The use of dual-wavelength airborne laser scanning for estimating tree species composition and species-specific stem volumes in a boreal forest

The estimation of species composition and species-specific stem volumes are critical components of many forest inventories. The use of airborne laser scanning with multiple spectral channels may prove instrumental for the cost-efficient retrieval of these forest variables. In this study, we scanned a boreal forest using two channels: 532 nm (green) and 1064 nm (near infrared). The data was used in a two-step methodology to (1) classify species, and (2) estimate species-specific stem volume at the level of individual tree crowns. The classification of pines, spruces and broadleaves involved linear discriminant analysis (LDA) and resulted in an overall accuracy of 91.1 % at the level of individual trees. For the estimation of stem volume, we employed species-specific k-nearest neighbors models and evaluated the performance at the plot level for 256 field plots located in central Sweden. This resulted in root-mean-square errors (RMSE) of 36 m3/ha (16 %) for total volume, 40 m3/ha (27 %) for pine volume, 32 m3/ha (48 %) for spruce volume, and 13 m3/ha (87 %) for broadleaf volume. We also simulated the use of a monospectral near infrared (NIR) scanner by excluding features based on the green channel. This resulted in lower overall accuracy for the species classification (86.8 %) and an RMSE of 41 m3/ha (18 %) for the estimation of total stem volume. The largest difference when only the NIR channel was used was the difficulty to accurately identify broadleaves and estimate broadleaf stem volume. When excluding the green channel, RMSE for broadleaved volume increased from 13 to 26 m3/ha. The study thus demonstrates the added benefit of the green channel for the estimation of both species composition and species-specific stem volumes. In addition, we investigated how tree height influences the results where shorter trees were found to be more difficult to classify correctly

Classification of tree species classes in a hemi-boreal forest from multispectral airborne laser scanning data using a mini raster cell method

Classification of tree species or species classes is still a challenge for remote sensing-based forest inventory. Operational use of Airborne Laser Scanning (ALS) data for prediction of forest variables has this far been dominated by area-based methods where laser scanning data have been used for estimation of forest variables within raster cells. Classification of tree species has however not been achieved with sufficient accuracy with area-based methods using only ALS data. Furthermore, analysis of tree species at the level of raster cells with typical size of 15 m ? 15 m is not ideal in the case of mixed species stands. Most ALS systems for terrestrial mapping use only one wavelength of light. New multispectral ALS systems for terrestrial mapping have recently become operational, such as the Optech Titan system with wavelengths 1550 nm, 1064 nm, and 532 nm. This study presents an alternative type of area-based method for classification of tree species classes where multispectral ALS data are used in combination with small raster cells. In this ?mini raster cell method? features for classification are derived from the intensity of the different wavelengths in small raster cells using a moving window average approach to allow for a heterogeneous tree species composition. The most common tree species in the Nordic countries are Pinus sylvestris and Picea abies, constituting about 80% of the growing stock volume. The remaining 20% consists of several deciduous species, mainly Betula pendula and Betula pubescens, and often grow in mixed forest stands. Classification was done for pine (Pinus sylvestris), spruce (Picea abies), deciduous species and mixed species in middle-aged and mature stands in a study area located in hemi-boreal forest in the southwest of Sweden (N 58?27?, E 13?39?). The results were validated at plot level with the tree species composition defined as proportion of basal area of the tree species classes. The mini raster cell classification method was slightly more accurate (75% overall accuracy) than classification with a plot level area-based method (68% overall accuracy). The explanation is most likely that the mini raster cell method is successful at classifying homogenous patches of tree species classes within a field plot, while classification based on plot level analysis requires one or several heterogeneous classes of mixed species forest. The mini raster cell method also results in a high-resolution tree species map. The small raster cells can be aggregated to estimate tree species composition for arbitrary areas, for example forest stands or area units corresponding to field plots

Multispectral terrestrial lidar : State of the Art and Challenges

The development of multispectral terrestrial laser scan-ning (TLS) is still at the very beginning, with only four instruments worldwide providing simultaneous three-dimensional (3D) point cloud and spectral measurement. Research on multiwavelength laser returns has been carried out by more groups, but there are still only about ten research instruments published and no commercial availability. This chapter summarizes the experiences from all these studies to provide an overview of the state of the art and future developments needed to bring the multispectral TLS technology into the next level. Alt-hough the current number of applications is sparse, they already show that multispectral lidar technology has po-tential to disrupt many fields of science and industry due to its robustness and the level of detail available

Exploring Data Mining Techniques for Tree Species Classification Using Co-Registered LiDAR and Hyperspectral Data

