Metsa katvuse ja liituse hindamine lennukilt laserskanneriga
Tests were carried out in mature Scots pine, Norway spruce and Silver birch stands at Järvselja, Estonia, to estimate canopy cover (K) and crown cover (L) from airborne lidar data. Independent estimates Kc and Lc for K and L were calculated from the Cajanus tube readings made on the ground at 1.3 m height. Lidar data based cover estimates depended on the inclusion of different order returns significantly. In all the stands first order return based estimate K1 was biased positively (3-10%) at the reference height of 1.3 m compared to ground measurements. All lidar based estimates decreased with increasing the reference height. Single return (Ky) and all return (Kk) based canopy cover estimates depended more on the sand structure compared to K1. The ratio of all return count to the first return count D behaved like crown cover estimate in all stands. However, in spruce stand D understimated Lc significantly. In the Scots pine stand K1(1.3) = 0.7431 was most similar canopy cover estimate relative to the ground estimate Kc = 0,7362 whereas Ky(1.3) and Kk(1.3) gave significant underestimates (>15%) of K. Caused by the simple structure of Scots pine stand - only one layer pine trees, the Cajanus tube based canopy cover (Kc), crown cover (Lc) and lidar data based canopy density D(1.3) values were rather similar. In the Norway spruce stand and in the Silver birch stand second layer and regeneration trees were present. In the Silver birch stand Kk(1.3) and Ky(1.3) estimated Kc rather well. In the Norway spruce stand Ky(1.3) and K1(1.3) were the best estimators of Kc whereas Kk(1.3) underestimated canopy cover. Lidar data were found to be usable for canopy cover and crown cover assessment but the selection of the estimator is not trivial and depends on the stand structure.
Information about the status of agricultural land is of strategic interest in every country. In Estonia, different and even contradicting estimates of area of actively used as agricultural land exist. To solve this problem, airborne laser scanning (ALS) data can be used to map the woody plant cover (values range from 0 to 1 corresponding to the no cover and full tree canopy cover) in agricultural land and to provide digital maps for further analysis. Canopy cover was estimated from ALS data by setting reference height to 2 m from ground. Validation dataset was created from ortophotos on 442 rectangular 100 m2 elementary sampling units. The relationship was linear and determination coefficient was high (R2 = 0.795) and the model fitting residual standard error was 0.116 indicating good applicability of ALS for woody plant cover mapping. The method can be easily automated, does not require additional fieldwork and can be applied in all places where ALS data are available
Thinning cuttings create moderate disturbances in forest stands. Thinning intensity indicates the amount of felled wood relative to the initial standing volume. We used sparse point clouds from airborne lidar measurements carried out in 2008 and 2012 at Aegviidu test site, Estonia, to study stand level relationships of thinning intensity to the changes in canopy cover and ALS-based wood volume estimates. Thinning intensity (Kr, HRV) was estimated from forest inventory data and harvester measurements of removed wood volume. The thinning intensity ranged from 17% to 56%. By raising threshold from 1.3 m to 8.0 m over ground surface we observed less canopy cover change, but stronger correlation with thinning intensity. Correlation between ALS-based and harvester-based thinning intensity was moderate. The ALS-based thinning intensity estimate was systematically smaller than Kr, HRV. Forest height growth compensates for a small decrease in canopy cover and intensity estimates for weak thinnings are not reliable using sparse point clouds and a four-year measurement interval.
Field measurements from 450 sample plots, airborne lidar data and spectral images from Aegviidu, Estonia, 15 by 15 km test site were used to analyse options to estimate main forest inventory variables using remote sensing data. Up to 7 m random error in location of 15 m radius sample plots within homogeneous stands causes usually about 0.5 m standard deviation in lidar pulse return height distribution percentiles. Forest mean height can be predicted with linear relationship from 80th percentile of lidar pulse return height distribution. Upper percentiles of pulse return height distribution are not significantly affected by omitting returns from ground and forest understorey vegetation. Total stem volume in forest can be predicted by using 80th percentile, 25th percentile and canopy cover as model arguments with less than 70 m3 ha-1 standard error. Best species specific stem volume models had 10 m3 ha-1 smaller standard error.
Canopy gap fraction has been estimated from hemispherical images using a thresholding method to separate sky and canopy pixels. The optimal objective thresholding rule has been searched by many authors without satisfactory results due to long list of reasons. Some recent studies have shown that unprocessed readings of camera CCD or CMOS sensor (raw data) have linear relationship with incident radiation. This allows a pair of cameras used in similar to a pair of plant canopy analyzers and canopy gap fraction can be calculated as the ratio of below canopy image and above canopy image. We tested new freeware program HemiSpherical Project Manager (HSP) for the restoration of the above canopy image from below canopy image which allows making field measurements with single below canopy operated camera. Results of perforated panel image analysis and comparison of plant area index (PAI) estimated independently by three operators from real canopy hemispherical images showed high degree of reliability of the new approach. Determination coefficients of linear regression of the PAI estimations of the three operators were 0.9962, 0.9875 and 0.9825. The canopy gap fraction data obtained from HSP were used to validate Nobis-Hunziker automatic thresholding algorithm. The results indicated that the Nobis-Hunziker algorithm underestimated PAI from out of camera JPEG images and overestimated PAI from raw data.
Hemispherical photography provides permanent records of forest canopy structure. We analysed digital hemispherical images taken during the period of 2007–2018 in a mature silver birch stand located in Järvselja, Estonia. The stand was thinned in 2004. Understory trees were removed in the spring of 2018. Images were processed using the LinearRatioSC method. Effective plant area index Leff during the leafless phenophase increased as a result of tree growth from 0.92 to 1.24 and understory cutting was not detectable. During the full foliage condition Leff increased from 3.6 in 2008 to 5.8 in 2017. After removal of understory trees from the stand Leff decreased, and repeated measurements in the summer of 2018 estimated the plant area index range 4.5 < Leff < 4.8. The results are in agreement with the expected changes following forest growth and demonstrate that LinearRatioSC is a suitable method for the estimation and long-term monitoring of forest canopy properties from digital hemispherical images.
