An increase in the ozone content in the mesosphere over the Moscow region during the solar eclipses of 20 March 2015 and 25 October 2022 was observed by means of a ground-based microwave radiometer operated at frequencies of the ozone spectral line of 142.175 GHz. Changes in ozone mixing ratio (OMR) at altitudes of 90 km and 65 km were estimated and compared with diurnal ozone variations measured on the dates closest to the events. It was found that the observed increase in the OMR at 90 km during the 20 March 2015 eclipse was almost two times greater than during the 25 October 2022 eclipse, although the maximum Sun’s obscurations of these eclipses were close to each other (0.625 and 0.646). Most likely, this difference can be explained by the difference in concentration of atomic hydrogen, which plays an important role in ozone destruction at altitudes of around 90 km and above.
{"title":"Ground-Based Microwave Measurements of Mesospheric Ozone Variations over Moscow Region during the Solar Eclipses of 20 March 2015 and 25 October 2022","authors":"S. Rozanov, A. Ignatyev, A. Zavgorodniy","doi":"10.3390/rs15133440","DOIUrl":"https://doi.org/10.3390/rs15133440","url":null,"abstract":"An increase in the ozone content in the mesosphere over the Moscow region during the solar eclipses of 20 March 2015 and 25 October 2022 was observed by means of a ground-based microwave radiometer operated at frequencies of the ozone spectral line of 142.175 GHz. Changes in ozone mixing ratio (OMR) at altitudes of 90 km and 65 km were estimated and compared with diurnal ozone variations measured on the dates closest to the events. It was found that the observed increase in the OMR at 90 km during the 20 March 2015 eclipse was almost two times greater than during the 25 October 2022 eclipse, although the maximum Sun’s obscurations of these eclipses were close to each other (0.625 and 0.646). Most likely, this difference can be explained by the difference in concentration of atomic hydrogen, which plays an important role in ozone destruction at altitudes of around 90 km and above.","PeriodicalId":20944,"journal":{"name":"Remote. Sens.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79820835","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}
Kun Zeng, S. Zeng, Hai Huang, T. Qiu, Shihui Shen, Hui Wang, Songkai Feng, Cheng Zhang
Remote and real-time displacement measurements are crucial for a successful bridge health monitoring program. Researchers have attempted to monitor the deformation of bridges using remote sensing techniques such as an accelerometer when a static reference frame is not available. However, errors accumulate throughout the double-integration process, significantly reducing the reliability and accuracy of the displacement measurements. To obtain accurate reference-free bridge displacement measurements, this paper aims to develop a real-time computing algorithm based on hybrid sensor data fusion and implement the algorithm via smart sensing technology. By combining the accelerometer and strain gauge measurements in real time, the proposed algorithm can overcome the limitations of the existing methods (such as integration errors, sensor drifts, and environmental disturbances) and provide real-time pseud-static and dynamic displacement measurements of bridges under loads. A wireless sensor, SmartRock, containing multiple sensing units (i.e., triaxial accelerometer and strain gauges) and a Micro Controlling Unit (MCU) were utilized for remote data acquisition and signal processing. A remote sensing system (with SmartRocks, an antenna, an industrial computer, a Wi-Fi hotspot, etc.) was deployed, and a laboratory truss bridge experiment was conducted to demonstrate the implementation of the algorithm. The results show that the proposed algorithm can estimate a bridge displacement with sufficient accuracy, and the remote system is capable of the real-time monitoring of bridge deformations compared to using only one type of sensor. This research represents a significant advancement in the field of bridge displacement monitoring, offering a reliable and reference-free approach for remote and real-time measurements.
