Towards a Generalized Method for Tree Species Classification Using Multispectral Airborne Laser Scanning in Ontario, Canada

Rana, Parvez; Prieur, Jean-François; Budei, Brindusa Cristina; St-Onge, Benoît

doi:10.1109/igarss.2018.8517991

Cited by 3 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The scanning configuration of LiDAR survey can be summarized as follows: pulse repetition frequency = 375kHz, scan frequency = 40 Hz, scan angle = ±20 • , and flying height ≈ 1, 100 m. As a result, the mean point density of channel 1 to 3 is ,respectively, 11.9 points/m 2 , 12.4 points/m 2 , and 4.8 points/m 2 , yielding to an approximate 0.5 m of mean point spacing. Although a total of 33 LiDAR data strips were intentionally collected to study forest attribute modelling (van Ewijk et al, 2019) and tree species classification (Rana et al, 2018), we selected two pairs of LiDAR data strips, with an approximate 55% of overlapping in each, to study the intensity variation before and after implementing the proposed range normalization.…”

Section: Multispectral Lidar Datamentioning

confidence: 99%

Estimation of Optimal Parameter for Range Normalization of Multispectral Airborne Lidar Intensity Data

Kwan

Yan

2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. Range normalization is a common data pre-process that aims to improve the radiometric quality of airborne LiDAR data. This radiometric treatment considers the rate of energy attenuation sustained by the laser pulse as it travels through a medium back and forth from the LiDAR system to the surveyed object. As a result, the range normalized intensity is proportional to the range to the power of a factor a. Existing literature recommended different a values on different land cover types, which are commonly adopted in forestry studies. Nevertheless, there is a lack of study evaluating the range normalization on multispectral airborne LiDAR intensity data. In this paper, we propose an overlap-driven approach that is able to estimate the optimal a value by pairing up the closest data points of two overlapping LiDAR data strips, and subsequently estimating the range normalization parameter a based on a least-squares adjustment. We implemented the proposed method on a set of multispectral airborne LiDAR data collected by a Optech Titan, and assessed the coefficient of variation of four land cover types before and after applying the proposed range normalization. The results showed that the proposed method was able to estimate the optimal a value, yielding the lowest cv, as verified by a cross validation approach. Nevertheless, the estimated a value is never identical for the four land cover classes and the three laser wavelengths. Therefore, it is not recommended to label a specific a value for the range normalization of airborne LiDAR intensity data within a specific land cover type. Instead, the range normalization parameter is deemed to be data-driven and should be estimated for each LiDAR dataset and study area.

show abstract

Section: Multispectral Lidar Datamentioning

confidence: 99%

Estimation of Optimal Parameter for Range Normalization of Multispectral Airborne Lidar Intensity Data

Kwan

Yan

2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…Forest segmentation can be done through two different techniques: (1) point cloud-based and (2) raster-based, using the canopy height model (CHM) [8,15,16]. The first technique generally gives good results, but it is time-consuming, complex and requires advanced LiDAR sensors [17]. The second technique has been studied much more, both at the stand level [18,19] and at the tree level [20][21][22], as there are a variety of algorithms that provide rapid ITC segmentation, which gives satisfactory results [14,16,23].…”

Section: Introductionmentioning

confidence: 99%

Object-Based Approach Using Very High Spatial Resolution 16-Band WorldView-3 and LiDAR Data for Tree Species Classification in a Broadleaf Forest in Quebec, Canada

2020

View full text Add to dashboard Cite

Species identification in Quebec, Canada, is usually performed with photo-interpretation at the stand level, and often results in a lack of precision which affects forest management. Very high spatial resolution imagery, such as WorldView-3 and Light Detection and Ranging have the potential to overcome this issue. The main objective of this study is to map 11 tree species at the tree level using an object-based approach. For modeling, 240 variables were derived from WorldView-3 with pixel-based and arithmetic feature calculation techniques. A global approach (11 species) was compared to a hierarchical approach at two levels: (1) tree type (broadleaf/conifer) and (2) individual broadleaf (five) and conifer (six) species. Five different model techniques were compared: support vector machine, classification and regression tree, random forest (RF), k-nearest neighbors, and linear discriminant analysis. Each model was assessed using 16-band or first 8-band derived variables, with the results indicating higher precision for the RF technique. Higher accuracies were found using 16-band instead of 8-band derived variables for the global approach (overall accuracy (OA): 75% vs. 71%, Kappa index of agreement (KIA): 0.72 vs. 0.67) and tree type level (OA: 99% vs. 97%, KIA: 0.97 vs. 0.95). For broadleaf individual species, higher accuracy was found using first 8-band derived variables (OA: 70% vs. 68%, KIA: 0.63 vs. 0.60). No distinction was found for individual conifer species (OA: 94%, KIA: 0.93). This paper demonstrates that a hierarchical classification approach gives better results for conifer species and that using an 8-band WorldView-3 instead of a 16-band is sufficient.

show abstract

Continental-scale hyperspectral tree species classification in the United States National Ecological Observatory Network

Marconi

Zou

Bohlman

et al. 2021

Preprint

View full text Add to dashboard Cite

Advances in remote sensing imagery and computer vision applications unlock the potential for developing algorithms to classify individual trees from remote sensing at unprecedented scales. However, most approaches to date focus on site-specific applications and a small number of taxonomic groups. This limitation makes it hard to evaluate whether these approaches generalize well across broader geographic areas and ecosystems. Leveraging field surveys and hyperspectral remote sensing data from the National Ecological Observatory Network (NEON), we developed a continental extent model for tree species classification that can be applied to the entire network including a wide range of US terrestrial ecosystems. We compared the performance of the generalized approach to models trained at each individual site, evaluating advantages and challenges posed by training species classifiers at the US scale. We evaluated the effect of geography, environmental, and ecological conditions on the accuracy and precision of species predictions. On average, the general model resulted in good overall classification accuracy (micro-F1 score), with better accuracy than site-specific classifiers (average individual tree level accuracy of 0.77 for the general model and 0.72 for site-specific models). Aggregating species to the genus-level increased accuracy to 0.83. Regions with more species exhibited lower classification accuracy. Trees were more likely to be confused with congeneric and co-occurring species and confusion was highest for trees with structural damage and in complex closed-canopy forests. The model produced accurate estimates of uncertainty, correctly identifying trees where confusion was likely. Using only data from NEON this single integrated classifier can make predictions for 20% of all tree species found in forest ecosystems across the US, suggesting the potential for broad scale general models for species classification from hyperspectral imaging.

show abstract

Towards a Generalized Method for Tree Species Classification Using Multispectral Airborne Laser Scanning in Ontario, Canada

Cited by 3 publications

References 6 publications

Estimation of Optimal Parameter for Range Normalization of Multispectral Airborne Lidar Intensity Data

Estimation of Optimal Parameter for Range Normalization of Multispectral Airborne Lidar Intensity Data

Object-Based Approach Using Very High Spatial Resolution 16-Band WorldView-3 and LiDAR Data for Tree Species Classification in a Broadleaf Forest in Quebec, Canada

Continental-scale hyperspectral tree species classification in the United States National Ecological Observatory Network

Contact Info

Product

Resources

About