Yixin Luo, Jiaming Han, Zhou Liu, Mi Wang, Guisong Xia
Instance segmentation in aerial images is an important and challenging task. Most of the existing methods have adapted instance segmentation algorithms developed for natural images to aerial images. However, these methods easily suffer from performance degradation in aerial images, due to the scale variations, large aspect ratios, and arbitrary orientations of instances caused by the bird’s-eye view of aerial images. To address this issue, we propose an elliptic centerness (EC) for instance segmentation in aerial images, which can assign the proper centerness values to the intricate aerial instances and thus mitigate the performance degradation. Specifically, we introduce ellipses to fit the various contours of aerial instances and measure these fitted ellipses by two-dimensional anisotropic Gaussian distribution. Armed with EC, we develop a one-stage aerial instance segmentation network. Extensive experiments on a commonly used dataset, the instance segmentation in aerial images dataset (iSAID), demonstrate that our proposed method can achieve a remarkable performance of instance segmentation while introducing negligible computational cost.
{"title":"An Elliptic Centerness for Object Instance Segmentation in Aerial Images","authors":"Yixin Luo, Jiaming Han, Zhou Liu, Mi Wang, Guisong Xia","doi":"10.34133/2022/9809505","DOIUrl":"https://doi.org/10.34133/2022/9809505","url":null,"abstract":"Instance segmentation in aerial images is an important and challenging task. Most of the existing methods have adapted instance segmentation algorithms developed for natural images to aerial images. However, these methods easily suffer from performance degradation in aerial images, due to the scale variations, large aspect ratios, and arbitrary orientations of instances caused by the bird’s-eye view of aerial images. To address this issue, we propose an elliptic centerness (EC) for instance segmentation in aerial images, which can assign the proper centerness values to the intricate aerial instances and thus mitigate the performance degradation. Specifically, we introduce ellipses to fit the various contours of aerial instances and measure these fitted ellipses by two-dimensional anisotropic Gaussian distribution. Armed with EC, we develop a one-stage aerial instance segmentation network. Extensive experiments on a commonly used dataset, the instance segmentation in aerial images dataset (iSAID), demonstrate that our proposed method can achieve a remarkable performance of instance segmentation while introducing negligible computational cost.","PeriodicalId":38304,"journal":{"name":"Yaogan Xuebao/Journal of Remote Sensing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44538513","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}
Morel’s “Optical modeling of the upper ocean in relation to its biogenous matter content (Case I waters)” (J. Geophys. Res. - Oceans, Vol. 93, pp. 107,49-10,768, 1988) laid the groundwork to model the optical properties of natural waters based on the concentration of chlorophyll ([Chl], in mg/m3). As stated in the abstract, it aims “tentatively to interpret the optical behavior of oceanic case-I waters,” where “Chlorophyll-like pigment concentration is used as the index to quantify the algal materials,” because [Chl] is routinely measured in marine/oceanic surveys. Specifically, Morel developed “statistical relationships between this index and the depth of euphotic layer, the spectral values of the attenuation coefficient for downwelling irradiance, or the scattering coefficient,” and further, “a pigment-dependent optical model is developed.” Thus, such a system allows many aspects of oceanographic applications when [Chl] (“this index”) is provided. In part, this system established [Chl] at the core of traditional ocean color remote sensing. To implement this system, however, it is necessary to have a complete understanding of the definition and evolution of this Case-1/Case-2 system, especially the qualitative definition of Case-1/Case-2 vs. the practical separation of Case-1/Case-2 as well as the quantitative modeling of the optical properties of Case-1 waters.
