Science of Remote Sensing最新文献

{"title":"The Hi-GLASS all-wave daily net radiation product: Algorithm and product validation","authors":"Bo Jiang , Jiakun Han , Hui Liang , Shunlin Liang , Xiuwan Yin , Jianghai Peng , Tao He , Yichuan Ma","doi":"10.1016/j.srs.2023.100080","DOIUrl":"https://doi.org/10.1016/j.srs.2023.100080","url":null,"abstract":"<div><p>The surface net radiation (<em>R</em><sub><em>n</em></sub>) represents the balance of the radiative budget on the land surface and drives many physical and biological processes. An accurate and long-term product for global daily coverage of <em>R</em><sub><em>n</em></sub> at a high spatial resolution is needed for a variety of applications at regional and local scales. This study proposes two algorithms, called the downward shortwave radiation (DSR)-based algorithm and the top-of-atmosphere (TOA)-based algorithm, to estimate <em>R</em><sub><em>n</em></sub> by using Landsat data. The DSR-based algorithm consists of three conditional models, and was developed based on the analysis of the relationship between <em>R</em><sub><em>n</em></sub> and shortwave radiation as well as ancillary information from ground measurements and various datasets. The TOA-based algorithm was developed by linking <em>R</em><sub><em>n</em></sub> to TOA observations from Landsat sensors and ancillary information. The two algorithms were developed by using the random forest method. The results of their validation against ground measurements showed that the DSR-based algorithm outperformed the TOA-based algorithm in terms of accuracy, with a determination coefficient (R<sup>2</sup>) of 0.93, root-mean-squared error (RMSE) of 17.58 Wm<sup>−2</sup>, and bias of −4.27 Wm<sup>−2</sup>. It was stable under various conditions. We then applied the DSR-based algorithm to generate a product of the global daily <em>R</em><sub><em>n</em></sub>, called the High-resolution (Hi)- Global LAnd Surface Satellite (GLASS), from 2013 to 2018 at a spatial resolution of 30 m under a clear sky based on remotely sensed products, including the DSR from GLASS, the normalized difference vegetation index (NDVI) obtained from Landsat, surface broadband albedo from Hi-GLASS, and meteorological factors based on reanalysis data from MERRA2. Following its validation using in-situ observations from 2013 to 2018, the overall accuracy of the daily <em>R</em><sub><em>n</em></sub> acquired by Hi-GLASS under clear sky was found to be satisfactory, with a value of R<sup>2</sup> of 0.90 and an RMSE of 25.03 Wm<sup>−2</sup>. Moreover, compared with the daily <em>R</em><sub><em>n</em></sub> obtained from the GLASS product at a spatial resolution of 5 km, that obtained by Hi-GLASS can better characterize the surface by providing more details and capturing the variations in the measurements, especially large and small values. However, due to limitations of the available datasets and the algorithm, the data on <em>R</em><sub><em>n</em></sub> for most regions lacked information on cloudy skies and areas at high latitudes. This information thus cannot be provided by Hi-GLASS yet. Moreover, the influence of the topography on values of <em>R</em><sub><em>n</em></sub> has not been thoroughly considered. Nonetheless, values of <em>R</em><sub><em>n</em></sub> under clear sky obtained from Hi-GLASS offer promise for use ","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"7 ","pages":"Article 100080"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49701455","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":"Improved Landsat-based snow cover mapping accuracy using a spatiotemporal NDSI and generalized linear mixed model","authors":"Charlotte Poussin ,&nbsp;Pablo Timoner ,&nbsp;Bruno Chatenoux ,&nbsp;Gregory Giuliani ,&nbsp;Pascal Peduzzi","doi":"10.1016/j.srs.2023.100078","DOIUrl":"https://doi.org/10.1016/j.srs.2023.100078","url":null,"abstract":"<div><p>Snow cover extent and distribution over the years have a significant impact on hydrological, terrestrial, and climatologic processes. Snow cover mapping accuracy using remote sensing data is then particularly important. This study analyses Landsat-8 NDSI snow cover datasets over time and space using different NDSI-based approach. The objectives are (i) to investigate the relation snow-NDSI with different environmental variables, (ii) to evaluate the accuracy of the common NDSI threshold of 0.4 against <em>in-situ</em> snow depth measurement and (iii) to develop a method that optimises snow cover mapping accuracy and minimises snow cover detection errors of omission and commission. Landsat-8 snow cover datasets were compared to ground snow depth measurements of climate stations over Switzerland for the period 2014–2020. It was found that there is a consistent relationship