NASA Goddard’s LiDAR, Hyperspectral, and Thermal imager provides co-registered remote sensing data on experimental forests. Data mining methods were used to achieve a final tree species classification accuracy of 68% using a combined LiDAR and hyperspectral dataset, and show promise for addressing deforestation and carbon sequestration on a species-specific level

Utilization of bistatic TanDEM-X data to derive land cover information

Forests have significance as carbon sink in climate change. Therefore, it is of high importance to track land use changes as well as to estimate the state as carbon sink. This is useful for sustainable forest management, land use planning, carbon modelling, and support to implement international initiatives like REDD+ (Reducing Emissions from Deforestation and Degradation). A combination of field measurements and remote sensing seems most suitable to monitor forests. Radar sensors are considered as high potential due to the weather and daytime independence. TanDEM-X is a interferometric SAR (synthetic aperture radar) mission in space and can be used for land use monitoring as well as estimation of biophysical parameters. TanDEM-X is a X-band system resulting in low penetration depth into the forest canopy. Interferometric information can be useful, whereas the low penetration can be considered as an advantage. The interferometric height is assumable as canopy height, which is correlated with forest biomass. Furthermore, the interferometric coherence is mainly governed by volume decorrelation, whereas temporal decorrelation is minimized. This information can be valuable for quantitative estimations and land use monitoring. The interferometric coherence improved results in comparison to land use classifications without coherence of about 10% (75% vs. 85%). Especially the differentiation between forest classes profited from coherence. The coherence correlated with aboveground biomass in a R² of about 0.5 and resulted in a root mean square error (RSME) of 14%. The interferometric height achieved an even higher correlation with the biomass (R²=0.68) resulting in cross-validated RMSE of 7.5%. These results indicated that TanDEM-X can be considered as valuable and consistent data source for forest monitoring. Especially interferometric information seemed suitable for biomass estimation

Geospatial Computer Vision Based on Multi-Modal Data—How Valuable Is Shape Information for the Extraction of Semantic Information?

In this paper, we investigate the value of different modalities and their combination for the analysis of geospatial data of low spatial resolution. For this purpose, we present a framework that allows for the enrichment of geospatial data with additional semantics based on given color information, hyperspectral information, and shape information. While the different types of information are used to define a variety of features, classification based on these features is performed using a random forest classifier. To draw conclusions about the relevance of different modalities and their combination for scene analysis, we present and discuss results which have been achieved with our framework on the MUUFL Gulfport Hyperspectral and LiDAR Airborne Data Set

Vegetation and Tree Species Classification Using Multidate and High-resolution Satellite Imagery and Lidar Data

Remote sensing can play a key role in understanding the makeup of urban forests. This thesis analyzes how high-resolution multispectral imagery, lidar point clouds, and multidate multispectral imagery allow for improved classification of London, Ontario’s urban forest. Chapter 2 uses object-based support vector machine classification (SVM) to classify five types of trees using features derived from Geoeye-1 imagery and lidar data. This results in an overall accuracy of 85.08% when features from both data sources are combined, compared with 77.73% when using only lidar features, and 71.85% when using only imagery features. Chapter 3 makes use of Planetscope and VENuS images from different seasons to classify deciduous trees, conifers, non-tree vegetation, and non-vegetation using SVM. Using multidate Planetscope images increases overall accuracy to 83.11% (8.19 percentage points more than single-date Planetscope classification), while using multidate VENuS images increases accuracy to 72.18% (2.22 percentage points higher than single-date VENuS classification)

Terrain classification using multi-wavelength LiDAR data

With the arrival of Optech’s Titan multispectral LiDAR sensor, it is now possible to simultaneously collect three different wavelengths of LiDAR data. Much of the work performed on multispectral LiDAR data involves gridding the point cloud to create Digital Elevation Models and multispectral image cubes. Gridding and raster analysis can have negative implications with respect to LiDAR data integrity and resolution. Presented here is a method of attributing the Titan LiDAR point cloud with the spectral information of all three lasers and the potential improvement of performing all analysis within the point cloud. Data from the Optech Titan are analyzed for purposes of terrain classification, adding the spectral component to the LiDAR data point cloud analysis. The approach used here combines the three spectral sensors into one point cloud, integrating the intensity information from the 3 sensors. Nearest-neighbor sorting techniques are used to create the merged point cloud. Standard LiDAR and spectral classification techniques are then applied. The ENVI spectral tool n-Dimensional Visualizer is used to extract spectral classes from the data, which can then be applied using supervised classification functions. The Maximum Likelihood classifier provided consistent results demonstrating effective terrain classification for as many as eleven classes.http://archive.org/details/terrainclassific1094547337Lieutenant, United States NavyApproved for public release; distribution is unlimited