ASSESSMENT OF VOLUME AND ABOVE-GROUND BIOMASS IN ARAUCARIA FOREST THROUGH SATELLITE IMAGES, COMPARING DIFFERENT METHODS IN THE SOUTH OF CHILE

Abstract. Initial results of biomass estimation in the La Fusta area from existing equations found in literature are presented. As expected, accuracy of general equations suffer from the equation coefficients being obtained from fitting training data from different sites. It is also clear from the results that there is a high variance between different methods, in particular when complex data mixture is applied. Biomass is difficult to assess for dense forests, as pixels are saturated. This must be considered when planning field-data collection, with more samples in dense forest to provide more robust estimators from the training phase. The SAR-only (PALSAR) method from eq. 4 provided the most bias in results, overestimating with respect to the other methods

KERNEL FEATURE CROSS-CORRELATION FOR UNSUPERVISED QUANTIFICATION OF DAMAGE FROM WINDTHROW IN FORESTS

In this study estimation of tree damage from a windthrow event using feature detection on RGB high resolution imagery is assessed. An accurate quantitative assessment of the damage in terms of volume is important and can be done by ground sampling, which is notably expensive and time-consuming, or by manual interpretation and analyses of aerial images. This latter manual method also requires an expert operator investing time to manually detect damaged trees and apply relation functions between measures and volume which are also error-prone. In the proposed method RGB images with 0.2 m ground sample distance are analysed using an adaptive template matching method. Ten images corresponding to ten separate study areas are tested. A 13 7 13 pixels kernel with a simplified lin ear-feature representation of a cylinder is applied at different rotation angles (from 0\ub0 to 170\ub0 at 10\ub0 steps). The higher values of the normalized cross-correlation (NCC) of all angles are recorded for each pixel for each image. Several features are tested: percentiles (75, 80, 85, 90, 95, 99, max) and sum and number of pixels with NCC above 0.55. Three regression methods are tested, multiple regression (mr), support vector machines (SVM) with linear kernel and random forests. The first two methods gave the best results. The ground-truth was acquired by ground sampling, and total volumes of damaged trees are estimated for each of the 10 areas. Damaged volumes in the ten areas range from 3c1.8 7 102 m3 to 3c1.2 7 104 m3. Regression results show that smv regression method over the sum gives an R-squared of 0.92, a mean of absolute errors (MAE) of 255 m3 and a relative absolute error (RAE) of 34% using leave-one-out cross validation from the 10 observations. These initial results are encouraging and support further investigations on more finely tuned kernel template metrics to define an unsupervised image analysis process to automatically assess forest damage from windthrow

ASSESSMENT OF CANOPY AND GROUND HEIGHT ACCURACY FROM GEDI LIDAR OVER STEEP MOUNTAIN AREAS

none3noKutchartt, E.; Pedron, M.; Pirotti, F.Kutchartt, E.; Pedron, M.; Pirotti, F

INFORSAT: AN ONLINE SENTINEL-2 MULTI-TEMPORAL ANALYSIS TOOL SET USING R CRAN

Remote sensing via orbiting satellite sensors is today a common tool to monitor numerous aspects related to the Earth surface and the atmosphere. The amount of data from imagery have increased tremendously since the past years, due to the increase in space missions and public and private agencies involved in this activity. A lot of these data are open-data, and academics and stakeholders in general can freely download and use it for any type of application. The bottle-neck is often not data availability anymore, but the processing resources and tools to analyse it. In particular multi-temporal analysis requires stacks of images thus digital space for storage and processing workflows that are tested and validated. Processing image by image is often not a viable approach anymore. Basic tools for multi-temporal analysis are provided via the same web interface, allowing the user to also apply parallel processing for a faster data extraction. A study case over burned areas in the north-eastern region of Italy are reported, to show how the multi-temporal analysis tools provided can be a valid source of data for further analysis. Multitemporal data consisting on the index values of each pixel inside user-defined areas can be downloaded in a spreadsheet that provides the values, the cell ids, the timestamp and the cloud and snow percentage. Also the full-resolution raster with index values that are rendered on screen can be downloaded as GeoTIFF at each specific date

DETECTING AND EVALUATING DISTURBANCE IN TEMPERATE RAINFOREST WITH SENTINEL-2, MACHINE LEARNING AND FOREST PARAMETERS

Abstract. Earth observation via remote sensing imagery provides a fast way to define alteration levels. In this work 12 stands of Araucaria-Nothofagus forests were selected in southern Chile, which represented four alteration levels: (i) None (ii) Low (iii) Medium and (iv) High. The stands were surveyed measuring 379 field plots and Google Earth Engine was used to collect a composite of Sentinel-2 images over a one-year range, from June 2019 to June 2020. The following approaches were tested: (i) aggregating the normalized difference vegetation index (NDVI) of each image and selecting the 95th and 99th percentile values of NDVI for each pixel; (ii) creating a composite imagery with best pixels over one year timeline using NDVI as weighting factor and NDVI value band itself (NDVI) – this is similar to the 99th percentile in the previous point, but with maximum values of NDVI; (iii) aggregating the composite as in the previous approach, but using the full spectral information of Sentinel-2 and then random forest machine learning for classification over alteration areas with k-fold validation with k=5. Results show that the 95th and 99th percentile of NDVI values from approach (i) do not discriminate the four classes correctly. The maximum NDVI from approach can distinguish all four classes. It must be noted through that statistical significance does not necessarily imply a strong practical significance; medium and high alterations have very similar NDVI distributions. Random forest results provided an F-score for each class higher than 80% except for the "medium alteration" class

