Delineation of homogeneous forest patches using combination of field measurements and LiDAR point clouds as a reliable reference for evaluation of low resolution global satellite data

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.

Influence of sampling intensity on performance of two-phase forest inventory using airborne laser scanning

Background: Forest inventories have always been a primary information source concerning the forest ecosystem state. Various applied survey approaches arise from the numerous important factors during sampling scheme planning. Paramount aspects include the survey goal and scale, target population inherent variation and patterns,and available resources. The last factor commonly inhibits the goal, and compromises have to be made. Airborne laser scanning(ALS) has been intensively tested as a cost-effective option for forest inventories. Despite existing foundations, research has provided disparate results. Environmental conditions are one of the factors greatly influencing inventory performance. Therefore, a need for site-related sampling optimization is well founded.Moreover, as stands are the basic operational unit of managed forest holdings, few related studies have presented stand-level results. As such, herein, we tested the sampling intensity influence on the performance of the ALSenhanced stand-level inventory.Results: Distributions of possible errors were plotted by comparing ALS model estimates, with reference values derived from field surveys of 3300 sample plots and more than 300 control stands located in 5 forest districts. No improvement in results was observed due to the scanning density. The variance in obtained errors stabilized in the interval of 200–300 sample plots, maintaining the bias within +/-5% and the precision above 80%. The sample plot area affected scores mostly when transitioning from 100 to 200 m2. Only a slight gain was observed when bigger plots were used.Conclusions: ALS-enhanced inventories effectively address the demand for comprehensive and detailed information on the structure of single stands over vast areas. Knowledge of the relation between the sampling intensity and accuracy of ALS estimates allows the determination of certain sampling intensity thresholds. This should be useful when matching the required sample size and accuracy with available resources. Site optimization may be necessary, as certain errors may occur due to the sampling scheme, estimator type or forest site, making these factors worth further consideration.