Pub Date : 2022-01-01DOI: 10.34133/remotesensing.0004
Ming Li, Jingzhi Li, Yuting Liu, Fan Liu
The purpose of pansharpening is to generate high-resolution multispectral (MS) images using both low-resolution MS images and high-resolution panchromatic images. Traditional remote sensing image fusion algorithms can be simplified to a unified detail injection (Di) context that treats the injected MS details as panchromatic-detail and integration with injection gain. The injected details are developed from traditional fusion strategies with clear physical interpretation and facilitate fast convergence of deep learning models for high-quality image fusion. The excellent ability of convolutional autoencoder (CAE) networks to retain image information enables its application to remote sensing image fusion. In this paper, a fusion method Di-based CAE (DiCAE) based on Di and CAE is proposed. DiCAE method is based on Di as the theoretical foundation and CAE network as the core of the algorithm. In addition, our method is evaluated through experiments on different satellite datasets, and the fusion results obtained by DiCAE have better objective evaluation metrics and better visual results compared to other state-of-the-art methods.
{"title":"Detail Injection-Based Convolutional Auto-Encoder for Pansharpening","authors":"Ming Li, Jingzhi Li, Yuting Liu, Fan Liu","doi":"10.34133/remotesensing.0004","DOIUrl":"https://doi.org/10.34133/remotesensing.0004","url":null,"abstract":"The purpose of pansharpening is to generate high-resolution multispectral (MS) images using both low-resolution MS images and high-resolution panchromatic images. Traditional remote sensing image fusion algorithms can be simplified to a unified detail injection (Di) context that treats the injected MS details as panchromatic-detail and integration with injection gain. The injected details are developed from traditional fusion strategies with clear physical interpretation and facilitate fast convergence of deep learning models for high-quality image fusion. The excellent ability of convolutional autoencoder (CAE) networks to retain image information enables its application to remote sensing image fusion. In this paper, a fusion method Di-based CAE (DiCAE) based on Di and CAE is proposed. DiCAE method is based on Di as the theoretical foundation and CAE network as the core of the algorithm. In addition, our method is evaluated through experiments on different satellite datasets, and the fusion results obtained by DiCAE have better objective evaluation metrics and better visual results compared to other state-of-the-art methods.","PeriodicalId":38304,"journal":{"name":"Yaogan Xuebao/Journal of Remote Sensing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42421935","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}
Pub Date : 2022-01-01DOI: 10.34133/remotesensing.0006
Xin Jiang, A. Ziegler, Shijing Liang, Dashan Wang, Zhenzhong Zeng
Reforestation is an eco-friendly strategy for countering rising carbon dioxide concentrations in the atmosphere and the negative effects of forest loss and degradation. China, with one of the world’s most considerable afforestation rates, has increased its forest cover from 16.6% 20 years ago to 23.0% by 2020. However, the maximum potential forest coverage achieved via tree planting and restoration is uncertain. To map potential tree coverage across China, we developed a random forest regression model relating environmental factors and appropriate forest types. We estimate 67.2 million hectares of land currently available for tree restoration after excluding existing forested areas, urban areas, and agriculture land covers/uses, which is 50% higher than the current understanding. Converting these lands to the forest would generate 3.99 gigatons of new above- and belowground carbon stocks, representing an important contribution to achieving carbon neutrality. This potential is spatially imbalanced, with the largest restorable carbon potential being located in the southwest (29.5%), followed by the northeast (17.2%) and northwest (16.8%). Our study highlights the need to align tree restoration areas with the uneven distribution of carbon sequestration potential. In addition to being a biological mitigation strategy to partially offset carbon dioxide emissions from fossil fuel burning, reforestation should provide other environmental services such as the restoration of degraded soils, conservation of biological diversity, revitalization of hydrological integrity, localized cooling, and improvement in air quality. Because of the collective benefits of forest restoration, we encourage that such activities be ecosystem focused as opposed to solely focusing on tree planting.