between NDSI values and land cover type, elevation, seasons, and snow depth measurements. The global NDSI threshold of 0.4 may not be always optimal for the Swiss territory and tends to underestimate the snow cover extent. Best NDSI thresholds vary spatially and are generally lower than 0.4 for the three snow depth threshold tested. We therefore propose a new spatiotemporal NDSI method to maximize snow cover mapping accuracy by using a generalized linear mixed model (GLMM). This model uses three environmental variables (i.e., elevation, land cover type and seasons) and raw NDSI values and improves snow cover mapping accuracy by 24% compared to the fixed threshold of 0.4. By using this method omissions errors decrease considerably while keeping a very low value of commission errors. This method will then be integrated in the Snow Observation from Space (SOfS) algorithm used for snow detection in Switzerland.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"7 ","pages":"Article 100078"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49728553","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":"Near real-time detection of winter cover crop termination using harmonized Landsat and Sentinel-2 (HLS) to support ecosystem assessment","authors":"Feng Gao ,&nbsp;Jyoti Jennewein ,&nbsp;W. Dean Hively ,&nbsp;Alexander Soroka ,&nbsp;Alison Thieme ,&nbsp;Dawn Bradley ,&nbsp;Jason Keppler ,&nbsp;Steven Mirsky ,&nbsp;Uvirkaa Akumaga","doi":"10.1016/j.srs.2022.100073","DOIUrl":"https://doi.org/10.1016/j.srs.2022.100073","url":null,"abstract":"<div><p>Cover crops are planted to reduce soil erosion, increase soil fertility, and improve watershed management. In the Delmarva Peninsula of the eastern United States, winter cover crops are essential for reducing nutrient and sediment losses from farmland. Cost-share programs have been created to incentivize cover crops to achieve conservation objectives. This program required that cover crops be planted and terminated within a specified time window. Usually, farmers report cover crop termination dates for each enrolled field (∼28,000 per year), and conservation district staff confirm the report with field visits within two weeks of termination. This verification process is labor-intensive and time-consuming and became restricted in 2020–2021 due to the COVID-19 pandemic. This study used Harmonized Landsat and Sentinel-2 (HLS, version 2.0) time-series data and the within-season termination (WIST) algorithm to detect cover crop termination dates over Maryland and the Delmarva Peninsula. The estimated remote sensing termination dates were compared to roadside surveys and to farmer-reported termination dates from the Maryland Department of Agriculture database for the 2020–2021 cover crop season. The results show that the WIST algorithm using HLS detected 94% of terminations (statuses) for the enrolled fields (n = 28,190). Among the detected terminations, about 49%, 72%, 84%, and 90% of remote sensing detected termination dates were within one, two, three, and four weeks of agreement to farmer-reported dates, respectively. A real-time simulation showed that the termination dates could be detected one week after termination operation using routinely available HLS data, and termination dates detected after mid-May are more reliable than those from early spring when the Normalized Difference Vegetation Index (NDVI) was low. We conclude that HLS imagery and the WIST algorithm provide a fast and consistent approach for generating near-real-time cover crop termination maps over large areas, which can be used to support cost-share program verification.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"7 ","pages":"Article 100073"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49757555","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":"Sentinel-3 SLSTR active fire (AF) detection and FRP daytime product - Algorithm description and global intercomparison to MODIS, VIIRS and landsat AF data","authors":"Weidong Xu ,&nbsp;Martin J. Wooster","doi":"10.1016/j.srs.2023.100087","DOIUrl":"https://doi.org/10.1016/j.srs.2023.100087","url":null,"abstract":"&lt;div&gt;&lt;p&gt;The Sea and Land Surface Temperature Radiometer (SLSTR) senses the Earth from onboard two concurrently operating European Copernicus Sentinel-3 (S3) satellites. As the Terra platform carrying the Moderate Resolution Imaging Spectroradiometer (MODIS) is reaching its end of life, the S3 Active Fire Detection and FRP products generated from data captured by S3 SLSTR are expected to soon become the main global active fire (AF) product for the mid-morning and evening low Earth orbit timeslots. The S3 night-time AF product issued by the European Space Agency (ESA) has been operational since March 2020, and