Liana canopy cover mapped throughout a tropical forest with high-fidelity imaging spectroscopy

Increasing size and abundance of lianas relative to trees are pervasive changes in Neotropical forests that may lead to reduced forest carbon stocks. Yet the liana growth form is chronically understudied in large-scale tropical forest censuses, resulting in few data on the scale, cause, and impact of increasing lianas. Satellite and airborne remote sensing provide potential tools to map and monitor lianas at much larger spatial and rapid temporal scales than are possible with plot-based forest censuses. We combined high-resolution airborne imaging spectroscopy and a ground-based tree canopy census to investigate whether tree canopies supporting lianas could be discriminated from tree canopies with no liana coverage. Using support vector machine algorithms, we achieved accuracies of nearly 90% in discriminating the presence–absence of lianas, and low error (15.7% RMSE) when predicting liana percent canopy cover. When applied to the full image of the study site, our model had a 4.1% false-positive error rate as validated against an independent plot-level dataset of liana canopy cover. Using the derived liana cover classification map, we show that 6.1%–10.2% of the 1823 ha study site has high-to-severe (50–100%) liana canopy cover. Given that levels of liana infestation are increasing in Neotropical forests and can result in high tree mortality, the extent of high-to-severe liana canopy cover across the landscape may have broad implications for ecosystem function and forest carbon storage. The ability to accurately map landscape-scale liana infestation is crucial to quantifying their effects on forest function and uncovering the mechanisms underlying their increase

Deep learning for fusion of APEX hyperspectral and full-waveform LiDAR remote sensing data for tree species mapping

Deep learning has been widely used to fuse multi-sensor data for classification. However, current deep learning architecture for multi-sensor data fusion might not always perform better than single data source, especially for the fusion of hyperspectral and light detection and ranging (LiDAR) remote sensing data for tree species mapping in complex, closed forest canopies. In this paper, we propose a new deep fusion framework to integrate the complementary information from hyperspectral and LiDAR data for tree species mapping. We also investigate the fusion of either “single-band” or multi-band (i.e., full-waveform) LiDAR with hyperspectral data for tree species mapping. Additionally, we provide a solution to estimate the crown size of tree species by the fusion of multi-sensor data. Experimental results on fusing real APEX hyperspectral and LiDAR data demonstrate the effectiveness of the proposed deep fusion framework. Compared to using only single data source or current deep fusion architecture, our proposed method yields improvements in overall and average classification accuracies ranging from 82.21% to 87.10% and 76.71% to 83.45%, respectively

Canopy structural attributes derived from AVIRIS imaging spectroscopy data in a mixed broadleaf/conifer forest

There is a well-established need within the remote sensing community for improved estimation and understanding of canopy structure and its influence on the retrieval of leaf biochemical properties. The main goal of this research was to assess the potential of optical spectral information from NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) to discriminate different canopy structural types. In the first phase, we assessed the relationships between optical metrics and canopy structural parameters obtained from LiDAR in terms of different canopy structural attributes (biomass (i.e., area under Vegetation Vertical Profile, VVPint), canopy height and vegetation complexity). Secondly, we identified and classified different âcanopy structural typesâ by integrating several structural traits using Random Forests (RF). The study area is a heterogeneous forest in Sierra National Forest in California (USA). AVIRIS optical properties were analyzed by means of several sets of variables, including single narrow band reflectance and 1st derivative, sub-pixel cover fractions, narrow-band indices, spectral absorption features, optimized normalized difference indices and Principal Component Analysis (PCA) components. Our results demonstrate that optical data contain structural information that can be retrieved. The first principal component, used as a proxy for albedo, was the most strongly correlated optical metric with vegetation complexity, and it also correlated well with biomass (VVPint) and height. In conifer forests, the shade fraction was especially correlated to vegetation complexity, while water-sensitive optical metrics had high correlations with biomass (VVPint). Single spectral band analysis results showed that correlations differ in magnitude and in direction, across the spectrum and by vegetation type and structural variable. This research illustrates the potential of AVIRIS to analyze canopy structure and to distinguish several structural types in a heterogeneous forest. Furthermore, RF using optical metrics derived from AVIRIS proved to be a powerful technique to generate maps of structural attributes. The results emphasize the importance of using the whole optical spectrum, since all spectral regions contributed to canopy structure assessment

Fusion Approaches to Individual Tree Species Classification Using Multi-Source Remotely Sensed Data

Tree species information plays essential roles in urban ecological management and sustainable development, and thus tree species classification has been an active research topic over the years. This study investigated fusion approaches deployed with Support Vector Machine (SVM) and Random Forest (RF) algorithms to incorporating multispectral imagery (MSI), a very high spatial resolution panchromatic image (PAN), and Light Detection and Ranging (LiDAR) data for five object-based tree species classification in an urban environment. The results demonstrated that 3D structural features contributed more to tree species with broad crowns, such as honey locust and Austrian pine, whereas textural features were more effective in differentiating trees in narrow crowns, such as spruce. Among all the possible classification schemes based on multi-source features in combinations, decision fusion achieved the best overall accuracies (0.86 for SVM and 0.84 for RF), slightly outperforming the feature fusion approach (0.85 for SVM and 0.83 for RF). Both fusion approaches significantly improved tree species classifications produced by MSI (0.7), PAN (0.74), and LiDAR (0.8) individually

