Zhenyu Zhang | University of Southern Queensland (original) (raw)
Papers by Zhenyu Zhang
… of the Photogrammetry, Remote Sensing and …, 2008
Airborne Light Detection and Ranging (LiDAR) - also referred to as Airborne Laser Scanning (ALS),... more Airborne Light Detection and Ranging (LiDAR) - also referred to as Airborne Laser Scanning (ALS), provides means for high density and high accuracy topographic data acquisition. LiDAR data have become a major source of digital terrain data and have been used in a wide of ...
Although remotely sensed data have been widely explored for forest applications, passive remote s... more Although remotely sensed data have been widely explored for forest applications, passive remote sensing techniques are limited in their ability to capture forest structural complexity, particularly in uneven-aged, mixed species forests with multiple canopy layers. Generally, these techniques are only able to provide information on horizontal (two-dimensional) forest extent. The vertical forest structure (or the interior of the canopy and understorey vegetation) cannot be mapped using these passive remote sensing techniques. Fortunately, it has been shown that active remote sensing techniques via airborne LiDAR (light detection and ranging) with capability of canopy penetration yields such high density sampling that detailed description of the forest structure in three-dimensions can be obtained. Accordingly, much interest is attached to exploring the application of this approach for identifying the distribution of designated vegetation communities. However, the suitability of LiDAR ...
Lecture Notes in Geoinformation and Cartography, 2013
It has been shown that new remote sensing technologies have the potential to complement deficienc... more It has been shown that new remote sensing technologies have the potential to complement deficiencies of conventional methods such as aerial photograph interpretation and field sampling as well as improve the accuracy, reduce costs, and increase the number of applications within various forest environments. Newly available high resolution spatial data such as small footprint, multiple-return, discrete airborne LiDAR data and WorldView-2 satellite imagery offer excellent opportunities to develop new and efficient ways of solving conventional problems in forestry. However, the development of a comprehensive procedure for deployment of these new remote sensing data to create forest mapping products that are comparable and/or superior in accuracy to conventional photo-interpreted maps poses big challenges. Proper use of high spatial resolution data with object-based image analysis approach and nonparametric classification method such as decision trees may offer an alternative to aerial photograph interpretation in support of forest classification and mapping. This study presented ways of processing airborne LiDAR data and WorldView-2 satellite imagery for object-based forest species classification using decision trees in the Strzelecki Ranges, one of the four major Victorian areas of cool temperate rainforest in Australia. The results showed the contribution of four new WorldView-2 image bands to forest classifications, and demonstrated that the integration of airborne LiDAR and eight WorldView-2 bands significantly improved the classification accuracy.
It has been shown that new remote sensing technologies have the potential to complement deficienc... more It has been shown that new remote sensing technologies have the potential to complement deficiencies of conventional methods such as aerial photograph interpretation and field sampling as well as improve the accuracy, reduce costs, and increase the number of applications within various forest environments. Newly available high resolution spatial data such as small footprint, multiple-return, discrete airborne LiDAR data and WorldView-2 satellite imagery offer excellent opportunities to develop new and efficient ways of solving conventional problems in forestry. However, the development of a comprehensive procedure for deployment of these new remote sensing data to create forest mapping products that are comparable and/or superior in accuracy to conventional photo-interpreted maps poses big challenges. Proper use of high spatial resolution data with object-based image analysis approach and nonparametric classification method such as decision trees may offer an alternative to aerial photograph interpretation in support of forest classification and mapping. This study presented ways of processing airborne LiDAR data and WorldView-2 satellite imagery for object-based forest species classification using decision trees in the Strzelecki Ranges, one of the four major Victorian areas of cool temperate rainforest in Australia. The results showed the contribution of four new WorldView-2 image bands to forest classifications, and demonstrated that the integration of airborne LiDAR and eight WorldView-2 bands significantly improved the classification accuracy.
gis.vsb.cz
It has been recognised that airborne LiDAR (light detection and ranging) offers advantages over t... more It has been recognised that airborne LiDAR (light detection and ranging) offers advantages over the interpretation of aerial photographs and processing of multi-spectral and/or hyper-spectral remote sensing data in forest classification. LiDAR with capability of ...
… of the 2009 Surveying and Spatial …, 2009
Airborne light detection and ranging (LiDAR) is one of the most effective means for high quality ... more Airborne light detection and ranging (LiDAR) is one of the most effective means for high quality terrain data acquisition. The high-accuracy and high-density LiDAR data makes it possible to model terrain surface in more detail. Using LiDAR data for DEM generation is becoming a ...