{"title":"The Two Faces of “Case-1” Water","authors":"Z. Lee, Jun-wu Tang","doi":"10.34133/2022/9767452","DOIUrl":"https://doi.org/10.34133/2022/9767452","url":null,"abstract":"Morel’s “Optical modeling of the upper ocean in relation to its biogenous matter content (Case I waters)” (J. Geophys. Res. - Oceans, Vol. 93, pp. 107,49-10,768, 1988) laid the groundwork to model the optical properties of natural waters based on the concentration of chlorophyll ([Chl], in mg/m3). As stated in the abstract, it aims “tentatively to interpret the optical behavior of oceanic case-I waters,” where “Chlorophyll-like pigment concentration is used as the index to quantify the algal materials,” because [Chl] is routinely measured in marine/oceanic surveys. Specifically, Morel developed “statistical relationships between this index and the depth of euphotic layer, the spectral values of the attenuation coefficient for downwelling irradiance, or the scattering coefficient,” and further, “a pigment-dependent optical model is developed.” Thus, such a system allows many aspects of oceanographic applications when [Chl] (“this index”) is provided. In part, this system established [Chl] at the core of traditional ocean color remote sensing. To implement this system, however, it is necessary to have a complete understanding of the definition and evolution of this Case-1/Case-2 system, especially the qualitative definition of Case-1/Case-2 vs. the practical separation of Case-1/Case-2 as well as the quantitative modeling of the optical properties of Case-1 waters.","PeriodicalId":38304,"journal":{"name":"Yaogan Xuebao/Journal of Remote Sensing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49620938","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}
Chi Li, Xiaoguang Xu, Xiong Liu, Jun Wang, K. Sun, J. van Geffen, Qindan Zhu, Jianzhong Ma, J. Jin, K. Qin, Qin He, P. Xie, Bo Ren, R. Cohen
Satellite retrievals of columnar nitrogen dioxide (NO2) are essential for the characterization of nitrogen oxides (NOx) processes and impacts. The requirements of modeled a priori profiles present an outstanding bottleneck in operational satellite NO2 retrievals. In this work, we instead use neural network (NN) models trained from over 360,000 radiative transfer (RT) simulations to translate satellite radiances across 390-495 nm to total NO2 vertical column (NO2C). Despite the wide variability of the many input parameters in the RT simulations, only a small number of key variables were found essential to the accurate prediction of NO2C, including observing angles, surface reflectivity and altitude, and several key principal component scores of the radiances. In addition to the NO2C, the NN training and cross-validation experiments show that the wider retrieval window allows some information about the vertical distribution to be retrieved (e.g., extending the rightmost wavelength from 465 to 495 nm decreases the root-mean-square-error by 0.75%) under high-NO2C conditions. Applying to four months of TROPOMI data, the trained NN model shows strong ability to reproduce the NO2C observed by the ground-based Pandonia Global Network. The coefficient of determination (R2, 0.75) and normalized mean bias (NMB, -33%) are competitive with the level 2 operational TROPOMI product (R2=0.77, NMB=−29%) over clear (geometric cloud fraction<0.2) and polluted (NO2C≥7.5×1015 molecules/cm2) regions. The NN retrieval approach is ~12 times faster than predictions using high spatial resolution (~3 km) a priori profiles from chemical transport modeling, which is especially attractive to the handling of large volume satellite data.
{"title":"Direct Retrieval of NO2 Vertical Columns from UV-Vis (390-495 nm) Spectral Radiances Using a Neural Network","authors":"Chi Li, Xiaoguang Xu, Xiong Liu, Jun Wang, K. Sun, J. van Geffen, Qindan Zhu, Jianzhong Ma, J. Jin, K. Qin, Qin He, P. Xie, Bo Ren, R. Cohen","doi":"10.34133/2022/9817134","DOIUrl":"https://doi.org/10.34133/2022/9817134","url":null,"abstract":"Satellite retrievals of columnar nitrogen dioxide (NO2) are essential for the characterization of nitrogen oxides (NOx) processes and impacts. The requirements of modeled a priori profiles present an outstanding bottleneck in operational satellite NO2 retrievals. In this work, we instead use neural network (NN) models trained from over 360,000 radiative transfer (RT) simulations to translate satellite radiances across 390-495 nm to total NO2 vertical column (NO2C). Despite the wide variability of the many input parameters in the RT simulations, only a small number of key variables were found essential to the accurate prediction of NO2C, including observing angles, surface reflectivity and altitude, and several key principal component scores of the radiances. In addition to the NO2C, the NN training and cross-validation experiments show that the wider retrieval window allows some information about the vertical distribution to be retrieved (e.g., extending the rightmost wavelength from 465 to 495 nm decreases the root-mean-square-error by 0.75%) under high-NO2C conditions. Applying to four months of TROPOMI data, the trained NN model shows strong ability to reproduce the NO2C observed by the ground-based Pandonia Global Network. The coefficient of determination (R2, 0.75) and normalized mean bias (NMB, -33%) are competitive with the level 2 operational TROPOMI product (R2=0.77, NMB=−29%) over clear (geometric cloud fraction<0.2) and polluted (NO2C≥7.5×1015 molecules/cm2) regions. The NN retrieval approach is ~12 times faster than predictions using high spatial resolution (~3 km) a priori profiles from chemical transport modeling, which is especially attractive to the handling of large volume satellite data.","PeriodicalId":38304,"journal":{"name":"Yaogan Xuebao/Journal of Remote Sensing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48607087","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}
