the results is critical. An imprecise accuracy assessment can be misleading and fail to validate the applicability of mapping products. Commencing with the WHU-Hi-HanChuan dataset, this paper revealed the impact of sample size ratios in test sets on accuracy metrics by generating a series of test sets with varying ratios of positive and negative sample size to evaluate the same map. A rigorous approach for accuracy assessment was suggested, and an example of tea plantations mapping is used to demonstrate the process and analyse potential issues in traditional approaches. A scale factor (<span><math><mi>λ</mi></math></span>) was constructed to measure the discrepancy in sample size ratios between test sets and actual conditions. Accuracy adjustment formulas were developed and applied to adjust the accuracy of 42 previous maps based on the <span><math><mi>λ</mi></math></span>. Results showed a higher ratio of positive to negative sample size in test set led to inflated user’s accuracy (UA), F1-score (F1) and overall accuracy (OA), but had little impact on producer’s accuracy. When the ratio aligned with that in the target area, the UA, F1, and OA closely matched the true values, indicating the proportion of positive and negative samples in test set should be consistent with that in actual situation. The accuracies reported by the traditional approaches including test set sampling from labelled data and 5-fold cross validation were far from the true accuracy and could not reflect the performance of the map. Among 42 previous maps, nearly 60% of the maps had UAs overestimated by 10%, and 9.5% of the maps had UAs and F1s deviations of more than 25%. The conclusions of this study provide a clear caution for future mapping research and assist in producing and identifying truly excellent maps.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"135 ","pages":"Article 104256"},"PeriodicalIF":7.6,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652000","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":"Multispectral imaging and terrestrial laser scanning for the detection of drought-induced paraheliotropic leaf movement in soybean","authors":"Erekle Chakhvashvili ,&nbsp;Lina Stausberg ,&nbsp;Juliane Bendig ,&nbsp;Lasse Klingbeil ,&nbsp;Bastian Siegmann ,&nbsp;Onno Muller ,&nbsp;Heiner Kuhlmann ,&nbsp;Uwe Rascher","doi":"10.1016/j.jag.2024.104250","DOIUrl":"10.1016/j.jag.2024.104250","url":null,"abstract":"<div><div>Plant foliage is known to respond rapidly to environmental stressors by adjusting leaf orientation at different timescales. One of the most fascinating mechanisms is paraheliotropism, also known as light avoidance through leaf movement. The leaf orientation (zenith and azimuth angles) is a parameter often overlooked in the plant and remote sensing community due to its challenging measurement procedures under field conditions. In this study, we investigate the synergistic potential of uncrewed aerial vehicle (UAV)-based mutlispectral imaging, terrestrial laser scanning (TLS) and radiative transfer model (RTM) inversion to identify the paraheliotropic response of two distinct soybean varieties: Minngold, a chlorophyll-deficient mutant, and Eiko, a wild variety. We examined their responses to drought stress during the boreal summer drought in 2022 in western Germany by measuring average leaf inclination angle (ALIA) and canopy reflectance. Measurements were taken in the morning and at midday to track leaf movement. Our observations show significant differences between the paraheliotropic response of both varieties. Eiko’s terminal and lateral leaves became vertically erect in the midday (<span><math><mrow><mn>54</mn><mo>→</mo><mn>6</mn><msup><mrow><mn>1</mn></mrow><mrow><mo>∘</mo></mrow></msup></mrow></math></span>), while Minngold’s ALIA remained largely unchanged (<span><math><mrow><mn>52</mn><mo>→</mo><mn>5</mn><msup><mrow><mn>7</mn></mrow><mrow><mo>∘</mo></mrow></msup></mrow></math></span>). Apart from the vertical leaf movement, we also observed leaf inversion (exposing the abaxial side of the leaf) in Eiko under extreme water scarcity. The red edge band at 740 nm showed the strongest correlation with ALIA (<span><math><mrow><msup><mrow><mi>r</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>=</mo><mn>0</mn><mo>.</mo><mn>52</mn><mo>−</mo><mn>0</mn><mo>.</mo><mn>76</mn></mrow></math></span>) The ratio of the far red edge to near infrared (RE740/NIR842) vegetation index compensated for varying light levels during morning and afternoon measurements, exhibiting strong correlations with ALIA when considering only sun-lit leaf spectra (<span><math><mrow><msup><mrow><mi>r</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>=</mo><mn>0</mn><mo>.</mo><mn>72</mn></mrow></math></span>). The retrieval of ALIA with PROSAIL varied based on ALIA constraints and the spectra used for retrieval (full spectrum or the combination of bands 742 and 842), resulting in a root mean square error (RMSE) of 7.7-12.9°. PROSAIL faced challenges in simulating the spectra of plots with very low LAI due to the soil background. This study made the first attempt to observe different paraheliotropic responses of two soybean varieties with UAV-based multispectral imaging. Proximal sensing opens up the possibilities to observe early stress indicators such as paraheliotropism, at much higher spatial and temporal resolution than ever before.