Machine Learning Approaches for Estimating Forest Stand Height Using Plot-Based Observations and Airborne LiDAR Data

Effective sustainable forest management for broad areas needs consistent country-wide forest inventory data. A stand-level inventory is appropriate as a minimum unit for local and regional forest management. South Korea currently produces a forest type map that contains only four categorical parameters. Stand height is a crucial forest attribute for understanding forest ecosystems that is currently missing and should be included in future forest type maps. Estimation of forest stand height is challenging in South Korea because stands exist in small and irregular patches on highly rugged terrain. In this study, we proposed stand height estimation models suitable for rugged terrain with highly mixed tree species. An arithmetic mean height was used as a target variable. Plot-level height estimation models were first developed using 20 descriptive statistics from airborne Light Detection and Ranging (LiDAR) data and three machine learning approachessupport vector regression (SVR), modified regression trees (RT) and random forest (RF). Two schemes (i.e., central plot-based (Scheme 1) and stand-based (Scheme 2)) for expanding from the plot level to the stand level were then investigated. The results showed varied performance metrics (i.e., coefficient of determination, root mean square error, and mean bias) by model for forest height estimation at the plot level. There was no statistically significant difference among the three mean plot height models (i.e., SVR, RT and RF) in terms of estimated heights and bias (p-values > 0.05). The stand-level validation based on all tree measurements for three selected stands produced varied results by scheme and machine learning used. It implies that additional reference data should be used for a more thorough stand-level validation to identify statistically robust approaches in the future. Nonetheless, the research findings from this study can be used as a guide for estimating stand heights for forests in rugged terrain and with complex composition of tree species

Airborne Lidar Sampling Strategies to Enhance Forest Aboveground Biomass Estimation from Landsat Imagery

Accurately estimating aboveground biomass (AGB) is important in many applications, including monitoring carbon stocks, investigating deforestation and forest degradation, and designing sustainable forest management strategies. Although lidar provides critical three-dimensional forest structure information for estimating AGB, acquiring comprehensive lidar coverage is often cost prohibitive. This research focused on developing a lidar sampling framework to support AGB estimation from Landsat images. Two sampling strategies, systematic and classification-based, were tested and compared. The proposed strategies were implemented over a temperate forest study site in northern New York State and the processes were then validated at a similar site located in central New York State. Our results demonstrated that while the inclusion of lidar data using systematic or classification-based sampling supports AGB estimation, the systematic sampling selection method was highly dependent on site conditions and had higher accuracy variability. Of the 12 systematic sampling plans, R-2 values ranged from 0.14 to 0.41 and plot root mean square error (RMSE) ranged from 84.2 to 93.9 Mg ha(-1). The classification-based sampling outperformed 75% of the systematic sampling strategies at the primary site with R-2 of 0.26 and RMSE of 70.1 Mg ha(-1). The classification-based lidar sampling strategy was relatively easy to apply and was readily transferable to a new study site. Adopting this method at the validation site, the classification-based sampling also worked effectively, with an R-2 of 0.40 and an RMSE of 108.2 Mg ha(-1) compared to the full lidar coverage model with an R-2 of 0.58 and an RMSE of 96.0 Mg ha(-1). This study evaluated different lidar sample selection methods to identify an efficient and effective approach to reduce the volume and cost of lidar acquisitions. The forest type classification-based sampling method described in this study could facilitate cost-effective lidar data collection in future studies

Forest and Crop Leaf Area Index Estimation Using Remote Sensing: Research Trends and Future Directions

Leaf area index (LAI) is an important vegetation leaf structure parameter in forest and agricultural ecosystems. Remote sensing techniques can provide an effective alternative to field-based observation of LAI. Differences in canopy structure result in different sensor types (active or passive), platforms (terrestrial, airborne, or satellite), and models being appropriate for the LAI estimation of forest and agricultural systems. This study reviews the application of remote sensing-based approaches across different system configurations (passive, active, and multisource sensors on different collection platforms) that are used to estimate forest and crop LAI and explores uncertainty analysis in LAI estimation. A comparison of the difference in LAI estimation for forest and agricultural applications given the different structure of these ecosystems is presented, particularly as this relates to spatial scale. The ease of use of empirical models supports these as the preferred choice for forest and crop LAI estimation. However, performance variation among different empirical models for forest and crop LAI estimation limits the broad application of specific models. The development of models that facilitate the strategic incorporation of local physiology and biochemistry parameters for specific forests and crop growth stages from various temperature zones could improve the accuracy of LAI estimation models and help develop models that can be applied more broadly. In terms of scale issues, both spectral and spatial scales impact the estimation of LAI. Exploration of the quantitative relationship between scales of data from different sensors could help forest and crop managers more appropriately and effectively apply different data sources. Uncertainty coming from various sources results in reduced accuracy in estimating LAI. While Bayesian approaches have proven effective to quantify LAI estimation uncertainty based on the uncertainty of model inputs, there is still a need to quantify uncertainty from remote sensing data source, ground measurements and related environmental factors to mitigate the impacts of model uncertainty and improve LAI estimation

