VARMA-project Jori Uusitalo
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1 VARMA-project Jori Uusitalo
2 Collaboration with third parties Prof. Markus Holopainen and Dr. Mikko Vastaranta, Finnish Academy Project, Centre of Excellenge in Laser Scanning Interpretation of laser-scanning data Prof. Jari Hynynen and Dr. Jouni Siipilehto, Luke, TEKES/Data-to-Intelligence Constructing tree list data based on laser scannings Dr. Harri Mäkinen, Luke Quality attributes for pine stems based on laser scanning
3 Reliability of stand structures for clear cut stands using three optional methods Accuracy of three pre-harvest inventory methods in predicting diameter-tree height distribution of boreal forest clear cut stands Area based airborne laser scanning (ABA) Smart-phone based inventory app Trestima The EMO inventory that employs conventional inventory scales (caliper, hypsometer and relascope) 3 Uusitalo, Lindeman & Ala-Ilomäki
4 Airborne Laser Scanning (ALS) Low resolution ALS data available for all eight study stands Interpretation and estimation of mean characteristics of the study stands Markus Holopainen Diameter distributions derived by models
5 ALS/ABA based dbh-distribution Each grid (16 x 16 m) interpreted separately Following mean characteristics for each tree species derived: The study stands appeared as georeferenced polygons while the results of the ALS inventory are input as 16 m x 16 m grid cells Match of each grid in relation to stand boundaries were analyzed with ArcGIS software. The proportion of the pixels within each 16 x 16 cell that locate inside the stand polygon was calculated The imperfect grid cells were weighted with this proportion when the stand attributes to each stand were summed up from the grid level predictions. Basal area (G) Stem number (N) Mean diameter (DGM) Mean height (HGM)
6 Pre-harvest measurement/emosoftware Subjectively chosen route, sampling points e.g. roughly every 50 paces Measurement of three/five nearest trees + BA counting Tree height (and crown heigt) of few sample trees EMO-software package generates tree lists
7 Trestima Smartphone based Trestima forest inventory system 10 BA photos taken per each stand and transferred to Trestima cloud service The system detects tree species and dbh within the photos The system outputs following stand characteristics Basal area (G) Stem number (N) Mean diameter (DGM) Mean height (HGM)
8 Trestima
9 Trestima Trestima
10 Derivation of tree lists based on mean stand characteristics (N, G, DG, HG) ALS grid ALS smoothed Trestima EMO software Mean stand characteristics for each grid cell Mean stand characteristics for each grid cell Mean stand characteristics for the whole stand Diameter distributions provided by EMO Diameter distributions for each grid cell Averaging mean stand chracteristics Summing of distributions for the whole stand Diameter distributions for the whole stand Diameter distributions for the whole stand Fine-tuning of diameter distributions Diameter distributions for each tree species by the parameter recovery method developed by Siipilehto (2013) 10 Uusitalo, Lindeman & Ala-Ilomäki
11 Predicting diameter distribution of stand 11 Uusitalo, J
12 Predicting diameter distribution of stand Table 8. Ranking the methods by the number of the best and the number of the worst cases among analyzed criterions: Bias and RMSE for N, G, DG, HG, V, log and pulp by tree species and stand totals, as well as Kolmogorov- Smirnov (KS) and Error-Index (EI) tests. (Total of 70 criterion) Rank best worst Criteria ALS ALS Tres. Tres. EMO ALS ALS grid smooth 5 10 kernel grid smooth Tres. 5 Tres. 10 EMO kernel Bias RMSE KS EI Total Uusitalo, J
13 13 Teppo Tutkija
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