Ice-rich permafrost thaws as a result of Arctic warming, and the land surface collapses to form characteristic thermokarst landscapes. Thermokarst landscapes can bring instability to the permafrost layer, affecting regional geomorphology, hydrology, and ecology and may further lead to permafrost degradation and greenhouse gas emissions. Field observations in permafrost regions are often limited, while satellite imagery provides a valuable record of land surface dynamics. Currently, continuous monitoring of regional-scale thermokarst landscape dynamics and disturbances remains a challenging task. In this study, we combined the Theil–Sen estimator with the LandTrendr algorithm to create a process flow for monitoring thermokarst landscape dynamics in Arctic permafrost region on the Google Earth Engine platform. A robust linear trend analysis of the Landsat Tasseled Cap index time series based on the Theil–Sen estimator and Mann–Kendall test showed the overall trends in greenness, wetness, and brightness in northern Alaska over the past 20 years. Six types of disturbances that occur in thermokarst landscape were demonstrated and highlighted, including long-term processes (thermokarst lake expansion, shoreline retreat, and river erosion) and short-term events (thermokarst lake drainage, wildfires, and abrupt vegetation change). These disturbances are widespread throughout the Arctic permafrost region and represent hotspots of abrupt permafrost thaw in a warming context, which would destabilize fragile thermokarst landscapes rich in soil organic carbon and affect the ecological carbon balance. The cases we present provide a basis for understanding and quantifying specific disturbance analyses that will facilitate the integration of thermokarst processes into climate models.
{"title":"Landsat-Based Monitoring of Landscape Dynamics in Arctic Permafrost Region","authors":"Yating Chen, Aobo Liu, Xiao Cheng","doi":"10.34133/2022/9765087","DOIUrl":"https://doi.org/10.34133/2022/9765087","url":null,"abstract":"Ice-rich permafrost thaws as a result of Arctic warming, and the land surface collapses to form characteristic thermokarst landscapes. Thermokarst landscapes can bring instability to the permafrost layer, affecting regional geomorphology, hydrology, and ecology and may further lead to permafrost degradation and greenhouse gas emissions. Field observations in permafrost regions are often limited, while satellite imagery provides a valuable record of land surface dynamics. Currently, continuous monitoring of regional-scale thermokarst landscape dynamics and disturbances remains a challenging task. In this study, we combined the Theil–Sen estimator with the LandTrendr algorithm to create a process flow for monitoring thermokarst landscape dynamics in Arctic permafrost region on the Google Earth Engine platform. A robust linear trend analysis of the Landsat Tasseled Cap index time series based on the Theil–Sen estimator and Mann–Kendall test showed the overall trends in greenness, wetness, and brightness in northern Alaska over the past 20 years. Six types of disturbances that occur in thermokarst landscape were demonstrated and highlighted, including long-term processes (thermokarst lake expansion, shoreline retreat, and river erosion) and short-term events (thermokarst lake drainage, wildfires, and abrupt vegetation change). These disturbances are widespread throughout the Arctic permafrost region and represent hotspots of abrupt permafrost thaw in a warming context, which would destabilize fragile thermokarst landscapes rich in soil organic carbon and affect the ecological carbon balance. The cases we present provide a basis for understanding and quantifying specific disturbance analyses that will facilitate the integration of thermokarst processes into climate models.","PeriodicalId":38304,"journal":{"name":"Yaogan Xuebao/Journal of Remote Sensing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43853375","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}
Liangfu Chen, H. Letu, M. Fan, Huazhe Shang, J. Tao, Laixiong Wu, Y. Zhang, Chao Yu, Jianbin Gu, Ning Zhang, Jin Hong, Zhongting Wang, Tianyu Zhang
The Chinese High-resolution Earth Observation System (CHEOS) program has successfully launched 7 civilian satellites since 2010. These satellites are named by Gaofen (meaning high resolution in Chinese, hereafter noted as GF). To combine the advantages of high temporal and comparably high spatial resolution, diverse sensors are deployed to each satellite. GF-1 and GF-6 carry both high-resolution cameras (2 m resolution panchromatic and 8 m resolution multispectral camera), providing high spatial imaging for land use monitoring; GF-3 is equipped with a C-band multipolarization synthetic aperture radar with a spatial resolution of up to 1 meter, mostly monitoring marine targets; GF-5 carried 6 sensors including hyperspectral camera and directional polarization camera, dedicated to environmental remote sensing and climate research, such as aerosol, clouds, and greenhouse gas monitoring; and GF-7 laser altimeter system payload enables a three-dimensional surveying and mapping of natural resource and land surveying, facilitating the accumulation of basic geographic information. This study provides an overview of GF civilian series satellites, especially their missions, sensors, and applications.