{"title":"Forest Restoration Potential in China: Implications for Carbon Capture","authors":"Xin Jiang, A. Ziegler, Shijing Liang, Dashan Wang, Zhenzhong Zeng","doi":"10.34133/remotesensing.0006","DOIUrl":"https://doi.org/10.34133/remotesensing.0006","url":null,"abstract":"Reforestation is an eco-friendly strategy for countering rising carbon dioxide concentrations in the atmosphere and the negative effects of forest loss and degradation. China, with one of the world’s most considerable afforestation rates, has increased its forest cover from 16.6% 20 years ago to 23.0% by 2020. However, the maximum potential forest coverage achieved via tree planting and restoration is uncertain. To map potential tree coverage across China, we developed a random forest regression model relating environmental factors and appropriate forest types. We estimate 67.2 million hectares of land currently available for tree restoration after excluding existing forested areas, urban areas, and agriculture land covers/uses, which is 50% higher than the current understanding. Converting these lands to the forest would generate 3.99 gigatons of new above- and belowground carbon stocks, representing an important contribution to achieving carbon neutrality. This potential is spatially imbalanced, with the largest restorable carbon potential being located in the southwest (29.5%), followed by the northeast (17.2%) and northwest (16.8%). Our study highlights the need to align tree restoration areas with the uneven distribution of carbon sequestration potential. In addition to being a biological mitigation strategy to partially offset carbon dioxide emissions from fossil fuel burning, reforestation should provide other environmental services such as the restoration of degraded soils, conservation of biological diversity, revitalization of hydrological integrity, localized cooling, and improvement in air quality. Because of the collective benefits of forest restoration, we encourage that such activities be ecosystem focused as opposed to solely focusing on tree planting.","PeriodicalId":38304,"journal":{"name":"Yaogan Xuebao/Journal of Remote Sensing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46629329","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}
Pub Date : 2022-01-01DOI: 10.34133/remotesensing.0001
Xiaoqiang Liu, Q. Ma, Xiaoyong Wu, T. Hu, G. Dai, Jin Wu, S. Tao, Shaopeng Wang, Lingli Liu, Q. Guo, Yanjun Su
Canopy structural complexity is a critical emergent forest attribute, and light detection and ranging (lidar)-based fractal dimension has been recognized as its powerful measure at the individual tree level. However, the current lidar-based estimation method is highly sensitive to data characteristics, and its scalability from individual trees to forest stands remains unclear. This study proposed an improved method to estimate fractal dimension from lidar data by considering Shannon entropy, and evaluated its scalability from individual trees to forest stands through mathematical derivations. Moreover, a total of 280 forest stand scenes simulated from the terrestrial lidar data of 115 trees spanning large variability in canopy structural complexity were used to evaluate the robustness of the proposed method and the scalability of fractal dimension. The results show that the proposed method can significantly improve the robustness of lidar-derived fractal dimensions. Both mathematical derivations and experimental analyses demonstrate that the fractal dimension of a forest stand is equal to that of the tree with the largest fractal dimension in it, manifesting its nonscalability from individual trees to forest stands. The nonscalability of fractal dimension reveals its limited capability in canopy structural complexity quantification and indicates that the power-law scaling theory of a forest stand underlying fractal geometry is determined by its dominant tree instead of the entire community. Nevertheless, we believe that fractal dimension is still a useful indicator of canopy structural complexity at the individual tree level and might be used along with other stand-level indexes to reflect the “tree-to-stand” correlation of canopy structural complexity.