Fusion Approaches to Individual Tree Species Classification Using Multi-Source Remotely Sensed Data

Tree species information plays essential roles in urban ecological management and sustainable development, and thus tree species classification has been an active research topic over the years. This study investigated fusion approaches deployed with Support Vector Machine (SVM) and Random Forest (RF) algorithms to incorporating multispectral imagery (MSI), a very high spatial resolution panchromatic image (PAN), and Light Detection and Ranging (LiDAR) data for five object-based tree species classification in an urban environment. The results demonstrated that 3D structural features contributed more to tree species with broad crowns, such as honey locust and Austrian pine, whereas textural features were more effective in differentiating trees in narrow crowns, such as spruce. Among all the possible classification schemes based on multi-source features in combinations, decision fusion achieved the best overall accuracies (0.86 for SVM and 0.84 for RF), slightly outperforming the feature fusion approach (0.85 for SVM and 0.83 for RF). Both fusion approaches significantly improved tree species classifications produced by MSI (0.7), PAN (0.74), and LiDAR (0.8) individually

Mapping the distribution of invasive tree species using deep one-class classification in the tropical montane landscape of Kenya

Some invasive tree species threaten biodiversity and cause irreversible damage to global ecosystems. The key to controlling and monitoring the propagation of invasive tree species is to detect their occurrence as early as possible. In this regard, one-class classification (OCC) shows potential in forest areas with abundant species richness since it only requires a few positive samples of the invasive tree species to be mapped, instead of all the species. However, the classical OCC method in remote sensing is heavily dependent on manually designed features, which have a limited ability in areas with complex species distributions. Deep learning based tree species classification methods mostly focus on multi-class classification, and there have been few studies of the deep OCC of tree species. In this paper, a deep positive and unlabeled learning based OCC framework—ITreeDet—is proposed for identifying the invasive tree species of Eucalyptus spp. (eucalyptus) and Acacia mearnsii (black wattle) in the Taita Hills of southern Kenya. In the ITreeDet framework, an absNegative risk estimator is designed to train a robust deep OCC model by fully using the massive unlabeled data. Compared with the state-of-the-art OCC methods, ITreeDet represents a great improvement in detection accuracy, and the F1-score was 0.86 and 0.70 for eucalyptus and black wattle, respectively. The study area covers 100 km2 of the Taita Hills, where, according to our findings, the total area of eucalyptus and black wattle is 1.61 km2 and 3.24 km2, respectively, which represent 6.78% and 13.65% of the area covered by trees and forest. In addition, both invasive tree species are located in the higher elevations, and the extensive spread of black wattle around the study area confirms its invasive tendency. The maps generated by the use of the proposed algorithm will help local government to develop management strategies for these two invasive species.Peer reviewe

Individual tree-based forest species diversity estimation by classification and clustering methods using UAV data

Monitoring forest species diversity is essential for biodiversity conservation and ecological management. Currently, unmanned aerial vehicle (UAV) remote sensing technology has been increasingly used in biodiversity monitoring due to its flexibility and low cost. In this study, we compared two methods for estimating forest species diversity indices, namely the spectral angle mapper (SAM) classification approach based on the established species-spectral library, and the self-adaptive Fuzzy C-Means (FCM) clustering algorithm by selected biochemical and structural features. We conducted this study in two complex subtropical forest areas, Mazongling (MZL) and Gonggashan (GGS) National Nature Forest Reserves using UAV-borne hyperspectral and LiDAR data. The results showed that the classification method performed better with higher values of R2 than the clustering algorithm for predicting both species richness (0.62 > 0.46 for MZL and 0.55 > 0.46 for GGS) and Shannon-Wiener index (0.64 > 0.58 for MZL, 0.52 > 0.47 for GGS). However, the Simpson index estimated by the classification method correlated less with the field measurements than the clustering algorithm (R2 = 0.44 and 0.83 for MZL and R2 = 0.44 and 0.62 for GGS). Our study demonstrated that the classification method could provide more accurate monitoring of forest diversity indices but requires spectral information of all dominant tree species at individual canopy scale. By comparison, the clustering method might introduce uncertainties due to the amounts of biochemical and structural inputs derived from the hyperspectral and LiDAR data, but it could acquire forest diversity patterns rapidly without distinguishing the specific tree species. Our findings underlined the advantages of UAV remote sensing for monitoring the species diversity in complex forest ecosystems and discussed the applicability of classification and clustering methods for estimating different individual tree-based species diversity indices

CHARACTERIZING FOREST STANDS USING UNMANNED AERIAL SYSTEMS (UAS) DIGITAL PHOTOGRAMMETRY: ADVANCEMENTS AND CHALLENGES IN MONITORING LOCAL SCALE FOREST COMPOSITION, STRUCTURE, AND HEALTH

