Frontiers in Remote Sensing最新文献

{"title":"An integrated hierarchical classification and machine learning approach for mapping land use and land cover in complex social-ecological systems","authors":"G. Ojwang, J. Ogutu, Mohammed Y. Said, M. Ojwala, S. Kifugo, Francesca Verones, B. Graae, R. Buitenwerf, Han Olff","doi":"10.3389/frsen.2023.1188635","DOIUrl":"https://doi.org/10.3389/frsen.2023.1188635","url":null,"abstract":"Mapping land use and land cover (LULC) using remote sensing is fundamental to environmental monitoring, spatial planning and characterising drivers of change in landscapes. We develop a new, general and versatile approach for mapping LULC in landscapes with relatively gradual transition between LULC categories such as African savannas. The approach integrates a well-tested hierarchical classification system with the computationally efficient random forest (RF) classifier and produces detailed, accurate and consistent classification of structural vegetation heterogeneity and density and anthropogenic land use. We use Landsat 8 OLI imagery to illustrate this approach for the Extended Greater Masai Mara Ecosystem (EGMME) in southwestern Kenya. We stratified the landscape into eight relatively homogeneous zones, systematically inspected the imagery and randomly allocated 1,697 training sites, 556 of which were ground-truthed, proportionately to the area of each zone. We directly assessed the accuracy of the visually classified image. Accuracy was high and averaged 88.1% (80.5%–91.7%) across all the zones and 89.1% (50%–100%) across all the classes. We applied the RF classifier to randomly selected samples from the original training dataset, separately for each zone and the EGMME. We evaluated the overall and class-specific accuracy and computational efficiency using the Out-of-Bag (OOB) error. Overall accuracy (79.3%–97.4%) varied across zones but was higher whereas the class-specific accuracy (25.4%–98.1%) was lower than that for the EGMME (80.2%). The hierarchical classifier identified 35 LULC classes which we aggregated into 18 intermediate mosaics and further into five more general categories. The open grassed shrubland (21.8%), sparse shrubbed grassland (10.4%) and small-scale cultivation (13.3%) dominated at the detailed level, grassed shrubland (31.9%) and shrubbed grassland (28.9%) at the intermediate level, and grassland (35.7%), shrubland (35.3%) and woodland (12.5%) at the general level. Our granular LULC map for the EGMME is sufficiently accurate for important practical purposes such as land use spatial planning, habitat suitability assessment and temporal change detection. The extensive ground-truthing data, sample site photos and classified maps can contribute to wider validation efforts at regional to global scales.","PeriodicalId":502669,"journal":{"name":"Frontiers in Remote Sensing","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140232512","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":"Validation of satellite water products based on HYPERNETS in situ data using a Match-up Database (MDB) file structure","authors":"Luis González Vilas, V. E. Brando, Javier A. Concha, C. Goyens, A. Dogliotti, D. Doxaran, Antoine Dille, D. Van der Zande","doi":"10.3389/frsen.2024.1330317","DOIUrl":"https://doi.org/10.3389/frsen.2024.1330317","url":null,"abstract":"A Match-up Database (MDB) file structure and tools were developed to ease the validation analysis of satellite water products and to improve the exchange and processing of match-up data from different sites, missions and atmospheric correction processors. In situ remote sensing reflectance (Rrs) measurements were available from the HYPSTAR® (HYperspectral Pointable System for Terrestrial and Aquatic Radiometry), a new automated hyperspectral radiometer. An MDB file is a NetCDF file containing all the potential match-ups between satellite and in situ data on a specific site and within a given time window. These files are generated and manipulated with three modules developed in Python to implement the validation protocols: extract satellite data, associate each extract with co-located in situ radiometry data, and then perform the validation analysis. This work provides details on the implementation of the open-source MDB file structure and tools. The approach is demonstrated by a multi-site matchup comparison based on satellite data from the Sentinel-2 MSI and Sentinel-3 OLCI sensors, and HYPSTAR® data acquired over six water sites with diverse optical regimes from February 2021 to March 2023.The analysis of Sentinel-3 OLCI matchups across the six sites shows consistency with previous comparisons based on AERONET-OC data over extended reflectance range. We evaluated Sentinel-2 MSI reflectance data corrected with two atmospheric correction processors (ACOLITE and C2RCC) over four sites with clear to highly turbid waters. Results showed that the performance of the processors depends on the optical regime of the sites. Overall, we proved the suitability of the open-source MDB-based approach to implement validation protocols and generate automated matchup analyses for different missions, processors and sites.","PeriodicalId":502669,"journal":{"name":"Frontiers in Remote