{"title":"An Introduction to the Chinese High-Resolution Earth Observation System: Gaofen-1~7 Civilian Satellites","authors":"Liangfu Chen, H. Letu, M. Fan, Huazhe Shang, J. Tao, Laixiong Wu, Y. Zhang, Chao Yu, Jianbin Gu, Ning Zhang, Jin Hong, Zhongting Wang, Tianyu Zhang","doi":"10.34133/2022/9769536","DOIUrl":"https://doi.org/10.34133/2022/9769536","url":null,"abstract":"The Chinese High-resolution Earth Observation System (CHEOS) program has successfully launched 7 civilian satellites since 2010. These satellites are named by Gaofen (meaning high resolution in Chinese, hereafter noted as GF). To combine the advantages of high temporal and comparably high spatial resolution, diverse sensors are deployed to each satellite. GF-1 and GF-6 carry both high-resolution cameras (2 m resolution panchromatic and 8 m resolution multispectral camera), providing high spatial imaging for land use monitoring; GF-3 is equipped with a C-band multipolarization synthetic aperture radar with a spatial resolution of up to 1 meter, mostly monitoring marine targets; GF-5 carried 6 sensors including hyperspectral camera and directional polarization camera, dedicated to environmental remote sensing and climate research, such as aerosol, clouds, and greenhouse gas monitoring; and GF-7 laser altimeter system payload enables a three-dimensional surveying and mapping of natural resource and land surveying, facilitating the accumulation of basic geographic information. This study provides an overview of GF civilian series satellites, especially their missions, sensors, and applications.","PeriodicalId":38304,"journal":{"name":"Yaogan Xuebao/Journal of Remote Sensing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46212128","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}
Gaofei Yin, A. Verger, Adrià Descals, I. Filella, J. Peñuelas
The chlorophyll/carotenoid index (CCI) is increasingly used for remotely tracking the phenology of photosynthesis. However, CCI is restricted to few satellites incorporating the 531 nm band. This study reveals that the Moderate Resolution Imaging Spectroradiometer (MODIS) broadband green reflectance (band 4) is significantly correlated with this xanthophyll-sensitive narrowband (band 11) (R2=0.98,p<0.001), and consequently, the broadband green-red vegetation index GRVI—computed with MODIS band 1 and band 4—is significantly correlated with CCI—computed with MODIS band 1 and band 11 (R2=0.97,p<0.001). GRVI and CCI performed similarly in extracting phenological metrics of the dates of the start and end of the season (EOS) when evaluated with gross primary production (GPP) measurements from eddy covariance towers. For EOS extraction of evergreen needleleaf forest, GRVI even overperformed solar-induced chlorophyll fluorescence which is seen as a direct proxy of plant photosynthesis. This study opens the door for GPP and photosynthetic phenology monitoring from a wide set of sensors with broadbands in the green and red spectral regions.