{"title":"Sensing Mechanism and Real-Time Bridge Displacement Monitoring for a Laboratory Truss Bridge Using Hybrid Data Fusion","authors":"Kun Zeng, S. Zeng, Hai Huang, T. Qiu, Shihui Shen, Hui Wang, Songkai Feng, Cheng Zhang","doi":"10.3390/rs15133444","DOIUrl":"https://doi.org/10.3390/rs15133444","url":null,"abstract":"Remote and real-time displacement measurements are crucial for a successful bridge health monitoring program. Researchers have attempted to monitor the deformation of bridges using remote sensing techniques such as an accelerometer when a static reference frame is not available. However, errors accumulate throughout the double-integration process, significantly reducing the reliability and accuracy of the displacement measurements. To obtain accurate reference-free bridge displacement measurements, this paper aims to develop a real-time computing algorithm based on hybrid sensor data fusion and implement the algorithm via smart sensing technology. By combining the accelerometer and strain gauge measurements in real time, the proposed algorithm can overcome the limitations of the existing methods (such as integration errors, sensor drifts, and environmental disturbances) and provide real-time pseud-static and dynamic displacement measurements of bridges under loads. A wireless sensor, SmartRock, containing multiple sensing units (i.e., triaxial accelerometer and strain gauges) and a Micro Controlling Unit (MCU) were utilized for remote data acquisition and signal processing. A remote sensing system (with SmartRocks, an antenna, an industrial computer, a Wi-Fi hotspot, etc.) was deployed, and a laboratory truss bridge experiment was conducted to demonstrate the implementation of the algorithm. The results show that the proposed algorithm can estimate a bridge displacement with sufficient accuracy, and the remote system is capable of the real-time monitoring of bridge deformations compared to using only one type of sensor. This research represents a significant advancement in the field of bridge displacement monitoring, offering a reliable and reference-free approach for remote and real-time measurements.","PeriodicalId":20944,"journal":{"name":"Remote. Sens.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79718591","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}
The Turkey–Syria earthquake on 6 February 2023 resulted in losses such as casualties, road damage, and building collapses. We mapped and quantified the areas impacted by the earthquake at different distances and directions using NOAA-20 VIIRS nighttime light (NTL) data. We then explored the relationship between the average changes in the NTL intensity, population density, and building density using the bivariate local indicators of the spatial association (LISA) method. In Turkey, Hatay, Gaziantep, and Sanliurfa experienced the largest NTL losses. Ar Raqqah was the most affected city in Syria, with the highest NTL loss rate. A correlation analysis showed that the number of injured populations in the provinces in Turkey and the number of pixels with a decreased NTL intensity exhibited a linear correlation, with an R-squared value of 0.7395. Based on the changing value of the NTL, the areas with large NTL losses were located 50 km from the earthquake epicentre in the east-by-south and north-by-west directions and 130 km from the earthquake epicentre in the southwest direction. The large NTL increase areas were distributed 130 km from the earthquake epicentre in the north-by-west and north-by-east directions and 180 km from the earthquake epicentre in the northeast direction, indicating a high resilience and effective earthquake rescue. The areas with large NTL losses had large populations and building densities, particularly in the areas approximately 130 km from the earthquake epicentre in the south-by-west direction and within 40 km of the earthquake epicentre in the north-by-west direction, which can be seen from the low–high (L-H) pattern of the LISA results. Our findings provide insights for evaluating natural disasters and can help decision makers to plan post-disaster reconstruction and determine risk levels on a national or regional scale.
{"title":"The Changes in Nighttime Lights Caused by the Turkey-Syria Earthquake Using NOAA-20 VIIRS Day/Night Band Data","authors":"Yuan Yuan, Congxiao Wang, Shaoyang Liu, Zuoqi Chen, Xiaolong Ma, Wei Li, Ling Zhang, Bailang Yu","doi":"10.3390/rs15133438","DOIUrl":"https://doi.org/10.3390/rs15133438","url":null,"abstract":"The Turkey–Syria earthquake on 6 February 2023 resulted in losses such as casualties, road damage, and building collapses. We mapped and quantified the areas impacted by the earthquake at different distances and directions using NOAA-20 VIIRS nighttime light (NTL) data. We then explored the relationship between the average changes in the NTL intensity, population density, and building density using the bivariate local indicators of the spatial association (LISA) method. In Turkey, Hatay, Gaziantep, and Sanliurfa experienced the largest NTL losses. Ar Raqqah was the most affected city in Syria, with the highest NTL loss rate. A correlation analysis showed that the number of injured populations in the provinces in Turkey and the number of pixels with a decreased NTL intensity exhibited a linear correlation, with an R-squared value of 0.7395. Based on the changing value of the NTL, the areas with large NTL losses were located 50 km from the earthquake