Chan, F., Marinova, D. and Anderssen, R.S. (eds) MODSIM2011, 19th International Congress on Modelling and Simulation., 2011
Although remotely sensed data have been widely explored for forest applications, passive remote s... more Although remotely sensed data have been widely explored for forest applications, passive remote sensing techniques are limited in their ability to capture forest structural complexity, particularly in uneven-aged, mixed species forests with multiple canopy layers. Generally, these techniques are only able to provide information on horizontal (two-dimensional) forest extent. The vertical forest structure (or the interior of the canopy and understorey vegetation) cannot be mapped using these passive remote sensing techniques. Fortunately, it has been shown that active remote sensing techniques via airborne LiDAR (light detection and ranging) with capability of canopy penetration yields such high density sampling that detailed description of the forest structure in three-dimensions can be obtained. Accordingly, much interest is attached to exploring the application of this approach for identifying the distribution of designated vegetation communities. However, the suitability of LiDAR data for the classification of forests with complex structures, particularly for cool temperate rainforest and neighbouring uneven-aged mixed forests in a severely disturbed landscape has hitherto remained untested. This study applied airborne LiDAR data for the classification of cool temperate rainforest dominated by Myrtle Beech (Nothofagus cunninghamii) and adjacent forests including naturally regenerated Mountain Ash (Eucalyptus regnans), mixed forest consisting of overstorey Mountain Ash and understorey Myrtle Beech, Silver Wattle (Acacia dealbata), and hardwood plantation dominated by Shining Gum (Eucalyptus nitens) in the Strzelecki Ranges, Victoria, Australia. LiDAR data were extracted within each of the forest plots. Nonground laser returns were used to generate forest height profiles for the analysis of the spatial distribution of vertical forest structure for the plots dominated by different forest types. The k-means clustering algorithm was performed on each of the plots to stratify the vertical forest structure into three layers, representing the overstorey, mid-storey and lower storey of the plot-level forests. Variables were then calculated from the LiDAR data based on the three-layered structure for each plot. The statistical analyses, which included oneway ANOVA (analysis of variance) and the post hoc tests, identified effective variables for forest type classifications. Linear discriminant analysis with cross-validation was carried out to classify the forest types and assess the classification accuracy using error matrixes. This study demonstrated the applicability of airborne LiDAR for the classification of the Australian cool temperate rainforest and adjacent forests in the study area.
The Australian landscape, as in many countries, has undergone a significant change. The extent of... more The Australian landscape, as in many countries, has undergone a significant change. The extent of native forests in Australia has steadily decreased over time since European settlement. The establishment of towns and cities, mining and a range of other factors have all reduced forest cover, however it is land clearing for agriculture that has been the most significant process by far. Along with the southern uplands of the Otways, the Central Highlands, and East Gippsland, the Strzelecki Ranges are recognised as one of the four major Victorian areas of cool temperate rainforest. Cool temperate rainforests, although now very restricted in their distribution, are of major historical and ecological significance. They are the remanets of the oldest extant vegetation formation in Australia and are categorised as an endangered Ecological Vegetation Class within Victoria. Areas bordering cool temperate rainforest in the Eastern Strzeleckis are a mosaic of different land use histories formatted by both natural and human disturbances. Different land use patterns have different influences on imbedded remnant patches of cool temperate rainforest mainly through edge effects. This study aims to model the long term land use and land cover changes (from 1939 to 2004) and their impacts on cool temperate rainforest in the Strzelecki Ranges by integrating remote sensing and geographical information system (GIS). The reconstructed history of land use and land cover is mainly based on historical aerial photography with the support of Vicmap Elevation, Ecological Vegetation Classes (EVCs) map. The general trend of land use and land cover change, including rainforest in study areas was analysed.
Proceedings of the 6th International Conference on Telecommunications and Remote Sensing, 2017
Area, 2011
The traditional methods of forest classification, based on the interpretation of aerial photograp... more The traditional methods of forest classification, based on the interpretation of aerial photographs and processing of multi-spectral and/or hyper-spectral remote sensing data are limited in their ability to capture the structural complexity of the forests compared with analysis of airborne LiDAR (light detection and ranging) data. This is because of LiDAR's penetration of forest canopies such that detailed and three-dimensional forest structure descriptions can be derived. This study applied airborne LiDAR data for the classification of cool temperate rainforest and adjacent forests in the Strzelecki Ranges, Victoria, Australia. Using normalised LiDAR point data, the forest vertical structure was stratified into three layers. Variables characterising the height distribution and density of forest components were derived from LiDAR data within each of these layers. The statistical analyses, which included one-way analysis of variance with post hoc tests, identified effective variables for forest-type classifications. The results showed that using linear discriminant analysis, an overall classification accuracy of 91.4% (as verified by the cross-validation) was achieved in the study area.