{"title":"A Broadband Green-Red Vegetation Index for Monitoring Gross Primary Production Phenology","authors":"Gaofei Yin, A. Verger, Adrià Descals, I. Filella, J. Peñuelas","doi":"10.34133/2022/9764982","DOIUrl":"https://doi.org/10.34133/2022/9764982","url":null,"abstract":"The chlorophyll/carotenoid index (CCI) is increasingly used for remotely tracking the phenology of photosynthesis. However, CCI is restricted to few satellites incorporating the 531 nm band. This study reveals that the Moderate Resolution Imaging Spectroradiometer (MODIS) broadband green reflectance (band 4) is significantly correlated with this xanthophyll-sensitive narrowband (band 11) (R2=0.98,p<0.001), and consequently, the broadband green-red vegetation index GRVI—computed with MODIS band 1 and band 4—is significantly correlated with CCI—computed with MODIS band 1 and band 11 (R2=0.97,p<0.001). GRVI and CCI performed similarly in extracting phenological metrics of the dates of the start and end of the season (EOS) when evaluated with gross primary production (GPP) measurements from eddy covariance towers. For EOS extraction of evergreen needleleaf forest, GRVI even overperformed solar-induced chlorophyll fluorescence which is seen as a direct proxy of plant photosynthesis. This study opens the door for GPP and photosynthetic phenology monitoring from a wide set of sensors with broadbands in the green and red spectral regions.","PeriodicalId":38304,"journal":{"name":"Yaogan Xuebao/Journal of Remote Sensing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44988164","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}
Wendian Lai, Z. Lee, Junwei Wang, Yongchao Wang, Rodrigo A. Garcia, Huaguo Zhang
Bottom depth (H) of optically shallow waters can be retrieved from multiband imagery, where remote sensing reflectance (Rrs) are commonly used as the input. Because of the difficulties of removing the atmospheric effects in coastal areas, quite often, there are no valid Rrs from satellites for the retrieval of H. More importantly, the empirical algorithms for H are hardly portable to new measurements. In this study, using data from Landsat-8 and ICESat-2 as examples, we present an approach to retrieve H directly from the top-of-atmosphere (TOA) data. It not only bypasses the requirement to correct the effects of aerosols but also shows promising portability to areas not included in algorithm development. Specifically, we use Rayleigh-corrected TOA reflectance (ρrc) in the 443–2300 nm range as input, along with a multilayer perceptron (MLPHρrc), for the retrieval of H. More than 78,000 matchup points of ρrc and H (0–25 m) were used to train MLPHρrc, which resulted in a Mean Absolute Percentage Difference (MARD) of 8.8% and a coefficient of determination (R2) of 0.96. This MLPHρrc was further applied to Landsat-8 data of six regions not included in the training phase, generating MARD and R2 values of 8.3% and 0.98, respectively. In contrast, a conventional two-band ratio algorithm with Rrs as the input generated MARD and R2 values of 31.6% and 0.68 and significantly fewer H retrievals due to failures in atmospheric correction. These results indicate a breakthrough of algorithm portability of MLPHρrc in sensing H of optically shallow waters.
{"title":"A Portable Algorithm to Retrieve Bottom Depth of Optically Shallow Waters from Top-Of-Atmosphere Measurements","authors":"Wendian Lai, Z. Lee, Junwei Wang, Yongchao Wang, Rodrigo A. Garcia, Huaguo Zhang","doi":"10.34133/2022/9831947","DOIUrl":"https://doi.org/10.34133/2022/9831947","url":null,"abstract":"Bottom depth (H) of optically shallow waters can be retrieved from multiband imagery, where remote sensing reflectance (Rrs) are commonly used as the input. Because of the difficulties of removing the atmospheric effects in coastal areas, quite often, there are no valid Rrs from satellites for the retrieval of H. More importantly, the empirical algorithms for H are hardly portable to new measurements. In this study, using data from Landsat-8 and ICESat-2 as examples, we present an approach to retrieve H directly from the top-of-atmosphere (TOA) data. It not only bypasses the requirement to correct the effects of aerosols but also shows promising portability to areas not included in algorithm development. Specifically, we use Rayleigh-corrected TOA reflectance (ρrc) in the 443–2300 nm range as input, along with a multilayer perceptron (MLPHρrc), for the retrieval of H. More than 78,000 matchup points of ρrc and H (0–25 m) were used to train MLPHρrc, which resulted in a Mean Absolute Percentage Difference (MARD) of 8.8% and a coefficient of determination (R2) of 0.96. This MLPHρrc was further applied to Landsat-8 data of six regions not included in the training phase, generating MARD and R2 values of 8.3% and 0.98, respectively. In contrast, a conventional two-band ratio algorithm with Rrs as the input generated MARD and R2 values of 31.6% and 0.68 and significantly fewer H retrievals due to failures in atmospheric correction. These results indicate a breakthrough of algorithm portability of MLPHρrc in sensing H of optically shallow waters.","PeriodicalId":38304,"journal":{"name":"Yaogan Xuebao/Journal of Remote Sensing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45583412","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}