{"title":"Nonscalability of Fractal Dimension to Quantify Canopy Structural Complexity from Individual Trees to Forest Stands","authors":"Xiaoqiang Liu, Q. Ma, Xiaoyong Wu, T. Hu, G. Dai, Jin Wu, S. Tao, Shaopeng Wang, Lingli Liu, Q. Guo, Yanjun Su","doi":"10.34133/remotesensing.0001","DOIUrl":"https://doi.org/10.34133/remotesensing.0001","url":null,"abstract":"Canopy structural complexity is a critical emergent forest attribute, and light detection and ranging (lidar)-based fractal dimension has been recognized as its powerful measure at the individual tree level. However, the current lidar-based estimation method is highly sensitive to data characteristics, and its scalability from individual trees to forest stands remains unclear. This study proposed an improved method to estimate fractal dimension from lidar data by considering Shannon entropy, and evaluated its scalability from individual trees to forest stands through mathematical derivations. Moreover, a total of 280 forest stand scenes simulated from the terrestrial lidar data of 115 trees spanning large variability in canopy structural complexity were used to evaluate the robustness of the proposed method and the scalability of fractal dimension. The results show that the proposed method can significantly improve the robustness of lidar-derived fractal dimensions. Both mathematical derivations and experimental analyses demonstrate that the fractal dimension of a forest stand is equal to that of the tree with the largest fractal dimension in it, manifesting its nonscalability from individual trees to forest stands. The nonscalability of fractal dimension reveals its limited capability in canopy structural complexity quantification and indicates that the power-law scaling theory of a forest stand underlying fractal geometry is determined by its dominant tree instead of the entire community. Nevertheless, we believe that fractal dimension is still a useful indicator of canopy structural complexity at the individual tree level and might be used along with other stand-level indexes to reflect the “tree-to-stand” correlation of canopy structural complexity.","PeriodicalId":38304,"journal":{"name":"Yaogan Xuebao/Journal of Remote Sensing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49244659","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}
Light scattering by pure water and seawater is a fundamental optical property that plays a critical role in ocean optics and ocean color studies. We briefly review the theory of molecular scattering in liquid and electrolyte solutions and focus on the recent developments in modeling the effect of pressure, extending to extreme environments, and evaluating the effect of salinity on the depolarization ratio. We demonstrate how the modeling of seawater scattering can be applied to better understand spectral absorption and attenuation of pure water and seawater. We recommend future efforts should be directed at measuring the polarized components of scattering by pure water over a greater range of wavelengths, temperature, salinity, and pressure to constrain and validate the model and to improve our knowledge of the water’s depolarization ratio.
{"title":"Light Scattering by Pure Water and Seawater: Recent Development","authors":"Xiaodong Zhang, Lianbo Hu","doi":"10.34133/2021/9753625","DOIUrl":"https://doi.org/10.34133/2021/9753625","url":null,"abstract":"Light scattering by pure water and seawater is a fundamental optical property that plays a critical role in ocean optics and ocean color studies. We briefly review the theory of molecular scattering in liquid and electrolyte solutions and focus on the recent developments in modeling the effect of pressure, extending to extreme environments, and evaluating the effect of salinity on the depolarization ratio. We demonstrate how the modeling of seawater scattering can be applied to better understand spectral absorption and attenuation of pure water and seawater. We recommend future efforts should be directed at measuring the polarized components of scattering by pure water over a greater range of wavelengths, temperature, salinity, and pressure to constrain and validate the model and to improve our knowledge of the water’s depolarization ratio.","PeriodicalId":38304,"journal":{"name":"Yaogan Xuebao/Journal of Remote Sensing","volume":"649 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41281537","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}
Leaf area index (LAI) is an essential climate variable that is crucial to understand the global vegetation change. Long-term satellite LAI products have been applied in many global vegetation change studies. However, these LAI products contain various uncertainties that are not been fully considered in current studies. The objective of this study is to explore the uncertainties in the global LAI products and the uncertainty variations. Two global LAI datasets—the European Geoland2 Version 2 (GEOV2) and Moderate Resolution Imaging Spectroradiometer (MODIS) (2003-2019)—were investigated. The qualitative quality flags (QQFs) and quantitative quality indicators (QQIs) embedded in the product quality layers were analyzed to identify the temporal anomalies in the quality profile. The results show that the global GEOV2 (0.042/10a) and MODIS (0.034/10a) LAI values have steadly increased from 2003 to 2019. The global LAI uncertainty (0.016/10a) and relative uncertainty (0.3%/10a) from GEOV2 have also increased gradually, especially during the growing season from April to October. The uncertainty increase is larger for woody biomes than for herbaceous types. Contrastingly, the MODIS LAI product uncertainty remained stable over the study period. The uncertainty increase indicated by GEOV2 is partly attributed to the sensor shift in the product series. Further algorithm enhancement is necessary to improve the cross-sensor performance. This study highlights the importance of studying the LAI uncertainty and the uncertainty variation. Temporal variations in the LAI products and the product quality revealed herein have significant implications on global vegetation change studies.
