摘要
Backgrounds： There are many satellite systems acquiring environmental data on the world. Acquired global remote sensing datasets require ground reference data in order to calibrate them and assess their quality. Regarding calibration and validation of these datasets with broad geographical extents, it is essential to register zones which might be considered as Homogeneous Patches （HPs）. Such patches enable an optimal calibration of satellite data/sensors, and what is more important is an analysis of components which significantly influence electro-magnetic signals registered by satellite sensors. Methods： We proposed two structurally different methods to identify HPs： predefined thresholding-based one （static one）, and statistical thresholding-based technique （dynamic one）. In the first method, 3 different thresholds were used： 5%, 10%, and 20%. Next, it was aimed to assess how delineated HPs were spatially matched to satellite data with coarse spatial resolution. Selected cell sizes were 25, 50, 100, 250, and 500 m. The number of particular grid cells which almost entirely fell into registered HPs was counted （leaving 2% cell area tolerance level）. This procedure was executed separately for each variant and selected structural variables, as well as for their intersection parts. Results： The results of this investigation revealed that ALS data might have the potential in the identification of HPs of forest stands. We showed that different ALS based variables and thresholds of HPs definition influenced areas which can be treated as similar and homogeneous. We proved that integration of more than one structural variable limits size of the HPs, in contrast, visual interpretation revealed that inside such patches vegetation structure is more constant. Conclusions： We concluded that ALS data can be used as a potential source of data to ＂enlarge＂ small ground sample plots and to be used for evaluation and calibration of remotely sensed datasets provided by global systems with coarse spatial resolutions.
Backgrounds： There are many satellite systems acquiring environmental data on the world. Acquired global remote sensing datasets require ground reference data in order to calibrate them and assess their quality. Regarding calibration and validation of these datasets with broad geographical extents, it is essential to register zones which might be considered as Homogeneous Patches （HPs）. Such patches enable an optimal calibration of satellite data/sensors, and what is more important is an analysis of components which significantly influence electro-magnetic signals registered by satellite sensors. Methods： We proposed two structurally different methods to identify HPs： predefined thresholding-based one （static one）, and statistical thresholding-based technique （dynamic one）. In the first method, 3 different thresholds were used： 5%, 10%, and 20%. Next, it was aimed to assess how delineated HPs were spatially matched to satellite data with coarse spatial resolution. Selected cell sizes were 25, 50, 100, 250, and 500 m. The number of particular grid cells which almost entirely fell into registered HPs was counted （leaving 2% cell area tolerance level）. This procedure was executed separately for each variant and selected structural variables, as well as for their intersection parts. Results： The results of this investigation revealed that ALS data might have the potential in the identification of HPs of forest stands. We showed that different ALS based variables and thresholds of HPs definition influenced areas which can be treated as similar and homogeneous. We proved that integration of more than one structural variable limits size of the HPs, in contrast, visual interpretation revealed that inside such patches vegetation structure is more constant. Conclusions： We concluded that ALS data can be used as a potential source of data to ＂enlarge＂ small ground sample plots and to be used for evaluation and calibration of remotely sensed datasets provided by global systems with coarse spatial resolutions.
基金
supported by the project REMBIOFOR(Remote sensing based assessment of woody biomass and carbon storage in forests)
supported by The National Centre for Research and Development under BIOSTRATEG program,agreement no.BIOSTRATEG1/267755/4/NCBR/2015
invented under the DUE GLOBBIOMASS project(contract 4,000,113,100/14/l-NB)