epicentre in the east-by-south and north-by-west directions and 130 km from the earthquake epicentre in the southwest direction. The large NTL increase areas were distributed 130 km from the earthquake epicentre in the north-by-west and north-by-east directions and 180 km from the earthquake epicentre in the northeast direction, indicating a high resilience and effective earthquake rescue. The areas with large NTL losses had large populations and building densities, particularly in the areas approximately 130 km from the earthquake epicentre in the south-by-west direction and within 40 km of the earthquake epicentre in the north-by-west direction, which can be seen from the low–high (L-H) pattern of the LISA results. Our findings provide insights for evaluating natural disasters and can help decision makers to plan post-disaster reconstruction and determine risk levels on a national or regional scale.","PeriodicalId":20944,"journal":{"name":"Remote. Sens.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81529725","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}
Juhuhn Kim, M. Emmerich, R. Voors, B. Ording, Jong-Seok Lee
Stringent global regulations aim to reduce nitrogen dioxide (NO2) emissions from maritime shipping. However, the lack of a global monitoring system makes compliance verification challenging. To address this issue, we propose a systematic approach to monitor shipping emissions using unsupervised clustering techniques on spatio-temporal georeferenced data, specifically NO2 measurements obtained from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Copernicus Sentinel-5 Precursor satellite. Our method involves partitioning spatio-temporally resolved measurements based on the similarity of NO2 column levels. We demonstrate the reproducibility of our approach through rigorous testing and validation using data collected from multiple regions and time periods. Our approach improves the spatial correlation coefficients between NO2 column clusters and shipping traffic frequency. Additionally, we identify a temporal correlation between NO2 column levels along shipping routes and the global container throughput index. We expect that our approach may serve as a prototype for a tool to identify anthropogenic maritime emissions, distinguishing them from background sources.
{"title":"A Systematic Approach to Identify Shipping Emissions Using Spatio-Temporally Resolved TROPOMI Data","authors":"Juhuhn Kim, M. Emmerich, R. Voors, B. Ording, Jong-Seok Lee","doi":"10.3390/rs15133453","DOIUrl":"https://doi.org/10.3390/rs15133453","url":null,"abstract":"Stringent global regulations aim to reduce nitrogen dioxide (NO2) emissions from maritime shipping. However, the lack of a global monitoring system makes compliance verification challenging. To address this issue, we propose a systematic approach to monitor shipping emissions using unsupervised clustering techniques on spatio-temporal georeferenced data, specifically NO2 measurements obtained from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Copernicus Sentinel-5 Precursor satellite. Our method involves partitioning spatio-temporally resolved measurements based on the similarity of NO2 column levels. We demonstrate the reproducibility of our approach through rigorous testing and validation using data collected from multiple regions and time periods. Our approach improves the spatial correlation coefficients between NO2 column clusters and shipping traffic frequency. Additionally, we identify a temporal correlation between NO2 column levels along shipping routes and the global container throughput index. We expect that our approach may serve as a prototype for a tool to identify anthropogenic maritime emissions, distinguishing them from background sources.","PeriodicalId":20944,"journal":{"name":"Remote. Sens.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90970573","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}
Satellite synthetic aperture radar (SAR) images are recognized as one of the most efficient tools for day/night, all weather and large area monitoring of ships at sea. However, false alarms discrimination is still one key problem on SAR ship detection. While many discrimination techniques have been proposed for the treatment of false alarms, not enough emphasis has been targeted to explore how obtained false alarms are related to the changing ocean environmental conditions. To this end, we combined a large set of Sentinel-1 SAR images with ocean surface wind and wave data into one dataset. SAR images were separated into three distinct groups according to wave age (WA) conditions present during image acquisition: young wind sea, old wind sea, and swell. A constant false alarm rate (CFAR) ship detection algorithm was implemented based on the generalized gamma distribution (GΓD). Kolmogorov–Smirnov distance was used to analyze the distribution goodness-of-fit among distinct ocean environments. A backscattering analysis of different sizes of ship targets and sea clutter was further performed using the OpenSARShip and automatic identification system (AIS) datasets to assess its separability. We derived a discrimination threshold adjustment based on WA conditions and showed its efficacy to drastically reduce false alarms. To our present knowledge, the use of WA as part of the CFAR and for the adjustment of the threshold of detection is a novelty which could be tested and evaluated for different SAR sensors.