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"135 ","pages":"Article 104250"},"PeriodicalIF":7.6,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142651864","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":"Tracking gain and loss of impervious surfaces by integrating continuous change detection and multitemporal classifications from 1985 to 2022 in Beijing","authors":"Xiao Zhang ,&nbsp;Liangyun Liu ,&nbsp;Wenhan Zhang ,&nbsp;Linlin Guan ,&nbsp;Ming Bai ,&nbsp;Tingting Zhao ,&nbsp;Zhehua Li ,&nbsp;Xidong Chen","doi":"10.1016/j.jag.2024.104268","DOIUrl":"10.1016/j.jag.2024.104268","url":null,"abstract":"<div><div>Impervious surfaces are important indicators of human activity, and finding ways to quantify the gain and loss of impervious surfaces is important for sustainable urban development. However, most relevant studies assume that the transformation of natural surfaces to impervious surfaces is irreversible; thus, the losses of impervious surfaces are often ignored. Here, we propose a novel framework taking advantage of continuous change detection, multitemporal classification, and LandTrendr optimization to track the annual gains and losses in impervious surfaces. It may be the first study to focus on both loss and gain of impervious surfaces using time-series Landsat imagery. Specifically, we built dual continuous-change-detection models to pursue lower commission and omission errors for generating time-series training samples. Then, we adopted time-series classifications from multisource information and derived training samples to develop annual impervious-surface maps from 1985 to 2022 in Beijing. Afterwards, a novel optimization algorithm considering spatial heterogeneity and taking advantage of the LandTrendr algorithm was also proposed to optimize the spatiotemporal consistency of these impervious-surface maps. We further calculated accuracy metrics for the proposed method using time-series validation points, finding overall accuracies of 92.91 %±0.97 % and 93.17 %±1.26 % for gains and losses in impervious surfaces, respectively, using a one-year tolerance. Lastly, we revealed the gains and losses of impervious surfaces in Beijing during 1985–2022. The gained area of impervious surfaces was found to be 1996.21 km² ± 18.58 km², and there was a rapid increase during 2000–2010; the total lost area of impervious surfaces was 898.60 km² ± 4.58 km², of which 564.85 km² ± 2.21 km² first increased and was then lost. Therefore, the proposed method provides a new way of tracking the gain and loss of impervious surfaces, and it offers new possibilities for monitoring urban regreening.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"135 ","pages":"Article 104268"},"PeriodicalIF":7.6,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142651953","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":"White blanket, blue waters: Tracing El Niño footprints in Canada","authors":"Afshin Amiri ,&nbsp;Silvio Gumiere ,&nbsp;Hossein Bonakdari","doi":"10.1016/j.jag.2024.104267","DOIUrl":"10.1016/j.jag.2024.104267","url":null,"abstract":"<div><div>The El Niño Southern Oscillation (ENSO) significantly influences global climate patterns, with one of the strongest warm phases (El Niño) occurring in 2023, altering precipitation and temperature regimes. In this study, the spatiotemporal variability in snow cover across Canadian provinces from December 2023 to February 2024 relative to long-term averages is explored. The NOAA-OISST, NOAA-CSFV2, and MODIS MOD10A1 remote sensing datasets were selected to assess the impacts of El Niño on snow cover changes and the subsequent effects on water availability, agricultural productivity, the municipal water supply, natural ecosystems, and wildfire risk in Canada. An analysis of sea surface temperature anomalies in the equatorial Pacific revealed that El Niño intensity and progression are linked to regional snow cover deviations. Compared with the long-term average, Canada’s snow cover area experienced significant declines in December 2023, January 2024, and February 2024, with decreases of 135,938 km² (−7.43 %), 309,928 km² (−15.26 %), and 136,406 km² (−4.57 %), respectively. The findings indicate significant disparities among provinces, with Ontario, Quebec, and Manitoba experiencing marked decreases in snow cover, whereas in Saskatchewan and Alberta, initial increases were followed by subsequent variability. In British Columbia, a late-season increase in snow was observed, whereas minor changes were noted in the Maritime provinces and Northern territories. The findings of this study highlight the importance of snow cover as an important factor that has a considerable impact on the hydrological cycle and agricultural productivity, influences environmental health and economic resilience, and is crucial for both natural ecosystems and human livelihoods.