Preserving biodiversity and maintaining ecological balance is essential in current environmental conditions.It is challenging to determine vegetation using traditional map classification approaches.The primary issue i...Preserving biodiversity and maintaining ecological balance is essential in current environmental conditions.It is challenging to determine vegetation using traditional map classification approaches.The primary issue in detecting vegetation pattern is that it appears with complex spatial structures and similar spectral properties.It is more demandable to determine the multiple spectral ana-lyses for improving the accuracy of vegetation mapping through remotely sensed images.The proposed framework is developed with the idea of ensembling three effective strategies to produce a robust architecture for vegetation mapping.The architecture comprises three approaches,feature-based approach,region-based approach,and texture-based approach for classifying the vegetation area.The novel Deep Meta fusion model(DMFM)is created with a unique fusion frame-work of residual stacking of convolution layers with Unique covariate features(UCF),Intensity features(IF),and Colour features(CF).The overhead issues in GPU utilization during Convolution neural network(CNN)models are reduced here with a lightweight architecture.The system considers detailing feature areas to improve classification accuracy and reduce processing time.The proposed DMFM model achieved 99%accuracy,with a maximum processing time of 130 s.The training,testing,and validation losses are degraded to a significant level that shows the performance quality with the DMFM model.The system acts as a standard analysis platform for dynamic datasets since all three different fea-tures,such as Unique covariate features(UCF),Intensity features(IF),and Colour features(CF),are considered very well.展开更多
Core sediments from the Muthupettai mangroves on the southeast coast of India were analyzed for soil texture,total nitrogen,organic carbon,phosphorus and heavy metals(Fe,Mn,Cr,Cu,Ni,Pb,Zn and Cd).The distinct seasonal...Core sediments from the Muthupettai mangroves on the southeast coast of India were analyzed for soil texture,total nitrogen,organic carbon,phosphorus and heavy metals(Fe,Mn,Cr,Cu,Ni,Pb,Zn and Cd).The distinct seasonal variation in the distribution of metals in the sediments was observed.The minimum concentration was recorded in river mouth and the maximum was in lagoon.High metal concentration in sediment was observed during monsoon and low concentration in summer.The total nu-trient in lagoon and river mouth was recorded in the range of 4.528 to 8.526 mg g-1 for organic carbon,2.213 to 10.5 mg g-1 for nitro-gen and 0.824 to 7.22 mg g-1.展开更多
文摘Preserving biodiversity and maintaining ecological balance is essential in current environmental conditions.It is challenging to determine vegetation using traditional map classification approaches.The primary issue in detecting vegetation pattern is that it appears with complex spatial structures and similar spectral properties.It is more demandable to determine the multiple spectral ana-lyses for improving the accuracy of vegetation mapping through remotely sensed images.The proposed framework is developed with the idea of ensembling three effective strategies to produce a robust architecture for vegetation mapping.The architecture comprises three approaches,feature-based approach,region-based approach,and texture-based approach for classifying the vegetation area.The novel Deep Meta fusion model(DMFM)is created with a unique fusion frame-work of residual stacking of convolution layers with Unique covariate features(UCF),Intensity features(IF),and Colour features(CF).The overhead issues in GPU utilization during Convolution neural network(CNN)models are reduced here with a lightweight architecture.The system considers detailing feature areas to improve classification accuracy and reduce processing time.The proposed DMFM model achieved 99%accuracy,with a maximum processing time of 130 s.The training,testing,and validation losses are degraded to a significant level that shows the performance quality with the DMFM model.The system acts as a standard analysis platform for dynamic datasets since all three different fea-tures,such as Unique covariate features(UCF),Intensity features(IF),and Colour features(CF),are considered very well.
文摘Core sediments from the Muthupettai mangroves on the southeast coast of India were analyzed for soil texture,total nitrogen,organic carbon,phosphorus and heavy metals(Fe,Mn,Cr,Cu,Ni,Pb,Zn and Cd).The distinct seasonal variation in the distribution of metals in the sediments was observed.The minimum concentration was recorded in river mouth and the maximum was in lagoon.High metal concentration in sediment was observed during monsoon and low concentration in summer.The total nu-trient in lagoon and river mouth was recorded in the range of 4.528 to 8.526 mg g-1 for organic carbon,2.213 to 10.5 mg g-1 for nitro-gen and 0.824 to 7.22 mg g-1.