{"title":"Marine Environmental Impact on CFAR Ship Detection as Measured by Wave Age in SAR Images","authors":"D. X. Bezerra, J. Lorenzzetti, R. L. Paes","doi":"10.3390/rs15133441","DOIUrl":"https://doi.org/10.3390/rs15133441","url":null,"abstract":"Satellite synthetic aperture radar (SAR) images are recognized as one of the most efficient tools for day/night, all weather and large area monitoring of ships at sea. However, false alarms discrimination is still one key problem on SAR ship detection. While many discrimination techniques have been proposed for the treatment of false alarms, not enough emphasis has been targeted to explore how obtained false alarms are related to the changing ocean environmental conditions. To this end, we combined a large set of Sentinel-1 SAR images with ocean surface wind and wave data into one dataset. SAR images were separated into three distinct groups according to wave age (WA) conditions present during image acquisition: young wind sea, old wind sea, and swell. A constant false alarm rate (CFAR) ship detection algorithm was implemented based on the generalized gamma distribution (GΓD). Kolmogorov–Smirnov distance was used to analyze the distribution goodness-of-fit among distinct ocean environments. A backscattering analysis of different sizes of ship targets and sea clutter was further performed using the OpenSARShip and automatic identification system (AIS) datasets to assess its separability. We derived a discrimination threshold adjustment based on WA conditions and showed its efficacy to drastically reduce false alarms. To our present knowledge, the use of WA as part of the CFAR and for the adjustment of the threshold of detection is a novelty which could be tested and evaluated for different SAR sensors.","PeriodicalId":20944,"journal":{"name":"Remote. Sens.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89300485","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}
In soybean, lodging is sometimes caused by strong winds and rains, resulting in a decrease in yield and quality. Technical measures against lodging include “pinching”, in which the main stem is pruned when excessive growth is expected. However, there can be a decrease in yield when pinching is undertaken when the risk of lodging is relatively low. Therefore, it is important that pinching is performed after the future risk of lodging has been determined. The lodging angle at the full maturity stage (R8) can be explained using a multiple regression model with main stem elongation from the sixth leaf stage (V6) to the blooming stage (R1) and main stem length at the full seed stage (R6) as the explanatory variables. The objective of this study was to develop an areal lodging prediction method by combining a main stem elongation model with areal main stem length estimation using UAV remote sensing. The main stem elongation model from emergence to R1 was a logistic regression formula with the temperature and daylight hours functions f (Ti, Di) as the explanatory variables. The main stem elongation model from R1 to the peak main stem length was a linear regression formula with the main stem length of R1 as the explanatory variable. The model that synthesized these two regression formulas were used as the main stem elongation model from emergence to R8. The accuracy of the main stem elongation model was tested on the test data, and the average RMSE was 5.3. For the areal main stem length estimation by UAV remote sensing, we proposed a soil-adjusted vegetation index (SAVIvc) that takes vegetation cover into account. SAVIvc was more accurate in estimating the main stem length than the previously reported vegetation index (R2 = 0.78, p < 0.001). The main stem length estimated by the main stem elongation model combined with SAVIvc was substituted into a multiple regression model of lodging angle to test the accuracy of the areal lodging prediction method. The method was able to predict lodging angles with an accuracy of RMSE = 8.8. These results suggest that the risk of lodging can be estimated in an areal manner prior to pinching, even though the actual occurrence is affected by wind.
在大豆中，强风和暴雨有时会引起倒伏，导致产量和品质下降。防止倒伏的技术措施包括“掐枝”，即在预计植株生长过快时修剪主干。然而，在倒伏风险相对较低的情况下进行采摘，可能会导致产量下降。因此，在确定未来的住宿风险之后进行捏取是很重要的。完全成熟期(R8)倒伏角可以用以第六叶期(V6)至开花期(R1)主茎伸长和全种期(R6)主茎长为解释变量的多元回归模型来解释。本研究的目的是利用无人机遥感技术，将主茎伸长模型与主茎面积长度估算相结合，建立一种区域倒伏预测方法。主茎伸长模型为以温度和日照时间函数f (Ti, Di)为解释变量的logistic回归公式。从R1到主茎峰值长度的主茎伸长模型为以R1主茎长度为解释变量的线性回归公式。将综合这两个回归公式的模型作为出苗期至R8的主要茎伸长模型。对试验数据进行主杆伸长模型的准确性检验，平均RMSE为5.3。针对无人机遥感估算面积主茎长，提出了考虑植被覆盖度的土壤调整植被指数(savvc)。savvc在估算主茎长方面比以往报道的植被指数更准确(R2 = 0.78, p < 0.001)。将主茎伸长模型结合savvc估算的主茎长度代入倒伏角多元回归模型，检验面积倒伏预测方法的准确性。该方法预测倒伏角度的RMSE精度为8.8。这些结果表明，即使实际发生受到风的影响，也可以在掐之前以一种区域方式估计倒伏的风险。