Present-day forests provide a wide variety of ecosystem services to the communities that rely on them. At the same time, these environments face routine and substantial disturbances that direct the need for site-specific, timely, and accurate monitoring/management (i.e., precision forestry). Unmanned Aerial Systems (UAS or UAV) and their associated technologies offer a promising tool for conducting such precision forestry. Now, even with only natural color, uncalibrated, UAS imagery, software workflows involving Structure from Motion (SfM) (i.e., digital photogrammetry) modelling and segmentation can be used to characterize the features of individual trees or forest communities. In this research, we tested the effectiveness of UAS-SfM for mapping local scale forest composition, structure, and health. Our first study showed that digital (automated) methods for classifying forest composition that utilized UAS imagery produced a higher overall accuracy than those involving other high-spatial-resolution imagery (7.44% - 16.04%). The second study demonstrated that natural color sensors could provide a highly efficient estimate of individual tree diameter at breast height (dbh) (± 13.15 cm) as well as forest stand basal area, tree density, and stand density. In the final study, we join a growing number of researchers examining precision applications in forest health monitoring. Here, we demonstrate that UAS, equipped with both natural color and multispectral sensors, are more capable of distinguishing forest health classes than freely available high-resolution airborne imagery. For five health classes, these UAS data produced a 14.93% higher overall accuracy in comparison to the airborne imagery. Together, these three chapters present a wholistic approach to enhancing and enriching precision forest management, which remains a critical requirement for effectively managing diverse forested landscapes

Fusing Small-footprint Waveform LiDAR and Hyperspectral Data for Canopy-level Species Classification and Herbaceous Biomass Modeling in Savanna Ecosystems

The study of ecosystem structure, function, and composition has become increasingly important in order to gain a better understanding of how impacts wrought by natural disturbances, climate, and human activity can alter ecosystem services provided to a population. Research groups at Rochester Institute of Technology and Carnegie Institution for Science are focusing on characterization of savanna ecosystems and are using data from the Carnegie Airborne Observatory (CAO), which integrates advanced imaging spectroscopy and waveform light detection and ranging (wLiDAR) data. This component of the larger ecosystem project has as a goal the fusion of imaging spectroscopy and small-footprint wLiDAR data in order to improve per-species structural parameter estimation towards classication and herbaceous biomass modeling. Waveform LiDAR has proven useful for extracting high vertical resolution structural parameters, while imaging spectroscopy is a well-established tool for species classication and biochemistry assessment. We hypothesize that the two modalities provide complementary information that could improve per-species structural assessment, species classication, and herbaceous biomass modeling when compared to single modality sensing systems. We explored a statistical approach to data fusion at the feature level, which hinged on our ability to reduce structural and spectral data dimensionality to those data features best suited to assessing these complex systems. The species classification approach was based on stepwise discrimination analysis (SDA) and used feature metrics from hyperspectral imagery (HSI) combined with wLiDAR data, which could help nding correlated features, and in turn improve classiers. It was found that fusing data with the SDA did not improve classication signicantly, especially compared to the HSI classication results. The overall classication accuracies were 53% for both original and PCA-based wLiDAR variables, 73% for the original HSI variables, 71% for PCA-based HSI variables, 73% for the original fusion of wLiDAR and HSI data set, and 74% for the PCA-based fusion variables. The kappa coecients achieved with the original and PCA-based wLiDAR variable classications were 0.41 and 0.44, respectively. For both original and PCA-based HSI classications, the kappa coecients were 0.63 and 0.60, respectively and 0.62 and 0.64 for original and PCA-based fusion variable classications, respectively. These results show that HSI was more successful in grouping important information in a smaller number of variables than wLiDAR and thus inclusion of structural information did not signicantly improve the classication. As for herbaceous biomass modeling, the statistical approach used for the fusion of wLiDAR and HSI was forward selection modeling (FSM), which selects signicant independent metrics and models those to measured biomass. The results were measured in R2 and RMSE, which indicate the similar ndings. Waveform LiDAR performed the poorest with an R2 of 0.07 for original wLiDAR variables and 0.12 for PCA-based wLiDAR variables. The respective RMSE were 19.99 and 19.41. For both original and PCA-based HSI variables, the results were better with R2 of 0.32 and 0.27 and RMSE of 17.27 and 17.80, respectively. For the fusion of original and PCA-based data, the results were comparable to HSI, with R2 values of 0.35 and 0.29 and RMSE of 16.88 and 17.59, respectively. These results indicate that small scale wLiDAR may not be able to provide accurate measurement of herbaceous biomass, although other factors could have contributed to the relatively poor results, such as the senescent state of grass by April 2008, the narrow biomass range that was measured, and the low biomass values, i.e., the limited laser-target interactions. We concluded that although fusion did not result in signicant improvements over single modality approaches in those two use cases, there is a need for further investigation during peak growing season