here we report on the significant adjustments made to enable the generation of a complimentary daytime product. Similar to MODIS, SLSTR possesses two middle infrared channels, both a ‘standard’ (normal gain; S7) channel and a ‘fire’ (low-gain; F1) channel - but in contrast to MODIS by day even the ambient background land surface is often saturated in the SLSTR standard gain MIR (S7) channel. This saturation necessitates far greater use of the F1 channel data by day for active fire detection than by night, even though F1 has characteristics which make its data more challenging to combine with that from the other SLSTR thermal infrared channels. Here we report on the approaches used to combine S7 and F1 data for optimized daytime AF detection, and also detail the other algorithm adjustments found necessary to include in the daytime AF product algorithm. We compare the resulting daytime SLSTR AF product data to that generated from near-simultaneous views provided by MODIS onboard Terra. When both sensors detect the same active fire cluster at similar time, there is minimal bias shown between the two FRP retrievals (the ordinary least squares linear best fit between matched SLSTR and MODIS per-fire FRP matchups has a slope of 0.97). At the regional scale, the S3 product detects 70% of the AF pixels that the matching MODIS product reports, but also provides a further (16%) set of unique AF pixel detections. Regional FRP totals derived from SLSTR appear slightly lower than those from MODIS, and the OLS linear best fit between these regional FRP matchup datasets has a slope of 0.91. This is largely due to SLSTR performing less well in detecting the lowest FRP fires by day, whereas by night the S3 product performs a little better than MODIS due to the increased night-time use of S7 in the earlier AF pixel detection stages. Global fire mapping at a 0.25° grid cell resolution shows very similar daytime fire patterns and FRP totals from S3 and Terra MODIS, with SLSTR detecting around twice the number of AF pixels due to the algorithm being more effective at identifying low FRP pixels at the edges of fire clusters. Regional time series case studies also show very similar temporal patterns between S3 and Terra MODIS. Longer-term intercomparisons such as these will provide the knowledge necessary to use MODIS and SLSTR AF products together to analyse long-","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"7 ","pages":"Article 100087"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49701580","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":"Examination of the amount of GEDI data required to characterize central Africa tropical forest aboveground biomass at REDD+ project scale in Mai Ndombe province","authors":"H.B. Kashongwe ,&nbsp;D.P. Roy ,&nbsp;D.L. Skole","doi":"10.1016/j.srs.2023.100091","DOIUrl":"https://doi.org/10.1016/j.srs.2023.100091","url":null,"abstract":"&lt;div&gt;&lt;p&gt;The Global Ecosystem Dynamics Investigation (GEDI) is the first spaceborne LiDAR designed to improve quantification of vegetation structure and forest aboveground biomass (AGB) including in the tropics where forest AGB inventory data are limited. GEDI is a sampling instrument on the International Space Station (ISS) and does not provide data on a regular, systematic basis. Reducing Emissions from Deforestation and Degradation and enhancement of carbon stocks (REDD+) projects require forest AGB inventories to quantify avoided carbon emissions achieved by conserving forest biomass. Although there is high confidence that GEDI can retrieve measurements that allow estimation of AGB at scale, less is known about how well its operational deployment performs for measurement of AGB to support REDD+ projects. This includes an understanding of the appropriate time period required to collect sufficient GEDI observations for reliable forest AGB assessment. This paper describes the first study to examine the amount of GEDI data needed to characterize tropical forest AGB at REDD+ project scale. In tropical Africa, the average REDD+ project size documented by the Center for International Forestry Research is equivalent to a square area of approximately 50 × 50 km (250,000 ha). Recently available good quality GEDI footprint-level AGB product data acquired over a 31 month period over Mai Ndombe province in the west of the Democratic Republic of the Congo were considered. A global 30 m percent tree cover product, updated with contemporary mapped forest cover loss, was used to map the intact forest across the province. Fifteen 50 × 50 km test sites, representing example REDD+ project areas with &gt;80% forest cover and good quality AGB forest footprint data distributed across each site, were selected. The sites were selected from five