Content of Remote Sensing and Geographic Information System in our journal

Published in last 1 year |  In last 2 years |  In last 3 years |  All Please wait a minute... For Selected: Download Citations EndNote Reference Manager ProCite BibTeX RefWorks Toggle Thumbnails Select Machine learning-based remote sensing retrievals of dissolved organic carbon in the Yangtze River Estuary Hao CHEN, Xianqiang HE, Run LI, Fang CAO Journal of East China Normal University(Natural Science)    2024, 2024 (4): 123-136.   DOI: 10.3969/j.issn.1000-5641.2024.04.012 Abstract （12）   HTML （0）    PDF （17080KB）（2）       Knowledge map    Dissolved organic carbon (DOC) is the largest reservoir of active organic matter in the ocean. Accurate characterization of the spatial and temporal patterns of DOC in large-river estuaries and neighboring coastal margins will help improve our understanding of biogeochemical processes and the fate of fluvial DOC across the estuary−coastal ocean continuum. By retrieving the absorption properties of colored dissolved organic matter (CDOM) in the dissolved organic matter (DOM) pool using machine learning models, and based on the correlation between CDOM absorption and DOC concentrations, we developed an ocean DOC algorithm for the GOCI satellite. The results indicated that the Nu-Supporting Vector Regression model performed best in retrieving CDOM absorption properties, with mean absolute percent differences (MAPD) of 32% and 8.6% for the CDOM absorption coefficient at 300 nm (aCDOM(300)) and CDOM spectral slope over the wavelength range of 275 ~ 295 nm (S275–295). Estimates of DOC concentrations based on the seasonal linear relationship between aCDOM(300) and DOC were achieved with high retrieval accuracy, with MAPD of 11% and 14% for the training dataset using field measurements and validation datasets on satellite platforms, respectively. Application of the DOC algorithm to GOCI satellite imagery revealed that DOC levels varied dramatically at both seasonal and hourly scales. Elevated surface DOC concentrations were largely associated with summer and lower DOC concentrations in winter as a result of seasonal cycles of Yangtze River discharges. The DOC also changed rapidly on an hourly scale due to the influence of the tide and local wind regimes. This study provides a useful method to improve our understanding of DOC dynamics and their environmental controls across the estuarine −coastal ocean continuum. Table and Figures | Reference | Related Articles | Metrics Select Accounting and change analysis of ecological asset in Yingpu Street, Shanghai Chen ZHANG, Zhi TANG, Yan LU, Rui LI, Zhongyang GUO Journal of East China Normal University(Natural Science)    2024, 2024 (4): 137-149.   DOI: 10.3969/j.issn.1000-5641.2024.04.013 Abstract （3）   HTML （0）    PDF （4177KB）（1）       Knowledge map    This study investigated the ecological assets of Yingpu Street, Shanghai, using historical aerial imagery data. Using various methods, such as the ecological assets balance sheet and correlation analysis, changes in the ecological assets of Yingpu from 2000 to 2021, as well as the underlying mechanisms behind these changes, were analyzed. The results showed that, in 2021, the ecological assets of Yingpu Street mainly consisted of arable land, wetlands, and grasslands with an overall moderate quality. The total ESV(ecological service value) was 9.39 × 106 CNY (Chinese Yuan) and was mainly due to contributions from water conservation and waste treatment services. The ecological assets of Yingpu decreased significantly during the period from 2000 to 2021, with the stock and flow decreasing by 33.07 and 22.97%, respectively. Urban construction led to a reduction in farmland ecological assets and was the major contributor to the overall decline observed. Returning farmlands to forests and grasslands played a key role in the substantial increase of forest and grassland ecological assets. The ESV of Yingpu was negatively correlated with night light intensity, population, GDP (gross domestic product), land surface temperature, and DEM (digital elevation model) (p < 0.001), but was positively correlated with slope (p < 0.001). Table and Figures | Reference | Related Articles | Metrics Select Remote sensing inversion of multi-period winter wheat canopy water content based on a genetic algorithm Suyun NIE, Bin YANG, Wei XIA, Yuan ZHANG Journal of East China Normal University(Natural Science)    2023, 2023 (3): 71-81.   DOI: 10.3969/j.issn.1000-5641.2023.03.008 Abstract （207）   HTML （17）    PDF （8950KB）（59）       Knowledge map    The remote sensing inversion of crop canopy water content is a valuable for assessing drought stress of wheat fields and implementing precision irrigation. This study aimed to quickly obtain the canopy water content during the growth period for winter wheat in North China by using multi-temporal remote sensing images of Landsat-8 OLI and Sentinel-2 MSI from January to May 2017. The regression relationship was constructed with NDWI and measured water content in a wheat field via the mixed pixel decomposition model. The genetic algorithm was then used to inverse the canopy water content. The proposed method demonstrated better performance compared to ground-truth data, with the coefficient of determination (R2) and the root mean square error (RMSE) of 0.567 and 5.6%, respectively. Additionally, the error was reduced by more than 20% when compared to the direct inversion based on NDWI. This study indicates that quantification of different linear mixing ratios of wheat canopy and background soil can effectively eliminate the influence of soil on wheat water content inversion, and is crucial for the application of remote sensing to wheat growth monitoring. Table and Figures | Reference | Related Articles | Metrics Select Emergency monitoring of remote sensing for flood inundation region based on SAR texture and LightGBM Cheng SUN, Fang SHEN, Rugang TANG Journal of East China Normal University(Natural Science)    2023, 2023 (3): 82-92.   DOI: 10.3969/j.issn.1000-5641.2023.03.009 Abstract （221）   HTML （20）    PDF （2605KB）（96）       Knowledge map    In response to the need for high timeliness and accuracy monitoring for inundation region during flood disaster, a new extraction method of water areas based on SAR texture and LightGBM was proposed. Compared with other methods, such as the SDWI water index, SVM, RF and GBDT methods, it shows that the accuracy of water extraction of river, lake and flooded area is beyond 98% and higher than other methods. Meanwhile, the operating efficiency of the proposed method is 20 ~ 100 times higher than other methods, which greatly improves the timeliness of inundation emergency monitoring during flood disaster. Table and Figures | Reference | Related Articles | Metrics