{"title":"Long-Term Variation of Global GEOV2 and MODIS Leaf Area Index (LAI) and Their Uncertainties: An Insight into the Product Stabilities","authors":"H. Fang, Yao Wang, Yinghui Zhang, Sijia Li","doi":"10.34133/2021/9842830","DOIUrl":"https://doi.org/10.34133/2021/9842830","url":null,"abstract":"Leaf area index (LAI) is an essential climate variable that is crucial to understand the global vegetation change. Long-term satellite LAI products have been applied in many global vegetation change studies. However, these LAI products contain various uncertainties that are not been fully considered in current studies. The objective of this study is to explore the uncertainties in the global LAI products and the uncertainty variations. Two global LAI datasets—the European Geoland2 Version 2 (GEOV2) and Moderate Resolution Imaging Spectroradiometer (MODIS) (2003-2019)—were investigated. The qualitative quality flags (QQFs) and quantitative quality indicators (QQIs) embedded in the product quality layers were analyzed to identify the temporal anomalies in the quality profile. The results show that the global GEOV2 (0.042/10a) and MODIS (0.034/10a) LAI values have steadly increased from 2003 to 2019. The global LAI uncertainty (0.016/10a) and relative uncertainty (0.3%/10a) from GEOV2 have also increased gradually, especially during the growing season from April to October. The uncertainty increase is larger for woody biomes than for herbaceous types. Contrastingly, the MODIS LAI product uncertainty remained stable over the study period. The uncertainty increase indicated by GEOV2 is partly attributed to the sensor shift in the product series. Further algorithm enhancement is necessary to improve the cross-sensor performance. This study highlights the importance of studying the LAI uncertainty and the uncertainty variation. Temporal variations in the LAI products and the product quality revealed herein have significant implications on global vegetation change studies.","PeriodicalId":38304,"journal":{"name":"Yaogan Xuebao/Journal of Remote Sensing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42254384","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}
Timely and accurate information on tree species (TS) is crucial for developing strategies for sustainable management and conservation of artificial and natural forests. Over the last four decades, advances in remote sensing technologies have made TS classification possible. Since many studies on the topic have been conducted and their comprehensive results and novel findings have been published in the literature, it is necessary to conduct an updated review on the status, trends, potentials, and challenges and to recommend future directions. The review will provide an overview on various optical and light detection and ranging (LiDAR) sensors; present and assess current various techniques/methods for, and a general trend of method development in, TS classification; and identify limitations and recommend future directions. In this review, several concluding remarks were made. They include the following: (1) A large group of studies on the topic were using high-resolution satellite, airborne multi-/hyperspectral imagery, and airborne LiDAR data. (2) A trend of “multiple” method development for the topic was observed. (3) Machine learning methods including deep learning models were demonstrated to be significant in improving TS classification accuracy. (4) Recently, unmanned aerial vehicle- (UAV-) based sensors have caught the interest of researchers and practitioners for the topic-related research and applications. In addition, three future directions were recommended, including refining the three categories of “multiple” methods, developing novel data fusion algorithms or processing chains, and exploring new spectral unmixing algorithms to automatically extract and map TS spectral information from satellite hyperspectral data.
