Quantifying forest stand parameters is crucial in forestry research and environmental monitoring because it provides important factors for analyzing forest structure and comprehending forest resources.And the estimati...Quantifying forest stand parameters is crucial in forestry research and environmental monitoring because it provides important factors for analyzing forest structure and comprehending forest resources.And the estimation of crown density and volume has always been a prominent topic in forestry remote sensing.Based on GF-2 remote sensing data,sample plot survey data and forest resource survey data,this study used the Chinese fir(Cunninghamia lanceolata(Lamb.)Hook.)and Pinus massoniana Lamb.as research objects to tackle the key challenges in the use of remote sensing technology.The Boruta feature selection technique,together with multiple stepwise and Cubist regression models,was used to estimate crown density and volume in portions of the research area’s stands,introducing novel technological methods for estimating stand parameters.The results show that:(i)the Boruta algorithm is effective at selecting the feature set with the strongest correlation with the dependent variable,which solves the problem of data and the loss of original feature data after dimensionality reduction;(ii)using the Cubist method to build the model yields better results than using multiple stepwise regression.The Cubist regression model’s coefficient of determination(R^(2))is all more than 0.67 in the Chinese fir plots and 0.63 in the P.massoniana plots.As a result,combining the two methods can increase the estimation accuracy of stand parameters,providing a theoretical foundation and technical support for future studies.展开更多
The geomorphic studies are extremely dependent on the quality and spatial resolution of digital elevation model(DEM)data.The unique terrain characteristics of a particular landscape are derived from DEM,which are resp...The geomorphic studies are extremely dependent on the quality and spatial resolution of digital elevation model(DEM)data.The unique terrain characteristics of a particular landscape are derived from DEM,which are responsible for initiation and development of ephemeral gullies.As the topographic features of an area significantly influences on the erosive power of the water flow,it is an important task the extraction of terrain features from DEM to properly research gully erosion.Alongside,topography is highly correlated with other geo-environmental factors i.e.geology,climate,soil types,vegetation density and floristic composition,runoff generation,which ultimately influences on gully occurrences.Therefore,terrain morphometric attributes derived from DEM data are used in spatial prediction of gully erosion susceptibility(GES)mapping.In this study,remote sensing-Geographic information system(GIS)techniques coupled with machine learning(ML)methods has been used for GES mapping in the parts of Semnan province,Iran.Current research focuses on the comparison of predicted GES result by using three types of DEM i.e.Advanced Land Observation satellite(ALOS),ALOS World 3D-30 m(AW3D30)and Advanced Space borne Thermal Emission and Reflection Radiometer(ASTER)in different resolutions.For further progress of our research work,here we have used thirteen suitable geo-environmental gully erosion conditioning factors(GECFs)based on the multi-collinearity analysis.ML methods of conditional inference forests(Cforest),Cubist model and Elastic net model have been chosen for modelling GES accordingly.Variable’s importance of GECFs was measured through sensitivity analysis and result show that elevation is the most important factor for occurrences of gullies in the three aforementioned ML methods(Cforest=21.4,Cubist=19.65 and Elastic net=17.08),followed by lithology and slope.Validation of the model’s result was performed through area under curve(AUC)and other statistical indices.The validation result of AUC has shown that Cforest is the most appropriate model for predicting the GES assessment in three different DEMs(AUC value of Cforest in ALOS DEM is 0.994,AW3D30 DEM is 0.989 and ASTER DEM is 0.982)used in this study,followed by elastic net and cubist model.The output result of GES maps will be used by decision-makers for sustainable development of degraded land in this study area.展开更多
基金supported by the project of the National Technology Extension Fund of Forestry,‘Forest Vegetation Carbon Storage Monitoring Technology Based on Watershed Algorithm’([2019]06)the National Natural Science Foundation of China,‘Study on Crown Models for Larix olgensis Based on Tree Growth’(31870620).
文摘Quantifying forest stand parameters is crucial in forestry research and environmental monitoring because it provides important factors for analyzing forest structure and comprehending forest resources.And the estimation of crown density and volume has always been a prominent topic in forestry remote sensing.Based on GF-2 remote sensing data,sample plot survey data and forest resource survey data,this study used the Chinese fir(Cunninghamia lanceolata(Lamb.)Hook.)and Pinus massoniana Lamb.as research objects to tackle the key challenges in the use of remote sensing technology.The Boruta feature selection technique,together with multiple stepwise and Cubist regression models,was used to estimate crown density and volume in portions of the research area’s stands,introducing novel technological methods for estimating stand parameters.The results show that:(i)the Boruta algorithm is effective at selecting the feature set with the strongest correlation with the dependent variable,which solves the problem of data and the loss of original feature data after dimensionality reduction;(ii)using the Cubist method to build the model yields better results than using multiple stepwise regression.The Cubist regression model’s coefficient of determination(R^(2))is all more than 0.67 in the Chinese fir plots and 0.63 in the P.massoniana plots.As a result,combining the two methods can increase the estimation accuracy of stand parameters,providing a theoretical foundation and technical support for future studies.
文摘The geomorphic studies are extremely dependent on the quality and spatial resolution of digital elevation model(DEM)data.The unique terrain characteristics of a particular landscape are derived from DEM,which are responsible for initiation and development of ephemeral gullies.As the topographic features of an area significantly influences on the erosive power of the water flow,it is an important task the extraction of terrain features from DEM to properly research gully erosion.Alongside,topography is highly correlated with other geo-environmental factors i.e.geology,climate,soil types,vegetation density and floristic composition,runoff generation,which ultimately influences on gully occurrences.Therefore,terrain morphometric attributes derived from DEM data are used in spatial prediction of gully erosion susceptibility(GES)mapping.In this study,remote sensing-Geographic information system(GIS)techniques coupled with machine learning(ML)methods has been used for GES mapping in the parts of Semnan province,Iran.Current research focuses on the comparison of predicted GES result by using three types of DEM i.e.Advanced Land Observation satellite(ALOS),ALOS World 3D-30 m(AW3D30)and Advanced Space borne Thermal Emission and Reflection Radiometer(ASTER)in different resolutions.For further progress of our research work,here we have used thirteen suitable geo-environmental gully erosion conditioning factors(GECFs)based on the multi-collinearity analysis.ML methods of conditional inference forests(Cforest),Cubist model and Elastic net model have been chosen for modelling GES accordingly.Variable’s importance of GECFs was measured through sensitivity analysis and result show that elevation is the most important factor for occurrences of gullies in the three aforementioned ML methods(Cforest=21.4,Cubist=19.65 and Elastic net=17.08),followed by lithology and slope.Validation of the model’s result was performed through area under curve(AUC)and other statistical indices.The validation result of AUC has shown that Cforest is the most appropriate model for predicting the GES assessment in three different DEMs(AUC value of Cforest in ALOS DEM is 0.994,AW3D30 DEM is 0.989 and ASTER DEM is 0.982)used in this study,followed by elastic net and cubist model.The output result of GES maps will be used by decision-makers for sustainable development of degraded land in this study area.