{"title":"Prediction of Areal Soybean Lodging Using a Main Stem Elongation Model and a Soil-Adjusted Vegetation Index That Accounts for the Ratio of Vegetation Cover","authors":"T. Konno, K. Homma","doi":"10.3390/rs15133446","DOIUrl":"https://doi.org/10.3390/rs15133446","url":null,"abstract":"In soybean, lodging is sometimes caused by strong winds and rains, resulting in a decrease in yield and quality. Technical measures against lodging include “pinching”, in which the main stem is pruned when excessive growth is expected. However, there can be a decrease in yield when pinching is undertaken when the risk of lodging is relatively low. Therefore, it is important that pinching is performed after the future risk of lodging has been determined. The lodging angle at the full maturity stage (R8) can be explained using a multiple regression model with main stem elongation from the sixth leaf stage (V6) to the blooming stage (R1) and main stem length at the full seed stage (R6) as the explanatory variables. The objective of this study was to develop an areal lodging prediction method by combining a main stem elongation model with areal main stem length estimation using UAV remote sensing. The main stem elongation model from emergence to R1 was a logistic regression formula with the temperature and daylight hours functions f (Ti, Di) as the explanatory variables. The main stem elongation model from R1 to the peak main stem length was a linear regression formula with the main stem length of R1 as the explanatory variable. The model that synthesized these two regression formulas were used as the main stem elongation model from emergence to R8. The accuracy of the main stem elongation model was tested on the test data, and the average RMSE was 5.3. For the areal main stem length estimation by UAV remote sensing, we proposed a soil-adjusted vegetation index (SAVIvc) that takes vegetation cover into account. SAVIvc was more accurate in estimating the main stem length than the previously reported vegetation index (R2 = 0.78, p < 0.001). The main stem length estimated by the main stem elongation model combined with SAVIvc was substituted into a multiple regression model of lodging angle to test the accuracy of the areal lodging prediction method. The method was able to predict lodging angles with an accuracy of RMSE = 8.8. These results suggest that the risk of lodging can be estimated in an areal manner prior to pinching, even though the actual occurrence is affected by wind.","PeriodicalId":20944,"journal":{"name":"Remote. Sens.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85673446","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}
Rubber (Hevea brasiliensis Muell.) plantations are among the most critical agricultural ecosystems in tropical regions, playing a vital role in regional carbon balance. Accurate large-scale biomass estimation for these plantations remains a challenging task due to the severe signal saturation problem. Recent advances in remote sensing big data, cloud platforms, and machine learning have facilitated the precise acquisition of key physiological variables, such as stand age (A) and canopy height (H), which are critical parameters for biomass estimation but have been underutilized in prior studies. Using Hainan Island—the second-largest rubber planting base in China—as a case study, we integrated extensive ground surveys, maps of stand age and canopy height, remote sensing indicators (RSIs), and geographical and climate indicators (ECIs) to ascertain the optimal method for estimating rubber plantation biomass. We compared different inputs and estimation approaches (direct and indirect) using the random forest algorithm and analyzed the spatiotemporal characteristics of rubber plantation biomass on Hainan Island. The results indicated that the traditional model (RSIs + ECIs) had low accuracy and significant estimation bias (R2 = 0.24, RMSE = 38.36 mg/ha). The addition of either stand age or canopy height considerably enhance model accuracy (R2 = 0.77, RMSE ≈ 21.12 mg/ha). Moreover, incorporating the DBH obtained through indirect inversion yielded even greater predictive accuracy (R2 = 0.97, RMSE = 7.73 mg/ha), outperforming estimates derived from an allometric equation model input with the DBH (R2 = 0.67, RMSE = 25.43 mg/ha). However, augmenting the model with stand age, canopy height, or their combination based on RSIs, ECIs, and DBH only marginally improved the accuracy. Consequently, it is not recommended in scenarios with limited data and computing resources. Employing the optimal model, we generated biomass maps of rubber plantations on Hainan Island for 2016 and 2020, revealing that the spatiotemporal distribution pattern of the biomass is closely associated with the establishment year of the rubber plantations. While average biomass in a few areas has undergone slight decreases, total biomass has exhibited significant growth, reaching 5.46 × 107 mg by the end of 2020, underscoring its considerable value as a carbon sink.