{"title":"Mapping Tree Species Using Advanced Remote Sensing Technologies: A State-of-the-Art Review and Perspective","authors":"R. Pu","doi":"10.34133/2021/9812624","DOIUrl":"https://doi.org/10.34133/2021/9812624","url":null,"abstract":"Timely and accurate information on tree species (TS) is crucial for developing strategies for sustainable management and conservation of artificial and natural forests. Over the last four decades, advances in remote sensing technologies have made TS classification possible. Since many studies on the topic have been conducted and their comprehensive results and novel findings have been published in the literature, it is necessary to conduct an updated review on the status, trends, potentials, and challenges and to recommend future directions. The review will provide an overview on various optical and light detection and ranging (LiDAR) sensors; present and assess current various techniques/methods for, and a general trend of method development in, TS classification; and identify limitations and recommend future directions. In this review, several concluding remarks were made. They include the following: (1) A large group of studies on the topic were using high-resolution satellite, airborne multi-/hyperspectral imagery, and airborne LiDAR data. (2) A trend of “multiple” method development for the topic was observed. (3) Machine learning methods including deep learning models were demonstrated to be significant in improving TS classification accuracy. (4) Recently, unmanned aerial vehicle- (UAV-) based sensors have caught the interest of researchers and practitioners for the topic-related research and applications. In addition, three future directions were recommended, including refining the three categories of “multiple” methods, developing novel data fusion algorithms or processing chains, and exploring new spectral unmixing algorithms to automatically extract and map TS spectral information from satellite hyperspectral data.","PeriodicalId":38304,"journal":{"name":"Yaogan Xuebao/Journal of Remote Sensing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42572508","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}
Xiao Zhang, Liangyun Liu, Xidong Chen, Yuan Gao, M. Jiang
Accurately monitoring the spatiotemporal dynamics of impervious surfaces is very important for understanding the process of urbanization. However, the complicated makeup and spectral heterogeneity of impervious surfaces create difficulties for impervious surface monitoring. In this study, we propose an automatic method to capture the spatiotemporal expansion of impervious surfaces using spectral generalization and time series Landsat imagery. First, the multitemporal compositing and relative radiometric normalization methods were used to extract phenological information and ensure spectral consistency between reference imagery and monitored imagery. Second, we automatically derived training samples from the prior MSMT_IS30-2020 impervious surface products and migrated the surface reflectance of impervious surfaces in the reference period of 2020 to other periods (1985–2015). Third, the random forest classification method, trained using the migrated surface reflectance of impervious surfaces and pervious surface training samples at each period, was employed to extract temporally independent impervious surfaces. Further, a temporal consistency check method was applied to ensure the consistency and reliability of the monitoring results. According to qualitative and quantitative validation results, the method achieved an overall accuracy of 90.9% and kappa coefficient of 0.859 in identifying the spatiotemporal expansion of impervious surfaces and performed better in capturing the impervious surface dynamics when compared with other impervious surface datasets. Lastly, our results indicate that a rapid increase of impervious surfaces was observed in the Yangtze River Delta, and the area of impervious surfaces in 2000 and 2020 was 1.86 times and 4.76 times that of 1985, respectively. Therefore, it could be concluded that the proposed method offered a novel perspective for providing timely and accurate impervious surface dynamics.
