- Research article
- Open Access
A 3D approach to reconstruct continuous optical images using lidar and MODIS
© Huang and Lian. 2015
- Received: 28 February 2015
- Accepted: 18 June 2015
- Published: 26 June 2015
Monitoring forest health and biomass for changes over time in the global environment requires the provision of continuous satellite images. However, optical images of land surfaces are generally contaminated when clouds are present or rain occurs.
To estimate the actual reflectance of land surfaces masked by clouds and potential rain, 3D simulations by the RAPID radiative transfer model were proposed and conducted on a forest farm dominated by birch and larch in Genhe City, DaXing’AnLing Mountain in Inner Mongolia, China. The canopy height model (CHM) from lidar data were used to extract individual tree structures (location, height, crown width). Field measurements related tree height to diameter of breast height (DBH), lowest branch height and leaf area index (LAI). Series of Landsat images were used to classify tree species and land cover. MODIS LAI products were used to estimate the LAI of individual trees. Combining all these input variables to drive RAPID, high-resolution optical remote sensing images were simulated and validated with available satellite images.
Evaluations on spatial texture, spectral values and directional reflectance were conducted to show comparable results.
The study provides a proof-of-concept approach to link lidar and MODIS data in the parameterization of RAPID models for high temporal and spatial resolutions of image reconstruction in forest dominated areas.
- Temporal interpolation
- 3D model
- High resolution
Optical remote sensing images have been widely used in monitoring forest ecosystems. Spatial, temporal and spectral resolutions are the three key indicators to be considered during most applications. Spatial resolution had been improved from a scale of hundreds of meters (e.g. Landsat 8) to one of a half-meter (e.g. GeoEye-1 or Worldview-2) with only a slight increase in the number of spectral bands. However, in forested area, temporal resolution is generally reduced by frequent rains or cloud covers, which prevents users from continuously acquiring clear optical remote sensing images.
Temporally continuous satellite images are important for forest monitoring (Lunetta et al. 2004; Masek et al. 2008; Nitze et al. 2015) since forest reflectance varies with seasonality (Kobayashi et al. 2007; Xu et al. 2013). A few studies have been conducted to interpolate those contaminated images by rains or clouds. For example, Landsat images have been blended with MODerate-resolution Imaging Spectroradiometer (MODIS) data to create spatial and temporal fusion data (Gao et al. 2006; Hilker et al. 2009; Wu et al. 2012). Further, radiative transfer models have also been used to simulate a series of high temporal resolution images for future space earth observation missions (Inglada et al. 2011). However, spatial resolution is generally moderate due to the use of simple homogeneous radiative transfer models, which are not able to deal with high resolution simulation with diverse tree species and mountain shadows.
In recent years, light detection and ranging (lidar) has been a widely used tool for forest studies (Adams et al. 2012; Arno et al. 2013; Montesano et al. 2013). The greatest advantage of lidar is to provide direct measurements of very detailed 3D forest structures, so it can be used to reconstruct 3D trees to support the simulation of radar remote sensing signals (Lucas et al. 2006) and to study how 3D structures affect the quality of optical images (Barbier et al. 2011). By coupling lidar with high temporal data such as MODIS using 3D radiative transfer models, it will be possible to generate both high spatial and temporal resolution optical remote sensing images. However, very few studies were found using this approach. Therefore, we will test the possibility of that approach to simulate high-resolution optical satellite images on an arbitrary day of the growing season in a forested area.
Plot variables at the study site
Tree stems (Birch: Larch)
Shrub height (m) and cover
Mean tree height (m)
Mean DBH (cm)
1.5, 70 %
2.68 ± 0.13
1.8, 60 %
2.88 ± 0.24
1.5, 75 %
2.56 ± 0.09
0.7, 3 %
3.31 ± 0.07
2.0, 80 %
0.3, 80 %
1.5, 50 %
2.06 ± 0.13
2.51 ± 0.20
1.3, 70 %
2.36 ± 0.17
1.2, 20 %
2.71 ± 0.25
1.2, 5 %
3.06 ± 0.22
0.7, 3 %
2.42 ± 0.50
1.2, 15 %
3.51 ± 0.20
1.0, 5 %
2.43 ± 0.12
0.5, 20 %
1.44 ± 0.06
1.0, 80 %
1.50 ± 0.15
1.3, 30 %
2.70 ± 0.12
0.5, 50 %
2.28 ± 0.09
Based on inventory data of individual tree structures in plots L1 to L9, the DBH and crown length (L) of trees were regressed on tree height (H), where heights were derived by lidar. For convenience, both crowns of larch and birch are defined as spherical in shape.
