The leaf-response to three-soil applied treatments of Paclobutrazol (PBZ;1000, 2000 and 0 ppm-control) was studied in a high-density plantation of eight guava (Psidium guajava) genotypes trees. All materials were prun...The leaf-response to three-soil applied treatments of Paclobutrazol (PBZ;1000, 2000 and 0 ppm-control) was studied in a high-density plantation of eight guava (Psidium guajava) genotypes trees. All materials were prunned in vase form, with two to three major branches, yearly prunning for triggering the annual production cycle, and average height of 2.0 m. The dataset comprises fourth radiometric indices highly related to plant physiological activities. The dataset model took into account data collection dates, guava genotypes, and the positional effect of sun radiation on leaves based on their proximity to the canopy level and downward to the base of the woody seasonal-branch. Unexpectedly, there were no significant differences (NS) in PBZ treatments for genotypes, leaf position and radiometric indices. Analysis of the radiometric indices data revealed that anthocyanin (ARI index) and chlorophyll (PRI index) have a strong inverse relationship. Significant differences (P ≤ 0.05) were found between guava genotypes, and anthocyanin content;these results show that guava genotypes have varied responses, which could derive in their classification based-on drought resistance or low water requirements, however, it is important to note that additional research is required to determine the scope of these indications.展开更多
Background:Species Distribution Modelling(SDM)coupled with freely available multispectral imagery from Sentinel-2(S2)satellite provides an immense contribution in monitoring invasive species.However,attempts to evalua...Background:Species Distribution Modelling(SDM)coupled with freely available multispectral imagery from Sentinel-2(S2)satellite provides an immense contribution in monitoring invasive species.However,attempts to evaluate the performances of SDMs using S2 spectral bands and S2 Radiometric Indices(S2-RIs)and biophysical variables,in particular,were limited.Hence,this study aimed at evaluating the performance of six commonly used SDMs and one ensemble model for S2-based variables in modelling the current distribution of Prosopis juliflora in the lower Awash River basin,Ethiopia.Thirty-five variables were computed from Sentinel-2B level-2A,and out of the variables,twelve significant variables were selected using Variable Inflation Factor(VIF).A total of 680 presence and absence data were collected to train and validate variables using the tenfold bootstrap replication approach in the R software“sdm”package.The performance of the models was evaluated using sensitivity,specificity,True Skill Statistics(TSS),kappa coefficient,area under the curve(AUC),and correlation.Results:Our findings demonstrated that except bioclim all machine learning and regression models provided successful prediction.Among the tested models,Random Forest(RF)performed better with 93%TSS and 99%AUC followed by Boosted Regression Trees(BRT),ensemble,Generalized Additive Model(GAM),Support Vector Machine(SVM),and Generalized Linear Model(GLM)in decreasing order.The relative influence of vegetation indices was the highest followed by soil indices,biophysical variables,and water indices in decreasing order.According to RF prediction,16.14%(1553.5 km^(2))of the study area was invaded by the alien species.Conclusions:Our results highlighted that S2-RIs and biophysical variables combined with machine learning and regression models have a higher capacity to model invasive species distribution.Besides,the use of machine learning algorithms such as RF algorithm is highly essential for remote sensing-based invasive SDM.展开更多
文摘The leaf-response to three-soil applied treatments of Paclobutrazol (PBZ;1000, 2000 and 0 ppm-control) was studied in a high-density plantation of eight guava (Psidium guajava) genotypes trees. All materials were prunned in vase form, with two to three major branches, yearly prunning for triggering the annual production cycle, and average height of 2.0 m. The dataset comprises fourth radiometric indices highly related to plant physiological activities. The dataset model took into account data collection dates, guava genotypes, and the positional effect of sun radiation on leaves based on their proximity to the canopy level and downward to the base of the woody seasonal-branch. Unexpectedly, there were no significant differences (NS) in PBZ treatments for genotypes, leaf position and radiometric indices. Analysis of the radiometric indices data revealed that anthocyanin (ARI index) and chlorophyll (PRI index) have a strong inverse relationship. Significant differences (P ≤ 0.05) were found between guava genotypes, and anthocyanin content;these results show that guava genotypes have varied responses, which could derive in their classification based-on drought resistance or low water requirements, however, it is important to note that additional research is required to determine the scope of these indications.
文摘Background:Species Distribution Modelling(SDM)coupled with freely available multispectral imagery from Sentinel-2(S2)satellite provides an immense contribution in monitoring invasive species.However,attempts to evaluate the performances of SDMs using S2 spectral bands and S2 Radiometric Indices(S2-RIs)and biophysical variables,in particular,were limited.Hence,this study aimed at evaluating the performance of six commonly used SDMs and one ensemble model for S2-based variables in modelling the current distribution of Prosopis juliflora in the lower Awash River basin,Ethiopia.Thirty-five variables were computed from Sentinel-2B level-2A,and out of the variables,twelve significant variables were selected using Variable Inflation Factor(VIF).A total of 680 presence and absence data were collected to train and validate variables using the tenfold bootstrap replication approach in the R software“sdm”package.The performance of the models was evaluated using sensitivity,specificity,True Skill Statistics(TSS),kappa coefficient,area under the curve(AUC),and correlation.Results:Our findings demonstrated that except bioclim all machine learning and regression models provided successful prediction.Among the tested models,Random Forest(RF)performed better with 93%TSS and 99%AUC followed by Boosted Regression Trees(BRT),ensemble,Generalized Additive Model(GAM),Support Vector Machine(SVM),and Generalized Linear Model(GLM)in decreasing order.The relative influence of vegetation indices was the highest followed by soil indices,biophysical variables,and water indices in decreasing order.According to RF prediction,16.14%(1553.5 km^(2))of the study area was invaded by the alien species.Conclusions:Our results highlighted that S2-RIs and biophysical variables combined with machine learning and regression models have a higher capacity to model invasive species distribution.Besides,the use of machine learning algorithms such as RF algorithm is highly essential for remote sensing-based invasive SDM.