{"title":"Comparison of Different Important Predictors and Models for Estimating Large-Scale Biomass of Rubber Plantations in Hainan Island, China","authors":"X. Li, Xincheng Wang, Yuanfeng Gao, Jiuhao Wu, Renxi Cheng, Donghao Ren, Qing Bao, Tin Yun, Zhixiang Wu, Guishui Xie, Bangqian Chen","doi":"10.3390/rs15133447","DOIUrl":"https://doi.org/10.3390/rs15133447","url":null,"abstract":"Rubber (Hevea brasiliensis Muell.) plantations are among the most critical agricultural ecosystems in tropical regions, playing a vital role in regional carbon balance. Accurate large-scale biomass estimation for these plantations remains a challenging task due to the severe signal saturation problem. Recent advances in remote sensing big data, cloud platforms, and machine learning have facilitated the precise acquisition of key physiological variables, such as stand age (A) and canopy height (H), which are critical parameters for biomass estimation but have been underutilized in prior studies. Using Hainan Island—the second-largest rubber planting base in China—as a case study, we integrated extensive ground surveys, maps of stand age and canopy height, remote sensing indicators (RSIs), and geographical and climate indicators (ECIs) to ascertain the optimal method for estimating rubber plantation biomass. We compared different inputs and estimation approaches (direct and indirect) using the random forest algorithm and analyzed the spatiotemporal characteristics of rubber plantation biomass on Hainan Island. The results indicated that the traditional model (RSIs + ECIs) had low accuracy and significant estimation bias (R2 = 0.24, RMSE = 38.36 mg/ha). The addition of either stand age or canopy height considerably enhance model accuracy (R2 = 0.77, RMSE ≈ 21.12 mg/ha). Moreover, incorporating the DBH obtained through indirect inversion yielded even greater predictive accuracy (R2 = 0.97, RMSE = 7.73 mg/ha), outperforming estimates derived from an allometric equation model input with the DBH (R2 = 0.67, RMSE = 25.43 mg/ha). However, augmenting the model with stand age, canopy height, or their combination based on RSIs, ECIs, and DBH only marginally improved the accuracy. Consequently, it is not recommended in scenarios with limited data and computing resources. Employing the optimal model, we generated biomass maps of rubber plantations on Hainan Island for 2016 and 2020, revealing that the spatiotemporal distribution pattern of the biomass is closely associated with the establishment year of the rubber plantations. While average biomass in a few areas has undergone slight decreases, total biomass has exhibited significant growth, reaching 5.46 × 107 mg by the end of 2020, underscoring its considerable value as a carbon sink.","PeriodicalId":20944,"journal":{"name":"Remote. Sens.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86446057","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}
Super-resolution (SR) technology plays a crucial role in improving the spatial resolution of remote sensing images so as to overcome the physical limitations of spaceborne imaging systems. Although deep convolutional neural networks have achieved promising results, most of them overlook the advantage of self-similarity information across different scales and high-dimensional features after the upsampling layers. To address the problem, we propose a hybrid-scale hierarchical transformer network (HSTNet) to achieve faithful remote sensing image SR. Specifically, we propose a hybrid-scale feature exploitation module to leverage the internal recursive information in single and cross scales within the images. To fully leverage the high-dimensional features and enhance discrimination, we designed a cross-scale enhancement transformer to capture long-range dependencies and efficiently calculate the relevance between high-dimension and low-dimension features. The proposed HSTNet achieves the best result in PSNR and SSIM with the UCMecred dataset and AID dataset. Comparative experiments demonstrate the effectiveness of the proposed methods and prove that the HSTNet outperforms the state-of-the-art competitors both in quantitative and qualitative evaluations.