Individual tree crowns were segmented from CHM using the “TreeVaW” tool (Popescu and Wynne 2004), which uses a circular window filter to segment trees and produces the location of each tree (x i , y i ), height (H i ) and crown radius (R i ).
Optical satellite images
Several scales of geo-referenced satellite images were used, including SPOT-6 (1 m), Landsat (30 m) and MODIS (250 m). Due to frequent cloud cover, SPOT and Landsat were not able to capture clear land surface images during rainy days, which happened mostly in the growing season (May to September). There was only one cloud-free SPOT-6 image, obtained on October 10, 2013. For the same reason, only three Landsat images were clear (May 5, 2013; Sep 9, 2013; Sep 29, 2013) in 2013. As well, we collected a Landsat 8 image on May 24, 2014.
A Gram-Schmidt Spectral Sharpening image fusion technique in ENVI 5.1 (ITT Exelis) was applied to produce pan-sharpened Landsat 7 or 8 multi-spectral images with a resolution of 15 m. This pan-sharpening method was selected because it preserves the original spectral information of the image and can be simultaneously applied to multispectral bands. The Landsat image pixel values (in digital numbers) were converted to top-of-atmosphere (TOA) spectral radiance, which was further converted to land surface reflectance using the Fast Line-of-sight Atmospheric Analysis of Hypercubes (FLAASH) atmospheric correction model with the atmospheric visibility parameter estimated from the MODIS aerosol product.
MODIS 16-day 250 m NDVI images and the 500 m LAI products from Jan 1 to Dec 31, 2013 were downloaded. Because a maximum value filtering method was used, NDVI and LAI products had significantly fewer cloud cover problems. NDVI data were used to determine the phenology of the boreal forest, including birch, larch and understory, which allow interpolation of Landsat images ranging from clear to contaminated days. MODIS LAI products were utilized to determine the leaf area of each tree. Despite its low resolution, it is the only continuous global leaf area product, but with acceptable accuracy (Ahl et al. 2006).
MODIS Bidirectional Reflectance Factor (BRF) products in May of 2013 were collected for validation. The BRF curves were reconstructed from the kernel coefficients using the Algorithm for Model Bidirectional Reflectance Anisotropies of the Land Surface (AMBRALS) (Wanner et al. 1995; Huang et al. 2013b; Sharma et al. 2013).
RAPID is a 3D radiative transfer model, able to simulate reflectance images over complex 3D natural scenes at large scales (30 to 1000 m) with great efficiency (Huang et al. 2013a), implying that RAPID can simulate images at MODIS pixel scales (250 to 1000 m). The main input parameters of RAPID consist of 3D structures of the ground, trees, buildings and rivers, as well as reflectance and transmittance of leaves, branches, walls, water bodies and roads under a few sun and sensor angles. The main outputs are BRF curves and land surface reflectance images with defined spatial resolution (default 0.5 m).
The sensor is an advanced version of the Compact High Resolution Imaging Spectrometer (CHRIS/PROBA). CHRIS is the only multi-angular sensor launched with both high spatial (17 m) and spectral resolution (20–40 nm) (Rautiainen et al. 2008; García Millán et al. 2014). For any selected target, five images with different viewing angles (−55°, −36°, 0°, 36° and 55°) were made within a short span of 2.5 min. The virtual sensor was placed above the canopy under clear sky conditions.
The DTM (digital terrain model) remained unchanged, a reasonable assumption for forested areas;
Individual tree LAI (LAItree) was predicted from tree height (Xiao et al. 2006);
We accepted a spherical leaf angle distribution (LADtree) for all trees due to missing measurements;
Reflectance of non-vegetation objects, such as walls, water bodies and roads remained constant, following precedents set in existing literature (Wang et al. 2008) or in the ENVI spectral library.
