1. Forest ecological applications of ALS ALS provide 3D information where each point has height (and intensity) value Even with low pulse density data, say, 0.7 hits per m 2 there are over 150 laser points in a square plot with radius of 9 meters In general, almost all vegetation phenomenon which have height dimension can be at least by some level be observed ALS data give excellent possibilities to study vertical forest structure, even better than to forest inventory where volume or biomass is most often considered

3. Forest structure ALS data have widely been used to examine vertical structures at tree and plot level Usually laser point cloud has been examined in 3D or just height distribution of laser hits Also leaf on/off data have been compared Forest structure closely related to diameter distributions The existence of tree layers or quantification of tree volumes?

4. Forest structure

5. Recognition of rare and “valuable” tree species Boreal forests –Aspen, Populus tremula –Common alder, Alnus glutinosa –Oak, Quercus robur High pulse density ALS data is needed Recognition of individual trees Tree species classification in basically: –Intensity –Height / density metrics –Differences in leaf on/off data –3D textural variables Tree height itself is not a good indicator!

6. Canopy gaps Small disturbance regime including appearance, enlargement, reduction and disappearance of gaps Man made gaps Studied lots of with aerial images; problems with shadows, variation in images Very hard to characterise in the field, rather easy by using 3D ALS data Size, height limit Different studies on recognition of gap type, change detection according to time series of ALS data, etc.

7. Prediction of dead wood In nature conservation areas dead wood can be predicted with meaningful precision by using ALS Approach based on canopy gap dynamics: existence of dead wood can be seen on forest structure Laser variables such as –Standard deviation of heights –Lower height percentiles –Intensity Better accuracy for downed dead wood

8. Prediction of dead wood For standing dead individual tree delineation or aerial photographs more suitable alternative? Prediction of dead wood not successful in managed stands –a lot of 0-values –amount of dead wood low –man made gaps Differences between countries and regions Mapping of hot spots?

9. Sampling methods for rare forest phenomenon Traditional sampling methods do not include information on rare phenomenon efficiently Adaptive cluster sampling Line intercept sampling Guided transect sampling Pre-information in sampling of biodiversity aspects

10. Guiding dead wood inventory by ALS data Planning of plot locations before actual measurements ALS used as auxiliary information Probability layers based on ALS variables correlations between ALS variables and ground truth value, e.g. mean height of ALS hits at plot level In the design phase in guiding the field inventory to the areas of more interest by giving interesting phenomena a larger probability to be included in the sample

11. Use of ALS as auxiliary information in sampling context of dead wood In the estimation phase ALS auxiliary information can be used for a given sample to improve the accuracy of estimators by using ratio or regression estimators Dead wood inventory as a part of stand level management inventory? Re-use of laser data Commercial forest inventories NLS DTM data

12. Site classification Site type classification Height  bonitet, age? Mapping herb-rich mature forests Differences in forest vertical structure Peatland classification Mapping of lichens Intensity Problem of scale: existing stand delineation, micro-segments, cell, …

13. Scale Scale is emphasized in ecological applications Tree level a natural unit in forestry Cell artificial, inefficient? Microstand natural unit Stand Difficult to maintain Landscape e.g. erosion modelling