Guangwei Chen, Runjie Jin, Zhanjiang Ye, Qi Li, J. Gu, Min Luo, Yongming Luo, G. Christakos, J. Morris, Junyu He, Dan Li, Hengwei Wang, Li Song, Qiuxuan Wang, Jiaping Wu
This study mapped the areal extent, identified the species composition, and analyzed the changes of salt marshes in the intertidal zone of China during the period 1985–2019. With the aid of the cloud platform of the Google Earth Engine, we selected Landsat 5/8 and Sentinel-2 images and used the support vector machine classification method to extract salt marsh information for the years of 1985, 1990, 1995, 2000, 2005, 2010, 2015, and 2019. Seven major species of salt marshes: Phragmites australis, Suaeda spp., Spartina alterniflora, Scirpus mariqueter, Tamarix chinensis, Cyperus malaccensis, and Sesuvium portulacastrum were identified. Our results showed that salt marshes are mainly distributed in Liaoning, Shandong, Jiangsu, Shanghai, and Zhejiang, with varying patterns of shrinking, expansion, or wavering in different places. The distribution of salt marshes has declined considerably from 151,324 ha in 1985 to 115,397 ha in 2019, a drop of 23.7%. During the same period, the area of native species has dropped 95.4% from 77,741 ha to 3,563 ha for Suaeda spp. and 45.1% from 60,511 ha to 33,193 ha for P. australis; on the contrary, the area of exotic species, S. alterniflora, has exhibited a sharp rise from just 99 ha to 67,527 ha. For the past 35 years, the driving factors causing salt marsh changes are mainly land reclamation, variations in water and sand fluxes, and interspecific competition and succession of salt marsh vegetation. These results provide fundamental reference information and could form the scientific basis for formulating policies for the conservation and utilization of salt marsh resources in China.
{"title":"Spatiotemporal Mapping of Salt Marshes in the Intertidal Zone of China during 1985–2019","authors":"Guangwei Chen, Runjie Jin, Zhanjiang Ye, Qi Li, J. Gu, Min Luo, Yongming Luo, G. Christakos, J. Morris, Junyu He, Dan Li, Hengwei Wang, Li Song, Qiuxuan Wang, Jiaping Wu","doi":"10.34133/2022/9793626","DOIUrl":"https://doi.org/10.34133/2022/9793626","url":null,"abstract":"This study mapped the areal extent, identified the species composition, and analyzed the changes of salt marshes in the intertidal zone of China during the period 1985–2019. With the aid of the cloud platform of the Google Earth Engine, we selected Landsat 5/8 and Sentinel-2 images and used the support vector machine classification method to extract salt marsh information for the years of 1985, 1990, 1995, 2000, 2005, 2010, 2015, and 2019. Seven major species of salt marshes: Phragmites australis, Suaeda spp., Spartina alterniflora, Scirpus mariqueter, Tamarix chinensis, Cyperus malaccensis, and Sesuvium portulacastrum were identified. Our results showed that salt marshes are mainly distributed in Liaoning, Shandong, Jiangsu, Shanghai, and Zhejiang, with varying patterns of shrinking, expansion, or wavering in different places. The distribution of salt marshes has declined considerably from 151,324 ha in 1985 to 115,397 ha in 2019, a drop of 23.7%. During the same period, the area of native species has dropped 95.4% from 77,741 ha to 3,563 ha for Suaeda spp. and 45.1% from 60,511 ha to 33,193 ha for P. australis; on the contrary, the area of exotic species, S. alterniflora, has exhibited a sharp rise from just 99 ha to 67,527 ha. For the past 35 years, the driving factors causing salt marsh changes are mainly land reclamation, variations in water and sand fluxes, and interspecific competition and succession of salt marsh vegetation. These results provide fundamental reference information and could form the scientific basis for formulating policies for the conservation and utilization of salt marsh resources in China.","PeriodicalId":38304,"journal":{"name":"Yaogan Xuebao/Journal of Remote Sensing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42055281","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}