{"title":"Automatically Monitoring Impervious Surfaces Using Spectral Generalization and Time Series Landsat Imagery from 1985 to 2020 in the Yangtze River Delta","authors":"Xiao Zhang, Liangyun Liu, Xidong Chen, Yuan Gao, M. Jiang","doi":"10.34133/2021/9873816","DOIUrl":"https://doi.org/10.34133/2021/9873816","url":null,"abstract":"Accurately monitoring the spatiotemporal dynamics of impervious surfaces is very important for understanding the process of urbanization. However, the complicated makeup and spectral heterogeneity of impervious surfaces create difficulties for impervious surface monitoring. In this study, we propose an automatic method to capture the spatiotemporal expansion of impervious surfaces using spectral generalization and time series Landsat imagery. First, the multitemporal compositing and relative radiometric normalization methods were used to extract phenological information and ensure spectral consistency between reference imagery and monitored imagery. Second, we automatically derived training samples from the prior MSMT_IS30-2020 impervious surface products and migrated the surface reflectance of impervious surfaces in the reference period of 2020 to other periods (1985–2015). Third, the random forest classification method, trained using the migrated surface reflectance of impervious surfaces and pervious surface training samples at each period, was employed to extract temporally independent impervious surfaces. Further, a temporal consistency check method was applied to ensure the consistency and reliability of the monitoring results. According to qualitative and quantitative validation results, the method achieved an overall accuracy of 90.9% and kappa coefficient of 0.859 in identifying the spatiotemporal expansion of impervious surfaces and performed better in capturing the impervious surface dynamics when compared with other impervious surface datasets. Lastly, our results indicate that a rapid increase of impervious surfaces was observed in the Yangtze River Delta, and the area of impervious surfaces in 2000 and 2020 was 1.86 times and 4.76 times that of 1985, respectively. Therefore, it could be concluded that the proposed method offered a novel perspective for providing timely and accurate impervious surface dynamics.","PeriodicalId":38304,"journal":{"name":"Yaogan Xuebao/Journal of Remote Sensing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43867631","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}
Pub Date : 2021-09-28DOI: 10.21203/rs.3.rs-910503/v1
Shuangna Jin, Wuming Zhang, Jie Shao, P. Wan, Shun Cheng, Shangshu Cai, G. Yan
BackgroundTree growth is an important indicator of forest health and can reflect changes in forest structure. Traditional tree growth estimates use easy-to-measure parameters (e.g., tree height, diameter at breast height (DBH), and crown diameter) obtained via forest in situ measurements, which are labor-intensive and time-consuming to perform and cannot easily describe the changes throughout the whole growth period of a tree. The combination of Terrestrial Laser Scanning (TLS) and Quantitative Structure Modelling (QSM) can accurately estimate tree structural parameters nondestructively and has the potential to estimate tree growth. Therefore, this paper estimates tree growth according to the stem-, crown-, and branch-level attributes observed by ground-based LiDAR point clouds. Compared with conventional methods, this paper used tree height, DBH, stem volume, crown diameter, crown volume and first-order branch volume to estimate the growth of 55-year-old larch trees in Saihanba at the stem, crown and branch levels, respectively. ResultsThe experimental results showed that the absolute growth of the first-order branch volume was equivalent to that of the stems, which highlights the importance of branches in the study of tree growth. For 55-year-old larch, tree growth is mainly reflected in the growth of the crown, i.e., the growth of branches. Compared to one-dimensional parameters (tree height, DBH and crown diameter), the growth of three-dimensional parameters (crown, stem and first-order branch volumes) was more obvious. ConclusionsFor 55-year-old larch, three-dimensional tree parameters can more effectively describe tree growth, and the absolute growth of the first-order branch volume is close to the stem volume. In addition, it is necessary to estimate tree growth at different levels.