AGB stratum defined from the GEDI data, and with three sites selected per stratum that had low, medium and high semivariogram sill values that reflect increasing within-site AGB spatial variation. The overall mean GEDI AGB (OMGA) was derived from all the good quality forest GEDI footprint AGB values acquired over the 31 months of GEDI operation at each site. The expected minimum number of GEDI orbits (&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;msubsup&gt;&lt;mi&gt;n&lt;/mi&gt;&lt;mrow&gt;&lt;mi&gt;o&lt;/mi&gt;&lt;mi&gt;r&lt;/mi&gt;&lt;mi&gt;b&lt;/mi&gt;&lt;mi&gt;i&lt;/mi&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;mi&gt;s&lt;/mi&gt;&lt;/mrow&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;/msubsup&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;) required to characterize the OMGA to within &lt;em&gt;p&lt;/em&gt; = ±5%, ±10%, and ±20% was derived by considering different combinations of GEDI orbits randomly selected from the 31 months of GEDI data. The expected minimum number of days (&lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;msubsup&gt;&lt;mi&gt;n&lt;/mi&gt;&lt;mrow&gt;&lt;mi&gt;d&lt;/mi&gt;&lt;mi&gt;a&lt;/mi&gt;&lt;mi&gt;y&lt;/mi&gt;&lt;mi&gt;s&lt;/mi&gt;&lt;/mrow&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;/msubsup&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;) required to characterize the AGB over each site was derived by multiplying the site &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;msubsup&gt;&lt;mi&gt;n&lt;/mi&gt;&lt;mrow&gt;&lt;mi&gt;o&lt;/mi&gt;&lt;mi&gt;r&lt;/mi&gt;&lt;mi&gt;b&lt;/mi&gt;&lt;mi&gt;i&lt;/mi&gt;&lt;mi&gt;t&lt;/mi&gt;&lt;mi&gt;s&lt;/mi&gt;&lt;/mrow&gt;&lt;mi&gt;p&lt;/mi&gt;&lt;/msubsup&gt;&lt;/mrow&gt;&lt;/m","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"7 ","pages":"Article 100091"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49701317","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":"Improved terrain estimation from spaceborne lidar in tropical peatlands using spatial filtering","authors":"Alexander R. Cobb ,&nbsp;René Dommain ,&nbsp;Rahayu S. Sukri ,&nbsp;Faizah Metali ,&nbsp;Bodo Bookhagen ,&nbsp;Charles F. Harvey ,&nbsp;Hao Tang","doi":"10.1016/j.srs.2022.100074","DOIUrl":"https://doi.org/10.1016/j.srs.2022.100074","url":null,"abstract":"<div><p>Tropical peatlands are estimated to hold carbon stocks of 70 Pg C or more as partly decomposed organic matter, or peat. Peat may accumulate over thousands of years into gently mounded deposits called peat domes with a relief of several meters over distances of kilometers. The mounded shapes of tropical peat domes account for much of the carbon storage in these landscapes, but their subtle topographic relief is difficult to measure. As many of the world's tropical peatlands are remote and inaccessible, spaceborne laser altimetry data from missions such as NASA's Global Ecosystem Dynamics Investigation (GEDI) on the International Space Station (ISS) and the Advanced Topographic Laser Altimeter System (ATLAS) instrument on the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory could help to describe these deposits. We evaluate retrieval of ground elevations derived from GEDI waveform data, as well as single-photon data from ATLAS, with reference to an airborne lidar dataset covering an area of over 300 km² in the Belait District of Brunei Darussalam on the island of Borneo. Spatial filtering of GEDI L2A version 2, algorithm 1 quality data reduced mean absolute deviations from airborne-lidar-derived ground elevations from 8.35 m to 1.83 m, root-mean-squared error from 15.98 m to 1.97 m, and unbiased root-mean-squared error from 13.62 m to 0.72 m. Similarly, spatial filtering of ATLAS ATL08 version 3 ground photons from strong beams at night reduced mean absolute deviations from 1.51 m to 0.64 m, root-mean-squared error from 3.85 m to 0.77 m, and unbiased root-mean-squared error from 3.54 m to 0.44 m. We conclude that despite sparse ground retrievals, these spaceborne platforms can provide useful data for tropical peatland surface altimetry if postprocessed with a spatial filter.