With these assumptions, there were two types of input parameters: fixed or dynamic. Fixed parameters were DEM, land cover map, individual tree map (coordinates, DBH, height, crown radius, crown length). DEM and land cover map were re-sampled to a resolution of 1 m. Land cover maps were generated using a decision tree method with six classes: bare soil, road, birch forest, larch forest, water surface and buildings. Decision rules were largely based on the Ratio Vegetation Index (RVI), the Normalized Difference Water Index (NDWI) and CHM. Dynamic parameters determined the seasonal change of reflectance, such as component reflectances, LAI and sun position, obtained mainly from time series of MODIS products, including NDVI, LAI and land surface temperature (LST).
Leaf reflectance and transmittance were measured only once, which was not sufficient to represent optical features for the entire growing season. Thanks to the PROSPECT model and changing the most sensitive input (leaf chlorophyll content) while fixing others, seasonal leaf reflectance and transmittance could be simulated (Barry et al. 2009). Previous studies have shown that the amount of leaf chlorophyll is correlated with NDVI (Wu et al. 2008; Rulinda et al. 2011; Croft et al. 2013; Feng and Niu 2014). Therefore, we used a linear relationship between MODIS NDVI products (0.1 to 1.0) and the amount of leaf chlorophyll (10 to 100 μg∙cm−2).
Seasonal variation in background reflectance was complex. However, soil moisture played a major role (Muller and Décamps 2001; Weidong et al. 2002; Whiting et al. 2004), which was then derived from TVDI (temperature vegetation dryness index) inferred from temperature and NDVI (Sandholt et al. 2002; Liang et al. 2014). TVDI is highly correlated with soil moisture (Holzman et al. 2014). Therefore, we estimated the soil reflectance as the weighted average of dry soil and wet soil reflectance, where the weights were TVDI and (1-TVDI) respectively. The background was defined as soil covered by a homogeneous shrub layer with a LAI of 0.5. Shrub leaves were assumed to have the same optical parameters as birch leaves.
From the Landsat classification map, pure birch and larch pixels were selected to determine the beginning and final day (DOY) of the annual growing season, using the following phenology analysis.
First, MODIS time series NDVI data were fitted using a harmonic analysis (Jonsson and Eklundh 2004) to remove random noises. We referred to the maximum and minimum values of NDVI as NDVImax and NDVImin. The starting date was defined as the DOY when NDVI exceeded 20 % of (NDVImax – NDVImin) between DOY 1 and DOY 180. Similarly, the final date was the DOY when NDVI exceeded 20 % of (NDVImax – NDVImin) between DOY 180 and DOY 365.
Temporal leaf area index of individual trees
Since the TreeVaW (Popescu and Wynne 2004) had not been tested in our study site, we manually segmented a few tree crowns in nine sub-plots with different tree densities in order to evaluate the accuracy of the extracted number of trees, height, location and crown radius.
We carried out four types of evaluations: (a) CCD image was used to check the pattern of simulated half-meter images; (b) Landsat images were used to check reflectance values of nadir images at the same date; (c) MODIS BRF products were used to compare simulated BRFs and (d) finally, we used four dates of Landsat images to evaluate temporal simulations.
Land cover classification
Comparisons of nadir images
Comparisons of BRF
Dynamic parameters of birch or larch forests
Chlorophyll content (μg∙cm−2)
May 5, 2013
0.16 × H tree
0.13 × H tree
May 24, 2014
0.17 × H tree
0.18 × H tree
June 27, 2012
0.20 × H tree
0.23 × H tree
Sept 5, 2013
0.19 × H tree
0.11 × H tree
Our main objective was to create and test how to couple lidar data and temporal optical data MODIS in order to simulate high-resolution optical satellite images. A framework was built and tested at the Genhe Forest Farm. In spite of some biases or errors, the approach successfully produced temporal images with high spatial, spectral and angular resolutions, which confirmed the possibility to fuse lidar and MODIS data.
