Chin J Plan Ecolo ›› 2016, Vol. 40 ›› Issue (2): 102-115.doi: 10.17521/cjpe.2014.0366

• Research Articles • Previous Articles     Next Articles

Canopy closure estimation in a temperate forest using airborne LiDAR and LANDSAT ETM+ data

Rui-Ying ZHANG1,2, Yong PANG2, Zeng-Yuan LI2,*(), Yu-Hai BAO1   

  1. 1College of Geographical Science, Inner Mongolia Normal University, Hohhot 010010, China
    2Institute of Forest Resource Information Technique, Chinese Academy of Forestry, Beijing 100091, China
  • Online:2016-03-08 Published:2016-02-10
  • Contact: Zeng-Yuan LI


Aims Forest canopy closure is one of the essential factors in forest survey, and plays an important role in forest ecosystem management. It is of great significance to study how to apply LiDAR (light detection and ranging) data efficiently in remote sensing estimation of forest canopy closure. LiDAR can be used to obtain data fast and accurately and therefore be used as training and validation data to estimate forest canopy closure in large spatial scale. It can compensate for the insufficiency (e.g. labor-intensive, time-consuming) of conventional ground survey, and provide foundations to forest inventory.Methods In this study, we estimated canopy closure of a temperate forest in Genhe forest of Da Hinggan Ling area, Nei Mongol, China, using LiDAR and LANDSAT ETM+ data. Firstly, we calculated the canopy closure from ALS (Airborne Laser Scanning) high density point cloud data. Then, the estimated canopy closure from ALS data was used as training and validation data to modeling and inversion from eight vegetation indices computed from LANDSAT ETM+ data. Three approaches, multi-variable stepwise regression (MSR), random forest (RF) and Cubist, were developed and tested to estimate canopy closure from these vegetation indices, respectively.Important findings The validation results showed that the Cubist model yielded the highest accuracy compared to the other two models (determination coefficient (R2) = 0.722, root mean square error (RMSE) = 0.126, relative root mean square error (rRMSE) = 0.209, estimation accuracy (EA) = 79.883%). The combination of LiDAR data and LANDSAT ETM+ showed great potential to accurately estimate the canopy closure of the temperate forest. However, the model prediction capability needs to be further improved in order to be applied in larger spatial scale. More independent variables from other remotely sensed datasets, e.g. topographic data, texture information from high-resolution imagery, should be added into the model. These variables can help to reduce the influence of optical image, vegetation indices, terrain and shadow and so on. Moreover, the accuracy of the LiDAR-derived canopy closure needs to be further validated in future studies.

Key words: LANDSAT ETM+, airborne laser scanning (ALS), forest canopy closure, vegetation index, multi- variable stepwise regression, random forest, Cubist

Table 1

The spectrum information and radiometric calibration parameters of LANDSAT ETM+ data"

Band name
Band number
Wavelength range (µm)
Corresponding original band number
红 Red 1 0.63-0.69 3 508
近红外 Near infrared 2 0.75-0.90 4 254
短波红外1 Short wave infrared 1 3 1.55-1.75 5 363
短波红外2 Short wave infrared 2 4 2.09-2.35 7 423

Fig. 1

The flowchart of forest canopy closure inversion based on LANDSAT ETM+ image."

Fig. 2

The calculation result of canopy closure. A, Canopy height model (m). B, Canopy closure result from calculation."

Fig. 3

The image of sample selecting range (the red area is the training sample and the yellow area is the validation sample)."

Fig. 4

The segmentation results of LANDSAT ETM+ image and canopy closure. A, The segmentation result of LANDSAT ETM+ image. B, The segmentation result of canopy closure."

Fig. 5

The scatterplot of model accuracy validation. A, multi-variable stepwise regret ssion (MSR) model-the scatterplot of model accuracy validation. B, Random forest (RF) model-the scatterplot of model accuracy validation. C, Cubist model-the scatterplot of model accuracy validation. CHM, canopy height model; RMSE, root mean square error."

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