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"7 ","pages":"Article 100074"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49703390","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":"Generation and comprehensive validation of 30 m conterminous United States Landsat percent tree cover and forest cover loss annual products","authors":"Alexey Egorov ,&nbsp;David P. Roy ,&nbsp;Luigi Boschetti","doi":"10.1016/j.srs.2023.100084","DOIUrl":"https://doi.org/10.1016/j.srs.2023.100084","url":null,"abstract":"<div><p>This study describes the generation and comprehensive validation of 30 m Landsat-based annual percent tree cover and forest cover loss products for the conterminous United States (CONUS). The products define (i) forest status with respect to three thematic classes: stable forest, stable non-forest, forest cover loss, (ii) percent tree cover (PTC, 0–100%), (iii) percent tree cover decrease (ΔPTC), and (iv) the Landsat acquisition dates bounding mapped forest cover loss occurrence. Forest was defined, based on the U.S. federal government forested land definition, as 30 m pixels with mapped PTC &gt;10%. Annual products were derived using temporally overlapping 9-year periods (mapping within each central 5-year period) of USGS Landsat Analysis Ready Data (ARD) with reconciliation of the results between periods. The products for 2013 are presented and were validated rigorously by comparison with 1910 30 m independent reference data interpreted from bi-temporal &lt;1 m resolution aerial imagery selected using a Stage 3 CONUS stratified random sampling design. The stable forest, stable non-forest, and forest cover loss results were validated using standard accuracy metrics derived from the confusion matrix. The overall accuracy was high (0.92), and class-specific user's accuracy (UA) and producer's accuracy (PA) metrics were also high for the stable forest (UA = 0.94, PA = 0.84) and stable non-forest (UA = 0.90, PA = 0.97) classes. The forest cover loss class had similarly high UA (0.89) but significantly lower PA (0.61) indicating non-negligible omission errors. All standard errors were &lt;5%. The total area of stable forest over CONUS for year 2013 was estimated as 3,049,380 ± 114,392 km² and the total area of forest cover loss was estimated as 31,382 ± 4751 km², with 95% confidence interval. The PTC and ΔPTC products were validated by linear regression with the reference data, indicating good PTC precision reflected by a high coefficient of determination (R² = 0.79), and accuracy with a regression slope close to unity (0.86) and small intercept (3.48). The regression between mapped ΔPTC and the reference data had a high coefficient of determination (R² = 0.74) but a regression slope further away from unity (0.78) and small intercept (1.68) consistent with the forest cover loss omission errors revealed by the confusion matrix. State-level comparison of the stable forest mapped area with forest land area statistics published by the U.S. federal government for the 48 CONUS states indicated reasonable correspondence (R² = 0.97) but with a 1.15 regression line slope indicating relative over estimation of the mapped stable forest area, likely related to forest land reporting differences.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"7 ","pages":"Article 100084"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49728478","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":"Modelling non-linear deforestation trends for an ecological tension zone in Brazil","authors":"Vilane Gonçalves Sales","doi":"10.1016/j.srs.2023.100076","DOIUrl":"https://doi.org/10.1016/j.srs.2023.100076","url":null,"abstract":"<div><p>Tropical deforestation is a recent phenomenon that started in the second part of the twentieth century. One may argue that the Brazilian state of Maranhão is an excellent case study for ex-amining deforestation trends and the effects of environmental policies. A man-made line sepa-rates Maranhão into two sections. Due to the administrative divide between the Legal Amazon Maranhão (LM) and the Cerrado Maranhão (CM), one may hypothesize about differences in deforestation between the two regions. This research employs a nonlinear modelling approach based on Generalized Additive Models (GAMs) with a quasi-Poisson distribution and a logarith-mic function to detect deforestation patterns in these areas. Deforestation is linked to the year and a variety of climatic variables. These covariates differ substantially across seasons (rainy and dry) and regions. During times of above-average precipitation, including in the dry and wet seasons, deforestation occurred in the LM area. However, in the non-enforced region, this regime was not followed. According to the statistics, deforestation decreased in the LM region when precipitation levels were below average.