Leaf Area Index (LAI) is an important biophysical variable for vegetation. Compared with vegetation indexes like NDVI
and EVI, LAI is more capable of monitoring forest canopy growth quantitatively. GLASS LAI is a spatially complete
and temporally continuous product derived from AVHRR and MODIS reflectance data. In this paper, we present the
approach to build dynamic LAI growth models for young and mature Larix gmelinii forest in north Daxing’anling in
Inner Mongolia of China using the Dynamic Harmonic Regression (DHR) model and Double Logistic (D-L) model
respectively, based on the time series extracted from multi-temporal GLASS LAI data. Meanwhile we used the dynamic
threshold method to attract the key phenological phases of Larix gmelinii forest from the simulated time series. Then,
through the relationship analysis between phenological phases and the meteorological factors, we found that the annual
peak LAI and the annual maximum temperature have a good correlation coefficient. The results indicate this forest
canopy growth dynamic model to be very effective in predicting forest canopy LAI growth and extracting forest canopy
LAI growth dynamic.
High spatial and temporal resolution Normalized Difference Vegetation Index (NDVI) data can be used to describe vegetation dynamics and provide the variation of surface for monitoring phenology and land cover change quantitatively. This paper presents a method using MODIS Land Cover data with 30m LULC map calculates the percentage of every class in the MODIS pixel. And the mean MODIS NDVI can be got through the average value of pure pixels using MODIS NBAR product from 2004 to 2010. Then the logistic model is fitted to the average MODIS NDVI to simulate the variation in NDVI time series. At last, the simulated NDVI time series of all vegetation types are extracted as background values and the HJ-1 CCD NDVI is used to adjust the curve of time-series NDVI to estimate the NDVI at high spatial and temporal resolution. The method is applied to the Heihe River basin and the region growing two crops a year. The results are compared with some filed measured data, which shows the high feasibility of the method to generate accurate and reliable data. It is proved that the method can be used in small scales to lager regions and the results can be a kind of fundamental data in other studies.
Based on the Aster LAI estimation, the main object of this paper is to generate the high spatial and
high temporal resolution LAI product. One method is proposed to get high spatial and temporal resolution LAI
product by fusing MODIS LAI product and Aster LAI. In this method, the LULC data is used to register with
MODIS data, then the percentage of classes of PFT classification in the MODIS pixel can be calculated. And the
multi-year mean MODIS LAI values are the background data, the Aster LAI is used to adjust this curve of
multi-year mean MODIS LAI. And we validate LAI with high spatial and high temporal resolution using the
measured data that is not to be used as the training data. The results is good and can meet our study needs.
A new approach for determining the forest leaf area index (LAI) from a geometric-optical model inversion using multisensor observations is developed. For improving the LAI estimate for the forested area on rugged terrain, a priori information on tree height and the spectra of four scene components of a geometric-optical mutual shadowing (GOMS) model are extracted from airborne light-detection and ranging (LiDAR) data and optical remote sensing data with high spatial resolution, respectively. The slope and aspect of the study area are derived from digital elevation model data. These extracted parameters are applied in an inversion to improve the estimates of forest canopy structural parameters in a GOMS model. For the field investigation, a bidirectional reflectance factor data set of needle forest pixels is collected by combining moderate-resolution-imaging-spectroradiometer (MODIS) and multiangle-imaging-spectroradiometer (MISR) multiangular remote sensing observations. Then, forest canopy parameters are inverted based on the GOMS model. Finally, the LAI of the forest canopy of each pixel is estimated from the retrieved structural parameters and validated by field measurements. The results indicate that the accuracy of forest canopy LAI estimates can be improved by combining observations of passive multiangle and active remote sensors.
Leaf Area Index (LAI) is a key vegetation structural parameter in ecosystem. Our new approach is on forest LAI
retrieval by GOMS model (Geometrical-Optical model considering the effect of crown shape and Mutual Shadowing)
inversion using multi-sensor observations. The mountainous terrain forest area in Dayekou in Gansu province of China
is selected as our study area. The model inversion method by integrating MODIS, MISR and LIDAR data for forest
canopy LAI retrieval is proposed. In the MODIS sub-pixel scale, four scene components' spectrum (sunlit canopy, sunlit
background, shaded canopy and shaded background) of GOMS model are extracted from SPOT data. And tree heights
are extracted from airborne LIDAR data. The extracted four scene components and tree heights are taken as the a priori
knowledge applied in GOMS model inversion for improving forest canopy structural parameters estimation accuracy.
According to the field investigation, BRDF data set of needle forest pixels is collected by combining MODIS BRDF
product and MISR BRF product. Then forest canopy parameters are retrieved based on GOMS. Finally, LAI of forest
canopy is estimated by the retrieved structural parameters and it is compared with ground measurement. Results indicate
that it is possible to improve the forest canopy structural parameters estimation accuracy by combining observations of
passive and active remote sensors.
Radiosity method is based on the computer simulation of 3D real structures of vegetations, such as leaves, branches and
stems, which are composed by many facets. Using this method we can simulate the canopy reflectance and its
bidirectional distribution of the vegetation canopy in visible and NIR regions. But with vegetations are more complex,
more facets to compose them, so large memory and lots of time to calculate view factors are required, which are the
choke points of using Radiosity method to calculate canopy BRF of lager scale vegetation scenes. We derived a new
method to solve the problem, and the main idea is to abstract vegetation crown shapes and to simplify their structures,
which can lessen the number of facets. The facets are given optical properties according to the reflectance, transmission
and absorption of the real structure canopy. Based on the above work, we can simulate the canopy BRF of the mix scenes
with different species vegetation in the large scale. In this study, taking broadleaf trees as an example, based on their
structure characteristics, we abstracted their crowns as ellipsoid shells, and simulated the canopy BRF in visible and NIR
regions of the large scale scene with different crown shape and different height ellipsoids. Form this study, we can
conclude: LAI, LAD the probability gap, the sunlit and shaded surfaces are more important parameter to simulate the
simplified vegetation canopy BRF. And the Radiosity method can apply us canopy BRF data in any conditions for our research.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.