Sensing","volume":"62 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140232359","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":"Secure learning-based coordinated UAV–UGV framework design for medical waste transportation","authors":"D. Sharma, Jeremy Lin","doi":"10.3389/frsen.2024.1351703","DOIUrl":"https://doi.org/10.3389/frsen.2024.1351703","url":null,"abstract":"A cost-effective solution with less human involvement must be developed for medical waste (MW) transportation. A learning-based coordinated unmanned aerial vehicle–unmanned ground vehicle (UAV–UGV) (CUU) framework, currently unavoidable use, with a transfer learning algorithm is suggested. A transfer learning algorithm is implemented for collision-free optimal path planning. In the framework, mobile ground robots collect medical waste from waste disposal centers through the pick-and-place technique. Then, networked drones lift the collected medical waste and fly through a predefined optimal trajectory. The framework considers the dynamic behavior of the environment and explores the actions for picking, placing, and dropping medical waste. A deep reinforcement learning mechanism has been incorporated for each successful or unsuccessful action by the framework to provide the rewards. With optimal policies, the coordinated UAV and UGV change their actions in dynamic conditions. An optimal cost of transportation of medical waste by the proposed framework is created by considering the weight of MW packets as the payload capacity of a CUU framework, the cost of steering the UAV and UGV, and the time required to transport the MW. The effectiveness of the CUU framework for MW transportation has been tested using MATLAB. The MW transportation data have been encrypted using an encryption key for security and authenticity.","PeriodicalId":502669,"journal":{"name":"Frontiers in Remote Sensing","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140237429","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":"Spectral imaging of grass species in arid ecosystems of Namibia","authors":"Paul Bantelmann, D. Wyss, Elizabeth Twitileni Pius, Martin Kappas","doi":"10.3389/frsen.2024.1368551","DOIUrl":"https://doi.org/10.3389/frsen.2024.1368551","url":null,"abstract":"Grasslands across the African continent are under pressure from climate change and human activities, particularly in arid ecosystems. From a remote sensing perspective, these ecosystems have not received much scientific attention, especially in Namibia. To address this knowledge gap, various remote sensing methods were implemented using new generation spaceborne imaging spectrometers amongst others. Therefore, this research provides a first methodological approach aimed at mapping and evaluating the distribution of grasslands within two private nature reserves, namely, the NamibRand Nature Reserve (NRNR) and ProNamib Nature Reserve (PNNR) with surrounding farmlands on the edge of Namib Sand Sea. The multi-sensor approach utilizes Mixture Tuned Matched Filtering (MTMF) and incorporated spectral information collected in the field to analyze grasslands. The research involves a sensor comparison of multispectral Sentinel-2 and PlanetScope data, hyperspectral data from Environmental Mapping and Analysis Programme (EnMAP) and PRecursore IperSpettrale della Missione Applicativa (PRISMA) and an additional data fusion product derived from Sentinel-2 and EnMAP imagery based on a Smoothing Filter-based Intensity Modulation Hypersharpening method (SFIM-HS). Additionally, a unique spectral library of collected field spectra was established and inter-species spectral separability and intra-species spectral homogeneity was analyzed. This library presents newly published spectra of individual species. Due to dry initial conditions, the calculated spectral separability of individual grasses is limited, making only a mean endmember feasible for partial unmixing. The validation results of satellite comparison show that data fusion products (R2 = 0.51 with Normalized Difference Vegetation Index (NDVI); R2 = 0.66 with Soil Adjusted Vegetation Index (SAVI)) are more suitable for mapping arid grasslands than multispectral or hyperspectral data (all R2 < 0.35). More research is required and potential methodological adjustments are discussed to further investigate the spatio-temporal dynamics of arid grasslands and to aid conservation efforts in the Greater Sossusvlei-Namib Landscape in line with the United Nations Decade of Restoration.","PeriodicalId":502669,"journal":{"name":"Frontiers in Remote Sensing","volume":"42 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140242604","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":"Reconstructing hyper-spectral downwelling irradiance from multi-spectral measurements","authors":"Jing Tan, R. Frouin, N. Haëntjens, Andrew Barnard, Emmanuel Boss, Paul Chamberlain, Matt Mazloff, Cristina Orrico","doi":"10.3389/frsen.2024.1335627","DOIUrl":"https://doi.org/10.3389/frsen.2024.1335627","url":null,"abstract":"Checking the radiometric calibration of satellite hyper-spectral sensors such as the PACE Ocean Color Instrument (OCI) while they operate in orbit and evaluating remote sensing reflectance, the basic