Arctic Sea Ice Thickness Estimation from CryoSat-2 Satellite Data Using Machine Learning-Based Lead Detection

Satellite altimeters have been used to monitor Arctic sea ice thickness since the early 2000s. In order to estimate sea ice thickness from satellite altimeter data, leads (i.e., cracks between ice floes) should first be identified for the calculation of sea ice freeboard. In this study, we proposed novel approaches for lead detection using two machine learning algorithms: decision trees and random forest. CryoSat-2 satellite data collected in March and April of 2011-2014 over the Arctic region were used to extract waveform parameters that show the characteristics of leads, ice floes and ocean, including stack standard deviation, stack skewness, stack kurtosis, pulse peakiness and backscatter sigma-0. The parameters were used to identify leads in the machine learning models. Results show that the proposed approaches, with overall accuracy >90%, produced much better performance than existing lead detection methods based on simple thresholding approaches. Sea ice thickness estimated based on the machine learning-detected leads was compared to the averaged Airborne Electromagnetic (AEM)-bird data collected over two days during the CryoSat Validation experiment (CryoVex) field campaign in April 2011. This comparison showed that the proposed machine learning methods had better performance (up to r = 0.83 and Root Mean Square Error (RMSE) = 0.29 m) compared to thickness estimation based on existing lead detection methods (RMSE = 0.86-0.93 m). Sea ice thickness based on the machine learning approaches showed a consistent decline from 2011-2013 and rebounded in 2014.open0

A Volumetric Approach to Population Estimation Using Lidar Remote Sensing

This research investigated the applicability of lidar data for estimating population at the census block level using a volumetric approach. The study area, near the urban downtown area of Denver, Colorado, was selected since it includes dense distribution of different types of residential buildings. A modified morphological building detection algorithm was proposed to extract buildings from the lidarderived surfaces. The extraction results showed that the modified morphological building detection algorithm can effectively recover building pixels occluded by nearby trees. The extracted buildings were further refined to residential buildings using parcel data. Two approaches (i.e., area- and volume-based) to population estimation were investigated at the census block level. Four regression models (i.e., simple linear regression, multiple linear regression, regression tree using one variable, and regression tree using multiple variables) were used to identify the relationship between census population and the area or volume information of the residential buildings. The volume-based models overwhelmingly outperformed the area-based models in the study area, and the models using multiple variables yielded more accurate estimation than the single variable models. The volume-based regression tree model using multiple variables yielded the most accurate estimations: R 2=0.89, RMSE=21 people, and RRMSE=26.8 percent in the calibration site; and R 2=0.80, RMSE=27 people, and RRMSE=30.1 percent in the validation site. As the results show, the volumetric approach using lidar remote sensing is effective for population estimation in regions with heterogeneous housing characteristics.close

Impact of Tree-Oriented Growth Order in Marker-Controlled Region Growing for Individual Tree Crown Delineation Using Airborne Laser Scanner (ALS) Data

Region growing is frequently applied in automated individual tree crown delineation (ITCD) studies. Researchers have developed various rules for initial seed selection and stop criteria when applying the algorithm. However, research has rarely focused on the impact of tree-oriented growth order. This study implemented a marker-controlled region growing (MCRG) algorithm that considers homogeneity, crown size, and shape using airborne laser scanning (ALS) data, and investigated the impact of three growth orders (i.e., sequential, independent, and simultaneous) on tree crown delineation. The study also investigated the benefit of combining ALS data and orthoimagery in treetop detection at both plot and individual tree levels. The results showed that complementary data from the orthoimagery reduced omission error associated with small trees in the treetop detection procedure and improved treetop detection percentage on a plot level by 2%–5% compared to ALS alone. For tree crown delineation, the growth order applied in the MCRG algorithm influenced accuracy. Simultaneous growth yielded slightly higher accuracy (about 2% improvement for producer’s and user’s accuracy) than sequential growth. Independent growth provided comparable accuracy to simultaneous growth in this study by dealing with overlapping pixels among trees according to crown shape. This study provides several recommendations for applying region growing in future ITCD research