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"135 ","pages":"Article 104267"},"PeriodicalIF":7.6,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142651957","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":"DeLA: An extremely faster network with decoupled local aggregation for large scale point cloud learning","authors":"Weikang Yang ,&nbsp;Xinghao Lu ,&nbsp;Binjie Chen ,&nbsp;Chenlu Lin ,&nbsp;Xueye Bao ,&nbsp;Weiquan Liu ,&nbsp;Yu Zang ,&nbsp;Junyu Xu ,&nbsp;Cheng Wang","doi":"10.1016/j.jag.2024.104255","DOIUrl":"10.1016/j.jag.2024.104255","url":null,"abstract":"<div><div>With advances in data collection technology, the volume of recent remote sensing point cloud datasets has grown significantly, posing substantial challenges for point cloud deep learning, particularly in neighborhood aggregation operations. Unlike simple pooling, neighborhood aggregation incorporates spatial relationships between points into the feature aggregation process, requiring repeated relationship learning and resulting in substantial computational redundancy. The exponential increase in data volume exacerbates this issue. To address this, we theoretically demonstrate that if basic spatial information is encoded in point features, simple pooling operations can effectively aggregate features. This means the spatial relationships can be extracted and integrated with other features during aggregation. Based on this concept, we propose a lightweight point network called DeLA (Decoupled Local Aggregation). DeLA separates the traditional neighborhood aggregation process into distinct spatial encoding and local aggregation operations, reducing the computational complexity by a factor of K, where K is the number of neighbors in the K-Nearest Neighbor algorithm (K-NN). Experimental results on five classic benchmarks show that DeLA achieves state-of-the-art performance with reduced or equivalent latency. Specifically, DeLA exceeds 90% overall accuracy on ScanObjectNN and 74% mIoU on S3DIS Area 5. Additionally, DeLA achieves state-of-the-art results on ScanNetV2 with only 20% of the parameters of equivalent models. Our code is available at <span><span>https://github.com/Matrix-ASC/DeLA</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"135 ","pages":"Article 104255"},"PeriodicalIF":7.6,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142651951","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":"Using UAV hyperspectral imagery and deep learning for Object-Based quantitative inversion of Zanthoxylum rust disease index","authors":"Kai Zhang ,&nbsp;Jie Deng ,&nbsp;Congying Zhou ,&nbsp;Jiangui Liu ,&nbsp;Xuan Lv ,&nbsp;Ying Wang ,&nbsp;Enhong Sun ,&nbsp;Yan Liu ,&nbsp;Zhanhong Ma ,&nbsp;Jiali Shang","doi":"10.1016/j.jag.2024.104262","DOIUrl":"10.1016/j.jag.2024.104262","url":null,"abstract":"<div><div><em>Zanthoxylum</em> rust (ZR) poses a significant threat to <em>Zanthoxylum bungeanum</em> Maxim.(ZBM) production, impacting both the yield and quality. The lack of current research on ZR using unmanned aerial vehicle (UAV) remote sensing poses a challenge to achieving precise management of individual ZBM plant. This study acquired six UAV hyperspectral images to create a ZR inversion dataset . This dataset, to our knowledge, is the first dataset for remote sensing deep learning (DL) of ZR using UAV. To facilitate automated extraction of individual ZBM plant and the quantitative inversion of ZR disease index (DI), we introduced the object-based quantitative inversion framework (OQIF). OQIF achieved high accuracy in recognizing ZBM (average precision at an intersection over union threshold of 0.5 was 90.0 %). Remarkably, OQIF demonstrates outstanding quantitative inversion results for ZR DI (R² = 0.90, RMSE = 3.97, n = 8166). For DI &lt; 10, the RMSE was 2.48, showcasing early detection capability. Our research has significant implications for ZBM cultivation and precision management, pioneering object-based quantitative inversion for tree diseases and yield estimation, with potential for early ZR detection.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"135 ","pages":"Article 104262"},"PeriodicalIF":7.6,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142651955","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":"Accuracy fluctuations of ICESat-2 height measurements in time series","authors":"Xu Wang ,&nbsp;Xinlian Liang ,&nbsp;Weishu Gong ,&nbsp;Pasi Häkli ,&nbsp;Yunsheng Wang","doi":"10.1016/j.jag.2024.104234","DOIUrl":"10.1016/j.jag.2024.104234","url":null,"abstract":"<div><div>The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) mission, spanning the past five years, has collected extensive three-dimensional Earth observation data, facilitating the understanding of environmental changes on a global scale. Its key product, Land and Vegetation Height (ATL08), offers global land and vegetation height data for carbon budget and cycle modeling. Consistent measurement accuracy of ATL08 is crucial for reliable time series analysis. However, fluctuations in the temporal accuracy of ATL08 data