The space-borne Moderate Resolution Imaging Spectroradiometer (MODIS) data based net primary production (NPP) product from Numerical Terradynamic Simulation Group (NTSG) was tested in the Kurzeme region, Latvia using a stand-wise forest inventory database. The NPP product has been validated globally and found to have no overall bias. In this study the NPP product was compared with stem biomass increment and soil fertility in respect to distance from the Baltic Sea coast. For each MODIS NPP product pixel we calculated forest cover, share of coniferous trees, average stem biomass increment and average site fertility (growth potential estimate). Then, 2432 pixels with a forest cover over 75% were selected for analysis. The results indicated that MODIS NPP decreased with distance from Baltic Sea coast but stem biomass increment and site fertility indicated a trend of increase. There was no functional relationship between MODIS NPP and stem biomass increment. Analysis of the landcover map used by NTSG for MODIS NPP product showed that the classes “Evergreen needleleaf” and “Mixed forests” differentiated only 10% by mode value of coniferous proportions in species composition. A non-natural jump was detected in the MODIS NPP values at a longitude of 22.5 degrees east corresponding to the border of the coarse scale meteorological dataset (NCEP Reanalysis (R2)) data representation unit. According to the results the MODIS NPP product is not applicable for regional level planning but can probably provide only rough average estimates of NPP for the Baltic region
Optical remote sensing data-based estimates of terrestrial net primary production (NPP) are released by different projects using light use efficiency-type models. Although spatial resolution of the NPP data sets is still too coarse (500–1000 m) for single forest stands, regional monitoring of forest management and growth with 25–100 ha sampling units is feasible if the NPPSAT estimates are sensitive to forest growth differences depending on soil fertility in the area of interest. In this study, NPP estimates for 2,914 mixed forest class pixels (according to the MODIS land cover map) located in Estonia were (1) obtained from three different NPPSAT products, (2) calculated using an empirical soil potential phytoproductivity (SPP) model applied to a 1:10,000 soil map (NPPSPP), and (3) calculated using stem volume increment estimates given in a forest management inventory data base (NPPFIDB). A linear multiple regression model was then used to explore the relationships of NPPSAT with the proportion of coniferous forests, the NPPSPP and distance of the pixels from the Baltic Sea coast – the variables that have been found informative in previous studies. We found a positive moderate correlation (0.57, p < 0.001) between NPPSPP and NPPFIDB. The local or downscaled meteorological data-based NPPSAT estimates were more consistent with the NPPSPP and NPPFIDB, but the correlation with NPPSAT was weak and sometimes even negative. The range of NPP estimates in NPPSAT data sets was much narrower than the range of NPPSPP or NPPFIDB. Errors in land cover maps and in estimates of absorbed photosynthetically active radiation were identified as the main reasons for NPPSAT inconsistencies.
Forest height increment rate is related to the forest growth conditions. Data bases of previous forest inventories contain information about forest heightage relationship on large number of forest stands while repeated measurements of permanent sample plots provide an excellent reference for comparison. Repeated airborne laser scanning of forest stands is an additional source for the estimation of change in forest structure. In this study, height growth of middle-aged and older forest stands for about 10 year period was compared to an algebraic difference model on permanent sample plots (66) and for a sample of forest stands with repeated airborne laser scanning data (61). The model was based on a large dataset of forest inventory records from the period of 1984–1993. Statistically significant increased forest height growth was found in permanent sample plots based on tree height measurements (9 cm yr−1) as well in stands with repeated laser scanning data (4.5 cm yr−1) in South-East Estonia compared to the algebraic difference model. The difference between the two data sets was explained by their mean age and site class, but the increased forest height growth compared to the old forest inventory data indicates improved growth conditions of forests in the test area. The results hint also that empirical data-based forest growth models need to be updated to avoid biased growth estimates.
Landsat-8 OLI and Sentinel-2 MSI images from years 2015 and 2016, a 1:10,000 digital soil map and a large number of reference samples were used with a random forest machine learning implementation in GRASS GIS to construct a tree species map for the entire territory of Estonia (42,755 km2). Class probabilities for seven main tree species, an extra class for other species and probability of the forest cover not conforming to the forest definition were assigned for each pixel. Validation of dominant species distribution by area showed very strong correlation at county level both in state forests (R2 = 0.98) and in private forests (R2 = 0.93). Validation of tree species composition using harvester measurement data from 2,045 regeneration felling areas showed also very strong correlation (R2 = 0.75) with the measured values of the proportion of coniferous trees. There was some tendency to underestimate the proportion of more common species and overestimation was found for the species with smaller proportion in the mixture. The accuracy for the proportion of deciduous species that were present in a smaller number of reference observations was substantially smaller. Validation of the results by using data from 659 large sample plots from the database of the Estonian Network of Forest Research Plots and 3,002 small sample plots from the National Forest Inventory (NFI) data base confirmed the findings based on harvester data. The NFI data revealed also a decrease of estimation error with the increase of forest age. Cohen’s kappa index of agreement for main species for NFI sample plots with main species proportion equal to or greater than 75% decreased from 0.69 to 0.66 when observations with forests younger than 20 years were included in the comparison. Overall, the constructed map provides valuable data about tree species composition for the forests where no up to date inventory data are available or for the projects that require continuous cover of tree species data of known quality over the entire Estonia.