{"title":"Hybrid-Scale Hierarchical Transformer for Remote Sensing Image Super-Resolution","authors":"Jianrun Shang, Mingliang Gao, Qilei Li, Jinfeng Pan, Guofeng Zou, Gwanggil Jeon","doi":"10.3390/rs15133442","DOIUrl":"https://doi.org/10.3390/rs15133442","url":null,"abstract":"Super-resolution (SR) technology plays a crucial role in improving the spatial resolution of remote sensing images so as to overcome the physical limitations of spaceborne imaging systems. Although deep convolutional neural networks have achieved promising results, most of them overlook the advantage of self-similarity information across different scales and high-dimensional features after the upsampling layers. To address the problem, we propose a hybrid-scale hierarchical transformer network (HSTNet) to achieve faithful remote sensing image SR. Specifically, we propose a hybrid-scale feature exploitation module to leverage the internal recursive information in single and cross scales within the images. To fully leverage the high-dimensional features and enhance discrimination, we designed a cross-scale enhancement transformer to capture long-range dependencies and efficiently calculate the relevance between high-dimension and low-dimension features. The proposed HSTNet achieves the best result in PSNR and SSIM with the UCMecred dataset and AID dataset. Comparative experiments demonstrate the effectiveness of the proposed methods and prove that the HSTNet outperforms the state-of-the-art competitors both in quantitative and qualitative evaluations.","PeriodicalId":20944,"journal":{"name":"Remote. Sens.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73226305","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}
Carlos Portillo-Quintero, J. Hernández‐Stefanoni, J. Dupuy
The Puuc Biocultural State Reserve (PBSR) is a unique model for tropical dry forest conservation in Mexico. Preserving forest biodiversity and carbon within the PBSR depends on maintaining low-impact productive activities coordinated by multiple communal and private landowners. In this study, we used state-of-the-art remote sensing data to investigate past spatial patterns in forest clearing dynamics and their relation to forest carbon density and forest plant species richness and diversity in the context of the forest conservation goals of the PBSR. We used a Landsat-based continuous change detection product for the 2000–2021 period and compared it to carbon density and tree species richness models generated from ALOS-2 PALSAR 2 imagery and national scale forest inventory data. The estimated error-adjusted area of detected annual forest clearings from the year 2000 until the year 2021 was 230,511 ha in total (±19,979 ha). The analysis of annual forest clearing frequency and area suggests that although forest clearing was significantly more intensive outside of the PBSR than within the PBSR during the entire 2000–2021 period, there is no evidence suggesting that the frequency and magnitude of forest clearing changed over the years after the creation of the PBSR in 2011. However, an emergent hotspot analysis shows that high spatiotemporal clustering of forest clearing events (hotspots) during the 2012–2021 period was less common than prior to 2011, and these more recent hotspots have been confined to areas outside the PBSR. After comparing forest clearing events to carbon density and tree species richness models, the results show that landowners outside the PBSR often clear forests with lower carbon density and species diversity than those inside the PBSR. This suggests that, compared to landowners outside the PBSR, landowners within the PBSR might be practicing longer fallow periods allowing forests to attain higher carbon density and tree species richness and hence better soil nutrient recovery after land abandonment. In conclusion, our results show that the PBSR effectively acted as a stabilizing forest management scheme during the 2012–2021 period, minimizing the impact of productive activities by lowering the frequency of forest clearing events and preserving late secondary forests within the PBSR. We recommend continuing efforts to provide alternative optimal field data collection strategies and modeling techniques to spatially predict key tropical forest attributes. Combining these models with continuous change detection datasets will allow for underlying ecological processes to be revealed and the generation of information better adapted to forest governance scales.