A novel transferable individual tree crown delineation model based on Fishing Net Dragging and boundary classification

This study provides a novel approach to individual tree crown delineation (ITCD) using airborne Light Detection and Ranging (LiDAR) data in dense natural forests using two main steps: crown boundary refinement based on a proposed Fishing Net Dragging (FiND) method, and segment merging based on boundary classification. FiND starts with approximate tree crown boundaries derived using a traditional watershed method with Gaussian filtering and refines these boundaries using an algorithm that mimics how a fisherman drags a fishing net. Random forest machine learning is then used to classify boundary segments into two classes: boundaries between trees and boundaries between branches that belong to a single tree. Three groups of LiDAR-derived features-two from the pseudo waveform generated along with crown boundaries and one from a canopy height model (CHM)-were used in the classification. The proposed ITCD approach was tested using LiDAR data collected over a mountainous region in the Adirondack Park, NY, USA. Overall accuracy of boundary classification was 82.4%. Features derived from the CHM were generally more important in the classification than the features extracted from the pseudo waveform. A comprehensive accuracy assessment scheme for ITCD was also introduced by considering both area of crown overlap and crown centroids. Accuracy assessment using this new scheme shows the proposed ITCD achieved 74% and 78% as overall accuracy, respectively, for deciduous and mixed forest. © 2015 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)close0

Trends in Automatic Individual Tree Crown Detection and Delineation—Evolution of LiDAR Data

Automated individual tree crown detection and delineation (ITCD) using remotely sensed data plays an increasingly significant role in efficiently, accurately, and completely monitoring forests. This paper reviews trends in ITCD research from 1990–2015 from several perspectives—data/forest type, method applied, accuracy assessment and research objective—with a focus on studies using LiDAR data. This review shows that active sources are becoming more prominent in ITCD studies. Studies using active data—LiDAR in particular—accounted for 80% of the total increase over the entire time period, those using passive data or fusion of passive and active data comprised relatively small proportions of the total increase (8% and 12%, respectively). Additionally, ITCD research has moved from incremental adaptations of algorithms developed for passive data sources to innovative approaches that take advantage of the novel characteristics of active datasets like LiDAR. These improvements make it possible to explore more complex forest conditions (e.g., closed hardwood forests, suburban/urban forests) rather than a single forest type although most published ITCD studies still focused on closed softwood (41%) or mixed forest (22%). Approximately one-third of studies applied individual tree level (30%) assessment, with only a quarter reporting more comprehensive multi-level assessment (23%). Almost one-third of studies (32%) that concentrated on forest parameter estimation based on ITCD results had no ITCD-specific evaluation. Comparison of methods continues to be complicated by both choice of reference data and assessment metric; it is imperative to establish a standardized two-level assessment framework to evaluate and compare ITCD algorithms in order to provide specific recommendations about suitable applications of particular algorithms. However, the evolution of active remotely sensed data and novel platforms implies that automated ITCD will continue to be a promising technology and an attractive research topic for both the forestry and remote sensing communities

Synergistic use of QuickBird multispectral imagery and LIDAR data for object-based forest species classification

This study evaluated the synergistic use of high spatial resolution multispectral imagery (i.e., QuickBird, 2.4 m) and low-posting-density LIDAR data (3 m) for forest species classification using an object-based approach. The integration of QuickBird multispectral imagery and LIDAR data was considered during image segmentation and the subsequent object-based classification. Three segmentation schemes were examined: (1) segmentation based solely on the spectral image layers; (2) segmentation based solely on LIDAR-derived layers; and (3) segmentation based on both the spectral and LIDAR-derived layers. For each segmentation scheme, objects were generated at twelve different scales in order to determine optimal scale parameters. Six categories of classification metrics were generated for each object based on spectral data alone, LIDAR data alone and the combination of both data sources. Machine learning decision trees were used to build classification rule sets. Quantitative segmentation quality assessment and classification accuracy results showed the integration of spectral and LIDAR data, in both image segmentation and object-based classification, improved the forest classification compared to using either data source independently. Better segmentation quality led to higher classification accuracy. The highest classification accuracy (Kappa = 91.6%) was acquired when using both spectral- and LIDAR-derived metrics based on objects segmented from both spectral and LIDAR layers at scale parameter 250, where best segmentation quality was achieved. Optimal scales were analyzed for each segmentation-classification scheme. Statistical analysis of classification accuracies at different scales revealed that there was a range of optimal scales that provided statistically similar accuracy.close686