{"title":"Estimation of Larch Growth at the Stem, Crown and Branch Levels Using Ground-based LiDAR Point Cloud","authors":"Shuangna Jin, Wuming Zhang, Jie Shao, P. Wan, Shun Cheng, Shangshu Cai, G. Yan","doi":"10.21203/rs.3.rs-910503/v1","DOIUrl":"https://doi.org/10.21203/rs.3.rs-910503/v1","url":null,"abstract":"\u0000 BackgroundTree growth is an important indicator of forest health and can reflect changes in forest structure. Traditional tree growth estimates use easy-to-measure parameters (e.g., tree height, diameter at breast height (DBH), and crown diameter) obtained via forest in situ measurements, which are labor-intensive and time-consuming to perform and cannot easily describe the changes throughout the whole growth period of a tree. The combination of Terrestrial Laser Scanning (TLS) and Quantitative Structure Modelling (QSM) can accurately estimate tree structural parameters nondestructively and has the potential to estimate tree growth. Therefore, this paper estimates tree growth according to the stem-, crown-, and branch-level attributes observed by ground-based LiDAR point clouds. Compared with conventional methods, this paper used tree height, DBH, stem volume, crown diameter, crown volume and first-order branch volume to estimate the growth of 55-year-old larch trees in Saihanba at the stem, crown and branch levels, respectively. ResultsThe experimental results showed that the absolute growth of the first-order branch volume was equivalent to that of the stems, which highlights the importance of branches in the study of tree growth. For 55-year-old larch, tree growth is mainly reflected in the growth of the crown, i.e., the growth of branches. Compared to one-dimensional parameters (tree height, DBH and crown diameter), the growth of three-dimensional parameters (crown, stem and first-order branch volumes) was more obvious. ConclusionsFor 55-year-old larch, three-dimensional tree parameters can more effectively describe tree growth, and the absolute growth of the first-order branch volume is close to the stem volume. In addition, it is necessary to estimate tree growth at different levels.","PeriodicalId":38304,"journal":{"name":"Yaogan Xuebao/Journal of Remote Sensing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48259440","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}
Zhaoying Zhang, Yongguang Zhang, Jing M. Chen, W. Ju, M. Migliavacca, T. El-Madany
Remote sensing of solar-induced chlorophyll fluorescence (SIF) provides new possibilities to estimate terrestrial gross primary production (GPP). To mitigate the angular and canopy structural effects on original SIF observed by sensors (SIFobs), it is recommended to derive total canopy SIF emission (SIFtotal) of leaves within a canopy using canopy interception (i0) and reflectance of vegetation (RV). However, the effects of the uncertainties in i0 and RV on the estimation of SIFtotal have not been well understood. Here, we evaluated such effects on the estimation of GPP using the Soil-Canopy-Observation of Photosynthesis and the Energy balance (SCOPE) model. The SCOPE simulations showed that the R2 between GPP and SIFtotal was clearly higher than that between GPP and SIFobs and the differences in R2 (ΔR2) tend to decrease with the increasing levels of uncertainties in i0 and RV. The resultant ΔR2 decreased to zero when the uncertainty level in i0 and RV was ~30% for red band SIF (RSIF, 683 nm) and ~20% for far-red band SIF (FRSIF, 740 nm). In addition, as compared to the TROPOspheric Monitoring Instrument (TROPOMI) SIFobs at both red and far-red bands, SIFtotal derived using any combination of i0 (from MCD15, VNP15, and CGLS LAI products) and RV (from MCD34, MCD19, and VNP43 BRDF products) showed comparable improvements in estimating GPP. With this study, we suggest a way to advance our understanding in the estimation of a more physiological relevant SIF datasets (SIFtotal) using current satellite products.
