Next Article in Journal
Automatic Geometric Processing for Very High Resolution Optical Satellite Data Based on Vector Roads and Orthophotos
Previous Article in Journal
Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2016, 8(4), 339; doi:10.3390/rs8040339

Estimating Stand Volume and Above-Ground Biomass of Urban Forests Using LiDAR

1
Department of Scienze Agro-Ambientali e Territoriali, University of Bari Aldo Moro, Via Amendola 165/A 70126 Bari, Italy
2
Center for Global Change and Earth Observations (CGCEO), Michigan State University, East Lansing, MI 48823, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Nicolas Baghdadi and Prasad S. Thenkabail
Received: 4 March 2016 / Revised: 6 April 2016 / Accepted: 14 April 2016 / Published: 19 April 2016
View Full-Text   |   Download PDF [4025 KB, uploaded 19 April 2016]   |  

Abstract

Assessing forest stand conditions in urban and peri-urban areas is essential to support ecosystem service planning and management, as most of the ecosystem services provided are a consequence of forest stand characteristics. However, collecting data for assessing forest stand conditions is time consuming and labor intensive. A plausible approach for addressing this issue is to establish a relationship between in situ measurements of stand characteristics and data from airborne laser scanning (LiDAR). In this study we assessed forest stand volume and above-ground biomass (AGB) in a broadleaved urban forest, using a combination of LiDAR-derived metrics, which takes the form of a forest allometric model. We tested various methods for extracting proxies of basal area (BA) and mean stand height (H) from the LiDAR point-cloud distribution and evaluated the performance of different models in estimating forest stand volume and AGB. The best predictors for both models were the scale parameters of the Weibull distribution of all returns (except the first) (proxy of BA) and the 95th percentile of the distribution of all first returns (proxy of H). The R2 were 0.81 (p < 0.01) for the stand volume model and 0.77 (p < 0.01) for the AGB model with a RMSE of 23.66 m3·ha−1 (23.3%) and 19.59 Mg·ha−1 (23.9%), respectively. We found that a combination of two LiDAR-derived variables (i.e., proxy of BA and proxy of H), which take the form of a forest allometric model, can be used to estimate stand volume and above-ground biomass in broadleaved urban forest areas. Our results can be compared to other studies conducted using LiDAR in broadleaved forests with similar methods. View Full-Text
Keywords: urban forest; Remote sensing; LiDAR; Stand volume; above-ground biomass; forest allometric model urban forest; Remote sensing; LiDAR; Stand volume; above-ground biomass; forest allometric model
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary material

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Giannico, V.; Lafortezza, R.; John, R.; Sanesi, G.; Pesola, L.; Chen, J. Estimating Stand Volume and Above-Ground Biomass of Urban Forests Using LiDAR. Remote Sens. 2016, 8, 339.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top