have been ignored in previous studies, leading to unknown uncertainties in existing time-series analyses. To bridge the knowledge gap, this study analyzes 59 months of ATL08 version 006 data in Finland to assess terrain and surface height accuracy, with a focus on temporal fluctuations across six major land cover types. A random forest (RF) model is employed to quantify the relative importance of error factors affecting height accuracy. Moreover, the study assesses accuracy at two official spatial resolutions, i.e., 100 m × 11 m and 20 m × 11 m, to evaluate the capability of ATL08 for the high-resolution height retrieval. For the terrain, the 100 m segment shows a bias of 0.04 m, a mean absolute error (MAE) of 0.44 m, and a root mean square error (RMSE) of 0.66 m, while the 20 m segment exhibits a bias of 0.10 m, a MAE of 0.35 m, and an RMSE of 0.49 m. For the surface height, the 100 m segment shows a bias of −0.59 m, a MAE of 3.06 m, an RMSE of 4.52 m, a bias% of −3.45 %, a MAE% of 21.26 %, and an RMSE% of 31.40 %. The 20 m segment exhibits a bias of −0.72 m, a MAE of 3.51 m, an RMSE of 5.23 m, a bias% of −5.81 %, a MAE% of 28.52 %, and an RMSE% of 42.47 %. The results indicate that improving segment resolution enhances terrain accuracy but reduces surface height accuracy. According to the error factor analysis, surface coverage and beam type are crucial for terrain retrieval accuracy, with their effects varying over time. Seasonal changes, particularly the presence of snow, affect terrain retrieval accuracy, with the lowest accuracy observed around March each year. This study confirms the critical impact of surface height on its retrieval accuracy and suggests avoiding the use of ATL08 for retrieving low target surface heights, especially in steep terrains. Nevertheless, the analysis affirms the applicability of ATL08 for canopy height estimation in boreal forests, primarily composed of coniferous species, highlighting its potential for extensive spatial and temporal research. This contributes to bridging the gaps between accurate estimates and large area coverage in global carbon budget and cycle studies. Additionally, the findings reveal that similar issues may exist in other satellite laser altimetry missions, emphasizing the important impacts of temporal fluctuations in surface and terrain accuracy when utilizing satellite laser altimetry datasets.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"135 ","pages":"Article 104234"},"PeriodicalIF":7.6,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142651952","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 high temporal resolution NDVI time series to monitor drought events in the Horn of Africa","authors":"Riccardo D’Ercole ,&nbsp;Daniele Casella ,&nbsp;Giulia Panegrossi ,&nbsp;Paolo Sanò","doi":"10.1016/j.jag.2024.104264","DOIUrl":"10.1016/j.jag.2024.104264","url":null,"abstract":"<div><div>This study investigates the reconstruction of climatological patterns and vegetation dynamics in the Horn of Africa region using high temporal resolution (i.e. daily) Normalized Difference Vegetation Index (NDVI) datasets. The analysis compares a straight-forward processing approach to derive a daily vegetation index from a geostationary (SEVIRI) satellite with existing NDVI series from geostationary or polar-orbiting (MODIS, MetOp-AVHRR) satellites, highlighting the impact of cloud contamination on data quality in high temporal resolution datasets. Using a smoothing process designed to reconstruct the upper envelope of the vegetation status series, we obtained a daily vegetation dataset that effectively mitigated cloud-induced fluctuations, outperforming polar-orbiting (e.g. MODIS) satellite-derived dataset in capturing regional climatology. We demonstrated this through statistical analysis, including autocorrelation and mean absolute difference between consecutive observations. We showed that cloud contamination significantly affects high temporal resolution NDVI series, particularly in forest areas, which makes it difficult to identify a suitable dataset to validate our approach. Therefore, we mitigated this problem using a Maximum Value Compositing technique, designed to remove cloud-induced biases and further compared our results with another independent vegetation index at coarser temporal resolution derived from AVHRR. We found that our vegetation index closely relates with MODIS 10-day composites after removing cloud-contaminated pixels. Furthermore, the study evaluates the sensitivity of the selected NDVI datasets to drought events, demonstrating the strength of the proposed SEVIRI dataset in capturing the intensity and persistence of vegetation anomalies. In conclusion, the study presents an innovative strategy for deriving daily-resolution NDVI datasets in cloud-prone regions, validating it with independent datasets at different sub-monthly temporal scales.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"135 ","pages":"Article 104264"},"PeriodicalIF":7.6,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142651954","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}