{"title":"Forest Clearing Dynamics and Its Relation to Remotely Sensed Carbon Density and Plant Species Diversity in the Puuc Biocultural State Reserve, Mexico","authors":"Carlos Portillo-Quintero, J. Hernández‐Stefanoni, J. Dupuy","doi":"10.3390/rs15133445","DOIUrl":"https://doi.org/10.3390/rs15133445","url":null,"abstract":"The Puuc Biocultural State Reserve (PBSR) is a unique model for tropical dry forest conservation in Mexico. Preserving forest biodiversity and carbon within the PBSR depends on maintaining low-impact productive activities coordinated by multiple communal and private landowners. In this study, we used state-of-the-art remote sensing data to investigate past spatial patterns in forest clearing dynamics and their relation to forest carbon density and forest plant species richness and diversity in the context of the forest conservation goals of the PBSR. We used a Landsat-based continuous change detection product for the 2000–2021 period and compared it to carbon density and tree species richness models generated from ALOS-2 PALSAR 2 imagery and national scale forest inventory data. The estimated error-adjusted area of detected annual forest clearings from the year 2000 until the year 2021 was 230,511 ha in total (±19,979 ha). The analysis of annual forest clearing frequency and area suggests that although forest clearing was significantly more intensive outside of the PBSR than within the PBSR during the entire 2000–2021 period, there is no evidence suggesting that the frequency and magnitude of forest clearing changed over the years after the creation of the PBSR in 2011. However, an emergent hotspot analysis shows that high spatiotemporal clustering of forest clearing events (hotspots) during the 2012–2021 period was less common than prior to 2011, and these more recent hotspots have been confined to areas outside the PBSR. After comparing forest clearing events to carbon density and tree species richness models, the results show that landowners outside the PBSR often clear forests with lower carbon density and species diversity than those inside the PBSR. This suggests that, compared to landowners outside the PBSR, landowners within the PBSR might be practicing longer fallow periods allowing forests to attain higher carbon density and tree species richness and hence better soil nutrient recovery after land abandonment. In conclusion, our results show that the PBSR effectively acted as a stabilizing forest management scheme during the 2012–2021 period, minimizing the impact of productive activities by lowering the frequency of forest clearing events and preserving late secondary forests within the PBSR. We recommend continuing efforts to provide alternative optimal field data collection strategies and modeling techniques to spatially predict key tropical forest attributes. Combining these models with continuous change detection datasets will allow for underlying ecological processes to be revealed and the generation of information better adapted to forest governance scales.","PeriodicalId":20944,"journal":{"name":"Remote. Sens.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75189280","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}
Mining-induced or enhanced geo-hazards (MGHs) pose significant risks in rural mountainous regions with underground mining operations by harming groundwater layers, water circulation systems, and mountain stability. MGHs occurring in naturally contaminated environments can severely amplify socio-environmental risks. A high correlation was found among undermining development, precipitation, and hazards; however, details of MGHs have yet to be adequately characterized. This study investigated multiple mining-induced/enhanced geo-hazards in a naturally contaminated mountain region in Bone Bolango Regency, Gorontalo Province, Indonesia, in 2020, where a rapidly developing coexisting mining sector was present. We utilized PlanetScope’s CubeSat constellations and Sentinel-1 dataset to assess the volume, distribution, pace, and pattern of MGHs. The findings reveal that severe landslides and floods accelerated the mobilization of potentially toxic elements (PTEs) via the river water system, thus considerably exacerbating socio-environmental risks. These results indicate potential dangers of enhanced PTE contamination for marine ecosystems and humans at a regional level. The study design and data used facilitated a comprehensive assessment of the MGHs and associated risks, providing important information for decision-makers and stakeholders. However, limitations in the methodology should be considered when interpreting the findings. The societal benefits of this study include informing policies and practices that aim to mitigate the negative impacts of mining activities on the environment and society at the local and regional levels.
{"title":"Monitoring Mining-Induced Geo-Hazards in a Contaminated Mountainous Region of Indonesia Using Satellite Imagery","authors":"Satomi Kimijima, M. Nagai","doi":"10.3390/rs15133436","DOIUrl":"https://doi.org/10.3390/rs15133436","url":null,"abstract":"Mining-induced or enhanced geo-hazards (MGHs) pose significant risks in rural mountainous regions with underground mining operations by harming groundwater layers, water circulation systems, and mountain stability. MGHs occurring in naturally contaminated environments can severely amplify socio-environmental risks. A high correlation was found among undermining development, precipitation, and hazards; however, details of MGHs have yet to be adequately characterized. This study investigated multiple mining-induced/enhanced geo-hazards in a naturally contaminated mountain region in Bone Bolango Regency, Gorontalo Province, Indonesia, in 2020, where a rapidly developing coexisting mining sector was present. We utilized PlanetScope’s CubeSat constellations and Sentinel-1 dataset to assess the volume, distribution, pace, and pattern of MGHs. The findings reveal that severe landslides and floods accelerated the mobilization of potentially toxic elements (PTEs) via the river water system, thus considerably exacerbating socio-environmental risks. These results indicate potential dangers of enhanced PTE contamination for marine ecosystems and humans at a regional level. The study design and data used facilitated a comprehensive assessment of the MGHs and associated risks, providing important information for decision-makers and stakeholders. However, limitations in the methodology should be considered when interpreting the findings. The societal benefits of this study include informing policies and practices that aim to mitigate the negative impacts of mining activities on the environment and society at the local and regional levels.","PeriodicalId":20944,"journal":{"name":"Remote. Sens.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80364605","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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