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"7 ","pages":"Article 100076"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49701488","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":"UAV-acquired imagery with photogrammetry provides accurate measures of mudflat elevation gradients and microtopography for investigating microphytobenthos patterning","authors":"Tristan J. Douglas ,&nbsp;Nicholas C. Coops ,&nbsp;Mark C. Drever","doi":"10.1016/j.srs.2023.100089","DOIUrl":"https://doi.org/10.1016/j.srs.2023.100089","url":null,"abstract":"<div><p>Intertidal mudflats are highly productive ecosystems where elevation gradients and complex microtopography drive the growth of benthic microalgae (microphytobenthos; MPB) that form the basis of estuarine foodwebs and are crucial for nutrient cycling, shoreline stabilization, and the persistence of marine and coastal species. Mudflat ecosystems are threatened by human activity and natural stressors and thus need to be mapped and monitored. Unoccupied aerial vehicle (UAV) technologies and digital aerial photogrammetry (DAP) have been successfully implemented to study mudflat environments. However, standardizing the UAV flight parameters needed for optimal DAP performance on mudflats remains outstanding. Here, we systematically determined the optimal flight parameters for collection and photogrammetric processing of UAV-acquired data on mudflats by (1) testing across-track overlap (50, 70, and 90%) and flight elevation (73 m and 110 m) parameters, assessing the accuracy of DAP results against reference data from a mobile laser scanner (MLS), and (2) comparing semi-variograms of digital surface models (DSMs) from two UAV flight elevations. We found that all combinations of UAV flight parameters yielded accurate DAP products; flight elevation had a marginal effect on image alignment and had no effect on accuracy, while across-track overlap had no effect on image alignment of DSM of difference (DoD) values. All UAV and MLS point clouds were aligned with and accuracy of &lt; 0.016 m and absolute values of mean DoDs were all sub-millimeter, ranging from 0.0001 ± 0.0322 to 0.0083 ± 0.0270 m. We conclude that conducting UAV surveys at 110 m elevation with 50% across-track image overlap is sufficient for high-accuracy DAP in mudflats. Finally, we tested the utility of such fine-scale topographic data for ecological applications by comparing elevation and topographic position indices (TPI) of DAP-derived DSMs to MPB abundance, measured as chlorophyll <em>a</em> (chl-<em>a</em>), calculated from UAV-acquired NDVI data. We found that elevation and TPI account for 1.6–17% of the variation in chl-<em>a</em> concentration, and that these relationships depend on distance from shore and mudflat morphology. Our findings contribute to standardizing the application of UAV technologies in mudflats and demonstrate the potential of UAV-acquired data for modeling the relationship between microtopography and MPB on ecologically important mudflats.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"7 ","pages":"Article 100089"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49701519","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":"Exploring the effects of training samples on the accuracy of crop mapping with machine learning algorithm","authors":"Yangyang Fu ,&nbsp;Ruoque Shen ,&nbsp;Chaoqing Song ,&nbsp;Jie Dong ,&nbsp;Wei Han ,&nbsp;Tao Ye ,&nbsp;Wenping Yuan","doi":"10.1016/j.srs.2023.100081","DOIUrl":"https://doi.org/10.1016/j.srs.2023.100081","url":null,"abstract":"<div><p>Machine learning algorithms are a frequently used crop classification method and have been applied to identify the distribution of various crops over regional and national scales. Previous studies have underscored that the number of training samples strongly influences the classification accuracy of machine learning algorithms, resulting in extensive training sample collection efforts. This study, taking winter wheat as an example, challenges the above principle by selecting training samples with the time-weighted dynamic time warping (TWDTW) method and finds that the classification accuracy of machine learning algorithms highly relies on the representativeness and proportion of training samples rather than the quantity. With the increase of the representativeness of training samples, i.e. more comprehensively reflected the characteristics of winter wheat, the classification accuracy is continually improved. The best classification accuracy is further achieved when selecting the training samples of winter wheat and non-winter wheat according to the ratio of their statistical areas. On the contrary, only a slight difference was found in overall accuracy (91.26% and 90.74%), producer’s accuracy (86.33% and 86.65%) and user’s accuracy (97.37% and 96.01%) when using 1,000 and 10,000 training samples. Overall, this study demonstrates that the characteristics of training samples have a great impact on the classification accuracy of machine learning algorithms, and the training samples generated by TWDTW method are reliable for crop mapping.</p></div>","PeriodicalId":101147,"journal":{"name":"Science of Remote Sensing","volume":"7 ","pages":"Article 100081"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49701606","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}