variable from which a variety of optical and biogeochemical ocean properties can be derived, requires measuring upwelling radiance just above the surface (Lw) and downwelling planar irradiance reaching the surface (Es). For this, the current HyperNav systems measure Lw at about 2 nm spectral resolution in the ultraviolet to near infrared, but Es in only four 10 nm wide spectral bands centered on 412, 489, 555, and 705 nm. In this study, the Es data acquired in these spectral bands in clear sky conditions are used to reconstruct via a multi-linear regression model the hyper-spectral Es signal at 0.5 nm resolution from 315 to 900 nm, the OCI spectral range, allowing an estimate of Es at the HyperNav, OCI, and other sensors’ resolutions. After correction of gaseous absorption and normalization by the top-of-atmosphere incident solar flux, the atmospheric diffuse transmittance is expressed as a linear combination of Es measured in those 4 spectral bands. Based on simulations for Sun zenith angles from 0 to 75° and a wide range of (i.e., expected) atmospheric, surface, and water conditions, the Es spectrum is reconstructed with a bias of less than 0.4% in magnitude and an RMS error (RMSE) ranging from 0% to 2.5%, depending on wavelength. The largest errors occur in spectral regions with strong gaseous absorption. In the presence of typical noise on Es measurements and uncertainties on the ancillary variables, the bias and RMSE become −2.5% and 7.0%, respectively. Using a General Additive Model with coefficients depending on Sun zenith angle and aerosol optical thickness at 550 nm improves statistical performance in the absence of noise, especially in the ultraviolet, but provides similar performance on noisy data, indicating more sensitivity to noise. Adding spectral bands in the ultraviolet, e.g., centered on 325, 340, and 380 nm, yields marginally more accurate results in the ultraviolet, due to uncertainties in the gaseous transmittance. Comparisons between the measured and reconstructed Es spectra acquired by the MOBY spectroradiometer show agreement within predicted uncertainties, i.e., biases less than 2% in magnitude and RMS differences less than 5%. Reconstruction can also be achieved accurately with other sets of spectral bands and extended to cloudy conditions since cloud optical properties, like aerosol properties, tend to vary regularly with wavelength. These results indicate that it is sufficient, for many scientific applications involving hyper-spectral Es, to measure Es in a few coarse spectral bands in the ultraviolet to near infrared and reconstruct the hyperspectral signal using the proposed multivariate linear modeling.","PeriodicalId":502669,"journal":{"name":"Frontiers in Remote Sensing","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140242277","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":"Recent advances and challenges in monitoring and modeling of disturbances in tropical moist forests","authors":"Jiaying He, Wei Li, Zhe Zhao, Lei Zhu, Xiaomeng Du, Yidi Xu, Minxuan Sun, Jiaxin Zhou, P. Ciais, J. Wigneron, Ronggao Liu, Guanghui Lin, Lei Fan","doi":"10.3389/frsen.2024.1332728","DOIUrl":"https://doi.org/10.3389/frsen.2024.1332728","url":null,"abstract":"Tropical moist forests have been severely affected by natural and anthropogenic disturbances, leading to substantial changes in global carbon cycle and climate. These effects have received great attention in scientific research and debates. Here we review recent progress on drivers and ecological impacts of tropical moist forest disturbances, and their monitoring and modeling methods. Disturbances in tropical moist forests are primarily driven by clearcutting, selective logging, fire, extreme drought, and edge effects. Compound disturbances such as fire and edge effects aggravate degradation in the edge forests. Drought can result in terrestrial carbon loss via physiological impacts. These disturbances lead to direct carbon loss, biophysical warming and microclimate change. Remote sensing observations are promising for monitoring forest disturbances and revealing mechanisms, which will be useful for implementing disturbance processes in dynamic vegetation models. Yet, constrained spatiotemporal coverages and resolutions limit the application of these data in process-based models. It is also challenging to represent physical processes derived from fine-resolution remote sensing data in coarse-resolution models. We highlight the need to continuously integrate new datasets and physical processes in forest disturbance modeling to advance understanding of disturbance patterns and impacts. Interactions and impacts of climate change and anthropogenic activities should also be considered for modeling and assessing feedbacks of tropical moist forest disturbances.","PeriodicalId":502669,"journal":{"name":"Frontiers in Remote Sensing","volume":"5 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140242971","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":"Erratum: Apparent surface-to-sky radiance ratio of natural waters including polarization and aerosol effects: implications for above-water radiometry","authors":"Frontiers Production