… of the Photogrammetry, Remote Sensing and …, 2008
Airborne Light Detection and Ranging (LiDAR) - also referred to as Airborne Laser Scanning (ALS),... more Airborne Light Detection and Ranging (LiDAR) - also referred to as Airborne Laser Scanning (ALS), provides means for high density and high accuracy topographic data acquisition. LiDAR data have become a major source of digital terrain data and have been used in a wide of ...
Although remotely sensed data have been widely explored for forest applications, passive remote s... more Although remotely sensed data have been widely explored for forest applications, passive remote sensing techniques are limited in their ability to capture forest structural complexity, particularly in uneven-aged, mixed species forests with multiple canopy layers. Generally, these techniques are only able to provide information on horizontal (two-dimensional) forest extent. The vertical forest structure (or the interior of the canopy and understorey vegetation) cannot be mapped using these passive remote sensing techniques. Fortunately, it has been shown that active remote sensing techniques via airborne LiDAR (light detection and ranging) with capability of canopy penetration yields such high density sampling that detailed description of the forest structure in three-dimensions can be obtained. Accordingly, much interest is attached to exploring the application of this approach for identifying the distribution of designated vegetation communities. However, the suitability of LiDAR ...
Lecture Notes in Geoinformation and Cartography, 2013
It has been shown that new remote sensing technologies have the potential to complement deficienc... more It has been shown that new remote sensing technologies have the potential to complement deficiencies of conventional methods such as aerial photograph interpretation and field sampling as well as improve the accuracy, reduce costs, and increase the number of applications within various forest environments. Newly available high resolution spatial data such as small footprint, multiple-return, discrete airborne LiDAR data and WorldView-2 satellite imagery offer excellent opportunities to develop new and efficient ways of solving conventional problems in forestry. However, the development of a comprehensive procedure for deployment of these new remote sensing data to create forest mapping products that are comparable and/or superior in accuracy to conventional photo-interpreted maps poses big challenges. Proper use of high spatial resolution data with object-based image analysis approach and nonparametric classification method such as decision trees may offer an alternative to aerial photograph interpretation in support of forest classification and mapping. This study presented ways of processing airborne LiDAR data and WorldView-2 satellite imagery for object-based forest species classification using decision trees in the Strzelecki Ranges, one of the four major Victorian areas of cool temperate rainforest in Australia. The results showed the contribution of four new WorldView-2 image bands to forest classifications, and demonstrated that the integration of airborne LiDAR and eight WorldView-2 bands significantly improved the classification accuracy.
It has been shown that new remote sensing technologies have the potential to complement deficienc... more It has been shown that new remote sensing technologies have the potential to complement deficiencies of conventional methods such as aerial photograph interpretation and field sampling as well as improve the accuracy, reduce costs, and increase the number of applications within various forest environments. Newly available high resolution spatial data such as small footprint, multiple-return, discrete airborne LiDAR data and WorldView-2 satellite imagery offer excellent opportunities to develop new and efficient ways of solving conventional problems in forestry. However, the development of a comprehensive procedure for deployment of these new remote sensing data to create forest mapping products that are comparable and/or superior in accuracy to conventional photo-interpreted maps poses big challenges. Proper use of high spatial resolution data with object-based image analysis approach and nonparametric classification method such as decision trees may offer an alternative to aerial photograph interpretation in support of forest classification and mapping. This study presented ways of processing airborne LiDAR data and WorldView-2 satellite imagery for object-based forest species classification using decision trees in the Strzelecki Ranges, one of the four major Victorian areas of cool temperate rainforest in Australia. The results showed the contribution of four new WorldView-2 image bands to forest classifications, and demonstrated that the integration of airborne LiDAR and eight WorldView-2 bands significantly improved the classification accuracy.
gis.vsb.cz
It has been recognised that airborne LiDAR (light detection and ranging) offers advantages over t... more It has been recognised that airborne LiDAR (light detection and ranging) offers advantages over the interpretation of aerial photographs and processing of multi-spectral and/or hyper-spectral remote sensing data in forest classification. LiDAR with capability of ...
… of the 2009 Surveying and Spatial …, 2009
Airborne light detection and ranging (LiDAR) is one of the most effective means for high quality ... more Airborne light detection and ranging (LiDAR) is one of the most effective means for high quality terrain data acquisition. The high-accuracy and high-density LiDAR data makes it possible to model terrain surface in more detail. Using LiDAR data for DEM generation is becoming a ...