{"title":"Sensitivity of Estimated Total Canopy SIF Emission to Remotely Sensed LAI and BRDF Products","authors":"Zhaoying Zhang, Yongguang Zhang, Jing M. Chen, W. Ju, M. Migliavacca, T. El-Madany","doi":"10.34133/2021/9795837","DOIUrl":"https://doi.org/10.34133/2021/9795837","url":null,"abstract":"Remote sensing of solar-induced chlorophyll fluorescence (SIF) provides new possibilities to estimate terrestrial gross primary production (GPP). To mitigate the angular and canopy structural effects on original SIF observed by sensors (SIFobs), it is recommended to derive total canopy SIF emission (SIFtotal) of leaves within a canopy using canopy interception (i0) and reflectance of vegetation (RV). However, the effects of the uncertainties in i0 and RV on the estimation of SIFtotal have not been well understood. Here, we evaluated such effects on the estimation of GPP using the Soil-Canopy-Observation of Photosynthesis and the Energy balance (SCOPE) model. The SCOPE simulations showed that the R2 between GPP and SIFtotal was clearly higher than that between GPP and SIFobs and the differences in R2 (ΔR2) tend to decrease with the increasing levels of uncertainties in i0 and RV. The resultant ΔR2 decreased to zero when the uncertainty level in i0 and RV was ~30% for red band SIF (RSIF, 683 nm) and ~20% for far-red band SIF (FRSIF, 740 nm). In addition, as compared to the TROPOspheric Monitoring Instrument (TROPOMI) SIFobs at both red and far-red bands, SIFtotal derived using any combination of i0 (from MCD15, VNP15, and CGLS LAI products) and RV (from MCD34, MCD19, and VNP43 BRDF products) showed comparable improvements in estimating GPP. With this study, we suggest a way to advance our understanding in the estimation of a more physiological relevant SIF datasets (SIFtotal) using current satellite products.","PeriodicalId":38304,"journal":{"name":"Yaogan Xuebao/Journal of Remote Sensing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44516944","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}
Despite the promising performance on benchmark datasets that deep convolutional neural networks have exhibited in single image super-resolution (SISR), there are two underlying limitations to existing methods. First, current supervised learning-based SISR methods for remote sensing satellite imagery do not use paired real sensor data, instead operating on simulated high-resolution (HR) and low-resolution (LR) image-pairs (typically HR images with their bicubic-degraded LR counterparts), which often yield poor performance on real-world LR images. Second, SISR is an ill-posed problem, and the super-resolved image from discriminatively trained networks with lp norm loss is an average of the infinite possible HR images, thus, always has low perceptual quality. Though this issue can be mitigated by generative adversarial network (GAN), it is still hard to search in the whole solution-space and find the best solution. In this paper, we focus on real-world application and introduce a new multisensor dataset for real-world remote sensing satellite imagery super-resolution. In addition, we propose a novel conditional GAN scheme for SISR task which can further reduce the solution-space. Therefore, the super-resolved images have not only high fidelity, but high perceptual quality as well. Extensive experiments demonstrate that networks trained on the introduced dataset can obtain better performances than those trained on simulated data. Additionally, the proposed conditional GAN scheme can achieve better perceptual quality while obtaining comparable fidelity over the state-of-the-art methods.
{"title":"Multisensor Remote Sensing Imagery Super-Resolution with Conditional GAN","authors":"Junwei Wang, Kun Gao, Zhenzhou Zhang, Chong Ni, Zibo Hu, Dayu Chen, Qiong Wu","doi":"10.34133/2021/9829706","DOIUrl":"https://doi.org/10.34133/2021/9829706","url":null,"abstract":"Despite the promising performance on benchmark datasets that deep convolutional neural networks have exhibited in single image super-resolution (SISR), there are two underlying limitations to existing methods. First, current supervised learning-based SISR methods for remote sensing satellite imagery do not use paired real sensor data, instead operating on simulated high-resolution (HR) and low-resolution (LR) image-pairs (typically HR images with their bicubic-degraded LR counterparts), which often yield poor performance on real-world LR images. Second, SISR is an ill-posed problem, and the super-resolved image from discriminatively trained networks with lp norm loss is an average of the infinite possible HR images, thus, always has low perceptual quality. Though this issue can be mitigated by generative adversarial network (GAN), it is still hard to search in the whole solution-space and find the best solution. In this paper, we focus on real-world application and introduce a new multisensor dataset for real-world remote sensing satellite imagery super-resolution. In addition, we propose a novel conditional GAN scheme for SISR task which can further reduce the solution-space. Therefore, the super-resolved images have not only high fidelity, but high perceptual quality as well. Extensive experiments demonstrate that networks trained on the introduced dataset can obtain better performances than those trained on simulated data. Additionally, the proposed conditional GAN scheme can achieve better perceptual quality while obtaining comparable fidelity over the state-of-the-art methods.","PeriodicalId":38304,"journal":{"name":"Yaogan Xuebao/Journal of Remote Sensing","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47625876","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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