Office","doi":"10.3389/frsen.2024.1379573","DOIUrl":"https://doi.org/10.3389/frsen.2024.1379573","url":null,"abstract":"","PeriodicalId":502669,"journal":{"name":"Frontiers in Remote Sensing","volume":"250 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140428157","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":"Accounting for 3D radiative effects in MODIS aerosol retrievals near clouds using CALIPSO observations","authors":"G. Wen, A. Marshak, Robert Levy, Gregory Schuster","doi":"10.3389/frsen.2023.1333814","DOIUrl":"https://doi.org/10.3389/frsen.2023.1333814","url":null,"abstract":"Retrieval of aerosol properties near clouds from passive remote sensing is challenging. Sunlight scattered by clouds into nearby clear regions can effectively enhance the clear area reflectance. These cloud 3D radiative effects may lead to large biases in aerosol retrievals if uncorrected, risking the incorrect interpretation of satellite observations for aerosol–cloud interaction in a cloudy atmosphere. In earlier studies, we developed a simple two-layer model (2LM) to estimate the cloud-induced clear-sky radiance enhancements in cloud fields. In this study, we take advantage of CALIPSO lidar observations, which should not be affected by the 3D radiative effect, to study passive aerosol retrievals in cloud fields in the Amazon region, specifically those produced by the operational Dark Target algorithm applied to Aqua-MODIS. From 2 years’ worth of co-located CALIPSO/MODIS aerosol retrievals, we find a larger increase in operationally retrieved MODIS AOD from clear to cloudy regions (∼0.075 or ∼40%) than for the CALIPSO AOD (∼0.021 or ∼20%). The much larger increase in MODIS AOD is mainly due to the 3D radiative effects. After using the 2LM model to account for cloud 3D radiative effects, the clear to cloudy increase in MODIS AOD was reduced to ∼0.043 (∼23%), which is much closer to CALIPSO observations. The 3D corrected average MODIS AOD for cloudy conditions is significantly larger than AOD for clear conditions, even for cloud fraction (CF) less than 0.1, suggesting aerosols in cloudy conditions are characteristically different from aerosols in clear conditions. Furthermore, the 3D correction of AOD (i.e., τ1D−τ3D) increases linearly with CF for a large range of CF. We have also examined the impact of the 3D effect on aerosol Ångström Exponent (AE) and fine model fraction (FMF) of AOD. We found that the uncorrected average AE and FMF depend strongly on CF, ∼25% increase in AE (decrease in particle size) and ∼60% increase in FMF as CF increases from 0.05 to 0.45. The 3D correction leads to smaller average AE (bigger particle size) and FMF that are almost independent of CF. Thus, the 3D corrected aerosol properties are expected to provide more accurate information for better understanding aerosol–cloud interactions.","PeriodicalId":502669,"journal":{"name":"Frontiers in Remote Sensing","volume":"177 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140481030","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":"Superyachts could support satellite ocean colour validation","authors":"Robert J. W. Brewin, Xuerong Sun, Dirk A. Aurin, Johannes J. Viljoen, Christopher Walsh, S. Sathyendranath","doi":"10.3389/frsen.2024.1336494","DOIUrl":"https://doi.org/10.3389/frsen.2024.1336494","url":null,"abstract":"Visible spectral radiometry of seawater, often referred to as ocean colour, from space, provides a synoptic view of surface phytoplankton, and other optically-active constituents, at high temporal resolution, that is unsurpassed by any other technology. Yet, in-situ observations of ocean colour are critical to the success of the satellite, tracking the calibration of the radiometers and validating atmospheric correction algorithms. Owing to the high cost of commercial field radiometers, as well as the high costs associated with ocean-based field work, ocean colour scientists are plagued by a sparsity of high quality in-situ radiometric observations, particularly in remote regions. In this perspective article, we highlight potential to increase the number of in-situ observations of ocean colour by harnessing superyachts. Using openly-available data processing software, we show that automated ocean colour data collected using a superyacht can be used for the validation of an ocean colour satellite, with comparable results to traditional validation studies. Reaching out to wealthy citizen scientists may help fill gaps in our ability to monitor the colour of the ocean.","PeriodicalId":502669,"journal":{"name":"Frontiers in Remote Sensing","volume":"34 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139602441","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":"An operational approach to near real time global high resolution mapping of the terrestrial Human Footprint","authors":"Francis Gassert, Oscar Venter, James E. M. Watson, Steven P. Brumby, Joseph C. Mazzariello, Scott C. Atkinson, Samatha Hyde","doi":"10.3389/frsen.2023.1130896","DOIUrl":"https://doi.org/10.3389/frsen.2023.1130896","url":null,"abstract":"","PeriodicalId":502669,"journal":{"name":"Frontiers in Remote Sensing","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139229481","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}