Major contributors on simulation
The framework included four main data sources: lidar, Landsat, MODIS and field data. To drive a 3D model, the most important inputs were 3D scenes and the inside reflectance and transmittance of 3D objects. Lidar was the first contributor to providing 3D structures of individual trees and background. Lidar-derived 3D structures were normally static, but 3D scenic objects, especially their LAI, were dynamic. Therefore, we used an allocation method to downscale MODIS LAI into each tree, a technique not found in previous studies. Landsat images were used to classify birch and larch, supporting the generation of 3D trees.
The optical parameters of 3D objects were collected in the field or obtained from existing references; these were also dynamic. Therefore, MODIS NDVI data were used to calibrate leaf chlorophyll for the PROSPECT model, which then simulated dynamic leaf reflectance and transmittance. Background soil reflectance varied over time and was difficult to obtain. An alternative is to use TVDI to adjust soil reflectance, which is a more recent idea and needs to be evaluated in any future research.
3D structure errors:
MODIS data uncertainty
Landsat data uncertainty
Landsat images were used to compare simulated nadir reflectance and image textures. Figure 10 shows significant differences in the blue band, which can be largely explained by an atmospheric correction error because blue band reflectance should be lower after a correct removal of aerosol scatter. This atmospheric correction was carried out by using the FLAASH module of the ENVI 5.1 software, where the aerosol optical depth and water content were only estimated from images.
RAPID is relatively fast with 3D models, but running one case (1 km) still needs four to six hours at individual tree scale on a workstation (using 10 CPU cores). We are of the opinion that it is not feasible for the generation of operational products. However, for some scientific use, focused on local areas, it may be worthwhile to obtain images with very high resolutions (spatial, spectral and angular) for research within an acceptable time frame and cost structure. Because RAPID can run at scalable resolutions, 3D scenes of dense forests can be up-scaled to regular grids with a medium resolution (e.g. 5 to 10 m), which significantly improves calculation efficiency (less than 30 min) without much loss in accuracy. Furthermore, we can create a reference table of 3D scenes, classifying a study area into fewer categories with possible combinations of DEM, understory, tree locations, tree heights and tree LAI. The corresponding reflectance images will be simulated and stored as an image database. Once the database is created, a quick search method can be used to pick up desired images based on input parameters such as DEM, understory, tree distribution and LAI. In the current framework, we only dealt with the capability of coupling simulation. Improvements will be presented in our next study.
Scale effect and scaling have been big issues in remote sensing community. When models or algorithms at small scales are used at large scales, they may produce certain errors, especially for non-linear models (Tao et al. 2009). Scale issues constrain the accuracy of retrieval and limit the development of remote sensing applications. In this study, MODIS LAI products at a 1 km scale were down-scaled to the level of LAI of individual trees, using lidar at a scale of only a few meters, used to coincide with the RAPID model scale. Assuming that MODIS LAI products are scale-corrected, this scaling does not change total leaf area. Since LAItree was found to be correlated with tree height, the down-scaling model should also be linear without model scale issues. In comparison with satellite images, the simulated RAPID images should also be re-sampled to coincide with the satellite scale, including the MODIS and Landsat scales. Fortunately, reflectance or radiance images are scale independent.
Using MODIS and lidar data to reconstruct 3D scenes at an individual tree scale, the RAPID model is capable of simulating time series of images at spatial scales from 0.5 m to 1 km and temporal scales of a few days. Although the 3D scene estimation is not perfect, this type of multi-scale optical image dataset will be useful to support the understanding of scale problems.
We presented a simulation framework which links lidar with optical images to produce series of temporal images. The study provides a proof-of-concept approach to link lidar data in the parameterization of a RAPID model for temporal image reconstruction in forest dominated areas. Demonstrations were applied at the Genhe Forest Farm, a remote forest reserve in China. Evaluations on nadir reflectance, spatial textures and BRF confirmed that 3D simulation provides an insight look into how images vary over time. Many uncertainties were identified, which can be expected to be reduced in any future study. Strategies to improve efficiency are possible and discussed.
The authors gratefully acknowledge the Chinese National Basic Research Program (2013CB733401) and the Chinese Natural Science Foundation Project (41171278).
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