Assessing land surface phenology in Araucaria-Nothofagus forests in Chile with Landsat 8/Sentinel-2 time series

This dataset contains the Enhanced Vegetation Index (EVI) data used in our research work about land surface phenology of Andean Araucaria-Nothofagus forests as well as the phenology information derived from it. Study area: Conguillío National Park, Chile Study period: 2016-2020 Description of datasets: conguillio.sen2.lnd8.evi.2016.2020.nc - A raster dataset (NetCDF) of EVI values (resolution 10m). EVI was calculated from Level-2 Sentinel-2 and Landsat 8 data. To ensure harmonization, the Landsat 8 data was resampled and reprojected to Sentinel-2 properties prior to the index calculation. evi_gb_beck_white.tif - A raster dataset (GeoTiff) of phenological metrics per year (resolution 10m). Metrics were derived by fitting a double logistic function (see Beck et al., 2006) to the smoothed and interpolated EVI pixel time series. Subsequently, the main phenological variables SOS (start of season) and EOS (end of season) were extracted using a 50% threshold value. The dataset itself is a result of the R package "greenbrown" and the layers are named accordingly (see https://greenbrown.r-forge.r-project.org/phenology.php). It is available as GeoTIFF and as R rasterfile. Details about the methodology and results describing this dataset can be found in the following publication: Kosczor, E., Forkel, M., Hernández, J., Kinalczyk, D., Pirotti, F. & Kutchartt, E., 2022. Assessing land surface phenology in Araucaria-Nothofagus forests in Chile with Landsat 8/Sentinel-2 time series. Int. J. Appl. Earth Obs. Geoinf. 112, 102862. https://doi.org/10.1016/j.jag.2022.10286

Basic wood density and moisture content of 14 shrub species under two different site conditions in the Chilean Mediterranean shrubland

Aim of study: The aim of this study is to provide information on species-specific basic wood density (g cm(-3)) and moisture content (%) in Mediterranean shrublands.Area of study: The study covers two sites of the sclerophyllous shrubland in central Chile, Cortaderal (34 degrees 35'S 71 degrees 29'W) and Miraflores (34 degrees 08'S 70 degrees 37'W), characterized by different climatic and topographic conditions.Materials and methods: The sampling area covers 4,000 m(2) over four plots at two sites. Shrub species were identified and size-related attributes such as height and crown size measured. A total of 322 shrubs were sampled at 0.3 m aboveground to determine basic wood density and moisture content. Species-specific differences and similarities were analyzed by multiple pairwise comparisons (post-hoc tests) and by ordination and hierarchical clustering.Main results: We found high variation across species in wood density (0.46-0.77 g cm(-3)) and moisture content (41.6-113.1%), with many significant differences among species in wood density and among sites in moisture content. Because intraspecific variability could not be explained by shrub size and pronounced differences in wood density (0.49-0.64 g cm(-3)) also occurred between species of the same genus (e.g., Baccharis linearis and Baccharis macraei), our results suggested that phylogenetic affinity may be less important than adaptation to local conditions.Research highlights: The values presented here were variable according to the type of species and environmental conditions, necessitating the determination of basic wood density (BWD) and moisture content at site - and species-specific level. The provided BWD estimates allow converting green volume to aboveground biomass in shrubland areas and are an essential source of information for estimating the carbon stocks

Assessing forest windthrow damage using single-date, post-event airborne laser scanning data

One of many possible climate change effects in temperate areas is the increase of frequency and severity of windstorms; thus, fast and cost efficient new methods are needed to evaluate wind-induced damages in forests. We present a method for assessing windstorm damages in forest landscapes based on a two-stage sampling strategy using single-date, post-event airborne laser scanning (ALS) data. ALS data are used for delineating damaged forest stands and for an initial evaluation of the volume of fallen trees. The total volume of fallen trees is then estimated using a two-stage model-assisted approach, where variables from ALS are used as auxiliary information in the difference estimator. In the first stage, a sample of the delineated forest stands is selected, and in the second stage the within-stand damages are estimated by means of line intercept sampling (LIS). The proposed method produces maps of windthrown areas, estimates of forest damages in terms of the total volume of fallen trees, and the uncertainty of the estimates. A case study is presented for a large windstorm that struck the Tuscany Region of Italy the night of the 4th and the 5th of March 2015 and caused extensive damages to trees in both forest and urban areas. The pure field-based estimates from LIS and the ALS-based estimates of stand-level fallen wood were very similar. Our positive results demonstrate the utility of the single-date approach for a fast assessment of windthrow damages in forest stands which is especially useful when pre-event ALS data are not available