Chan, F., Marinova, D. and Anderssen, R.S. (eds) MODSIM2011, 19th International Congress on Modelling and Simulation., 2011
Although remotely sensed data have been widely explored for forest applications, passive remote s... more Although remotely sensed data have been widely explored for forest applications, passive remote sensing techniques are limited in their ability to capture forest structural complexity, particularly in uneven-aged, mixed species forests with multiple canopy layers. Generally, these techniques are only able to provide information on horizontal (two-dimensional) forest extent. The vertical forest structure (or the interior of the canopy and understorey vegetation) cannot be mapped using these passive remote sensing techniques. Fortunately, it has been shown that active remote sensing techniques via airborne LiDAR (light detection and ranging) with capability of canopy penetration yields such high density sampling that detailed description of the forest structure in three-dimensions can be obtained. Accordingly, much interest is attached to exploring the application of this approach for identifying the distribution of designated vegetation communities. However, the suitability of LiDAR data for the classification of forests with complex structures, particularly for cool temperate rainforest and neighbouring uneven-aged mixed forests in a severely disturbed landscape has hitherto remained untested. This study applied airborne LiDAR data for the classification of cool temperate rainforest dominated by Myrtle Beech (Nothofagus cunninghamii) and adjacent forests including naturally regenerated Mountain Ash (Eucalyptus regnans), mixed forest consisting of overstorey Mountain Ash and understorey Myrtle Beech, Silver Wattle (Acacia dealbata), and hardwood plantation dominated by Shining Gum (Eucalyptus nitens) in the Strzelecki Ranges, Victoria, Australia. LiDAR data were extracted within each of the forest plots. Nonground laser returns were used to generate forest height profiles for the analysis of the spatial distribution of vertical forest structure for the plots dominated by different forest types. The k-means clustering algorithm was performed on each of the plots to stratify the vertical forest structure into three layers, representing the overstorey, mid-storey and lower storey of the plot-level forests. Variables were then calculated from the LiDAR data based on the three-layered structure for each plot. The statistical analyses, which included oneway ANOVA (analysis of variance) and the post hoc tests, identified effective variables for forest type classifications. Linear discriminant analysis with cross-validation was carried out to classify the forest types and assess the classification accuracy using error matrixes. This study demonstrated the applicability of airborne LiDAR for the classification of the Australian cool temperate rainforest and adjacent forests in the study area.
The Australian landscape, as in many countries, has undergone a significant change. The extent of... more The Australian landscape, as in many countries, has undergone a significant change. The extent of native forests in Australia has steadily decreased over time since European settlement. The establishment of towns and cities, mining and a range of other factors have all reduced forest cover, however it is land clearing for agriculture that has been the most significant process by far. Along with the southern uplands of the Otways, the Central Highlands, and East Gippsland, the Strzelecki Ranges are recognised as one of the four major Victorian areas of cool temperate rainforest. Cool temperate rainforests, although now very restricted in their distribution, are of major historical and ecological significance. They are the remanets of the oldest extant vegetation formation in Australia and are categorised as an endangered Ecological Vegetation Class within Victoria. Areas bordering cool temperate rainforest in the Eastern Strzeleckis are a mosaic of different land use histories formatted by both natural and human disturbances. Different land use patterns have different influences on imbedded remnant patches of cool temperate rainforest mainly through edge effects. This study aims to model the long term land use and land cover changes (from 1939 to 2004) and their impacts on cool temperate rainforest in the Strzelecki Ranges by integrating remote sensing and geographical information system (GIS). The reconstructed history of land use and land cover is mainly based on historical aerial photography with the support of Vicmap Elevation, Ecological Vegetation Classes (EVCs) map. The general trend of land use and land cover change, including rainforest in study areas was analysed.
Proceedings of the 6th International Conference on Telecommunications and Remote Sensing, 2017
Area, 2011
The traditional methods of forest classification, based on the interpretation of aerial photograp... more The traditional methods of forest classification, based on the interpretation of aerial photographs and processing of multi-spectral and/or hyper-spectral remote sensing data are limited in their ability to capture the structural complexity of the forests compared with analysis of airborne LiDAR (light detection and ranging) data. This is because of LiDAR's penetration of forest canopies such that detailed and three-dimensional forest structure descriptions can be derived. This study applied airborne LiDAR data for the classification of cool temperate rainforest and adjacent forests in the Strzelecki Ranges, Victoria, Australia. Using normalised LiDAR point data, the forest vertical structure was stratified into three layers. Variables characterising the height distribution and density of forest components were derived from LiDAR data within each of these layers. The statistical analyses, which included one-way analysis of variance with post hoc tests, identified effective variables for forest-type classifications. The results showed that using linear discriminant analysis, an overall classification accuracy of 91.4% (as verified by the cross-validation) was achieved in the study area.