Hengmao Wang, Fei Jiang, Yi Liu, Dongxu Yang, Mousong Wu, W. He, Jun Wang, Jing Wang, W. Ju, Jing M. Chen
TanSat is China’s first greenhouse gases observing satellite. In recent years, substantial progresses have been achieved on retrieving column-averaged CO2 dry air mole fraction (XCO2). However, relatively few attempts have been made to estimate terrestrial net ecosystem exchange (NEE) using TanSat XCO2 retrievals. In this study, based on the GEOS-Chem 4D-Var data assimilation system, we infer the global NEE from April 2017 to March 2018 using TanSat XCO2. The inversion estimates global NEE at −3.46 PgC yr-1, evidently higher than prior estimate and giving rise to an improved estimate of global atmospheric CO2 growth rate. Regionally, our inversion greatly increases the carbon uptakes in northern mid-to-high latitudes and significantly enhances the carbon releases in tropical and southern lands, especially in Africa and India peninsula. The increase of posterior sinks in northern lands is mainly attributed to the decreased carbon release during the nongrowing season, and the decrease of carbon uptakes in tropical and southern lands basically occurs throughout the year. Evaluations against independent CO2 observations and comparison with previous estimates indicate that although the land sinks in the northern middle latitudes and southern temperate regions are improved to a certain extent, they are obviously overestimated in northern high latitudes and underestimated in tropical lands (mainly northern Africa), respectively. These results suggest that TanSat XCO2 retrievals may have systematic negative biases in northern high latitudes and large positive biases over northern Africa, and further efforts are required to remove bias in these regions for better estimates of global and regional NEE.
{"title":"Global Terrestrial Ecosystem Carbon Flux Inferred from TanSat XCO2 Retrievals","authors":"Hengmao Wang, Fei Jiang, Yi Liu, Dongxu Yang, Mousong Wu, W. He, Jun Wang, Jing Wang, W. Ju, Jing M. Chen","doi":"10.34133/2022/9816536","DOIUrl":"https://doi.org/10.34133/2022/9816536","url":null,"abstract":"TanSat is China’s first greenhouse gases observing satellite. In recent years, substantial progresses have been achieved on retrieving column-averaged CO2 dry air mole fraction (XCO2). However, relatively few attempts have been made to estimate terrestrial net ecosystem exchange (NEE) using TanSat XCO2 retrievals. In this study, based on the GEOS-Chem 4D-Var data assimilation system, we infer the global NEE from April 2017 to March 2018 using TanSat XCO2. The inversion estimates global NEE at −3.46 PgC yr-1, evidently higher than prior estimate and giving rise to an improved estimate of global atmospheric CO2 growth rate. Regionally, our inversion greatly increases the carbon uptakes in northern mid-to-high latitudes and significantly enhances the carbon releases in tropical and southern lands, especially in Africa and India peninsula. The increase of posterior sinks in northern lands is mainly attributed to the decreased carbon release during the nongrowing season, and the decrease of carbon uptakes in tropical and southern lands basically occurs throughout the year. Evaluations against independent CO2 observations and comparison with previous estimates indicate that although the land sinks in the northern middle latitudes and southern temperate regions are improved to a certain extent, they are obviously overestimated in northern high latitudes and underestimated in tropical lands (mainly northern Africa), respectively. These results suggest that TanSat XCO2 retrievals may have systematic negative biases in northern high latitudes and large positive biases over northern Africa, and further efforts are required to remove bias in these regions for better estimates of global and regional NEE.","PeriodicalId":38304,"journal":{"name":"Yaogan Xuebao/Journal of Remote Sensing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45240302","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}
Canopy cover is an important parameter affecting forest succession, carbon fluxes, and wildlife habitats. Several global maps with different spatial resolutions have been produced based on satellite images, but facing the deficiency of reliable references for accuracy assessments. The rapid development of unmanned aerial vehicle (UAV) equipped with consumer-grade camera enables the acquisition of high-resolution images at low cost, which provides the research community a promising tool to collect reference data. However, it is still a challenge to distinguish tree crowns and understory green vegetation based on the UAV-based true color images (RGB) due to the limited spectral information. In addition, the canopy height model (CHM) derived from photogrammetric point clouds has also been used to identify tree crowns but limited by the unavailability of understory terrain elevations. This study proposed a simple method to distinguish tree crowns and understories based on UAV visible images, which was referred to as BAMOS for convenience. The central idea of the BAMOS was the synergy of spectral information from digital orthophoto map (DOM) and structural information from digital surface model (DSM). Samples of canopy covers were produced by applying the BAMOS method on the UAV images collected at 77 sites with a size of about 1.0 km2 across Daxing’anling forested area in northeast of China. Results showed that canopy cover extracted by the BAMOS method was highly correlated to visually interpreted ones with correlation coefficient (r) of 0.96 and root mean square error (RMSE) of 5.7%. Then, the UAV-based canopy covers served as references for assessment of satellite-based maps, including MOD44B Version 6 Vegetation Continuous Fields (MODIS VCF), maps developed by the Global Land Cover Facility (GLCF) and by the Global Land Analysis and Discovery laboratory (GLAD). Results showed that both GLAD and GLCF canopy covers could capture the dominant spatial patterns, but GLAD canopy cover tended to miss scattered trees in highly heterogeneous areas, and GLCF failed to capture non-tree areas. Most important of all, obvious underestimations with RMSE about 20% were easily observed in all satellite-based maps, although the temporal inconsistency with references might have some contributions.
{"title":"Regional Sampling of Forest Canopy Covers Using UAV Visible Stereoscopic Imagery for Assessment of Satellite-Based Products in Northeast China","authors":"Tianyu Yu, W. Ni, Zhiyu Zhang, Qinhuo Liu, G. Sun","doi":"10.34133/2022/9806802","DOIUrl":"https://doi.org/10.34133/2022/9806802","url":null,"abstract":"Canopy cover is an important parameter affecting forest succession, carbon fluxes, and wildlife habitats. Several global maps with different spatial resolutions have been produced based on satellite images, but facing the deficiency of reliable references for accuracy assessments. The rapid development of unmanned aerial vehicle (UAV) equipped with consumer-grade camera enables the acquisition of high-resolution images at low cost, which provides the research community a promising tool to collect reference data. However, it is still a challenge to distinguish tree crowns and understory green vegetation based on the UAV-based true color images (RGB) due to the limited spectral information. In addition, the canopy height model (CHM) derived from photogrammetric point clouds has also been used to identify tree crowns but limited by the unavailability of understory terrain elevations. This study proposed a simple method to distinguish tree crowns and understories based on UAV visible images, which was referred to as BAMOS for convenience. The central idea of the BAMOS was the synergy of spectral information from digital orthophoto map (DOM) and structural information from digital surface model (DSM). Samples of canopy covers were produced by applying the BAMOS method on the UAV images collected at 77 sites with a size of about 1.0 km2 across Daxing’anling forested area in northeast of China. Results showed that canopy cover extracted by the BAMOS method was highly correlated to visually interpreted ones with correlation coefficient (r) of 0.96 and root mean square error (RMSE) of 5.7%. Then, the UAV-based canopy covers served as references for assessment of satellite-based maps, including MOD44B Version 6 Vegetation Continuous Fields (MODIS VCF), maps developed by the Global Land Cover Facility (GLCF) and by the Global Land Analysis and Discovery laboratory (GLAD). Results showed that both GLAD and GLCF canopy covers could capture the dominant spatial patterns, but GLAD canopy cover tended to miss scattered trees in highly heterogeneous areas, and GLCF failed to capture non-tree areas. Most important of all, obvious underestimations with RMSE about 20% were easily observed in all satellite-based maps, although the temporal inconsistency with references might have some contributions.","PeriodicalId":38304,"journal":{"name":"Yaogan Xuebao/Journal of Remote Sensing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42701176","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}
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