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.
Currently, how to effectively utilize assimilation technique to retrieve biophysical parameters from time series remote
sensing dada has attracted special concern. The assimilation technique is based on a reasonable consideration of the
dynamical change rules of biophysical parameters and the time series observational quantities, thereby improving the
quality of the retrieved profiles. In this paper, a variational assimilation procedure for retrieving leaf area index from
time seires remote sensing data is investigated. The procedure is based on the formulation of an objective function, and
SCE-UA optimization method is used to estimate LAI from the MODIS reflectance data with a higher quality in a given
time window. A preliminary analysis using MODIS surface reflectance data at some sites was performed to validate this
method. And the results show that the algorithm is able to produce temporally continuous LAI product efficiently, and
the accuracy of the retrieved LAI has been significantly improved over the MODIS LAI product compared to the field
measured LAI data.
To obtain the transmission line information for the maintenance of high and ultrahigh voltage electrical network, some
methods of automatic power line and spacer extraction from high-resolution remote sensing images are presented in this
paper. Aiming at the features of power lines and spacers in the high-resolution remote sensing images, a local linear
detector is used to detect pixels of linear features in any directions. According to the responses of the detector, a score
image is constructed, and an optimum method is used to determine the threshold according to different background.
Then the pixels of power lines and spacers can be detected automatically from the score image. After thinning of the
pixels, a curve fitting method is applied to obtain segments of power lines and spacers. As the influence of many
disturbing factors, the detected segments are discontinuous, and there are even some false detections. Based on shape
information of the power lines and spacers, the correct segments are chosen, and they are connected into complete form
of power lines or spacers. The experimental results indicate that our methods can accurately extract power lines and
spacers with different shapes from the high-resolution remote sensing images.
With the rapid development of the technique of remote sensing, many vegetation biophysical variables are estimated
from remote sensing data. However, the biophysical variable products are limited to certain resolutions and some
products are incomplete in space. Therefore these products can not meet the needs of many operational applications.
Therefore, more and more attentions have been paid to fusing or assimilating multi-sensor and multi-temporal data to
improve the biophysical variable products with precision as high as possible and high temporal resolution and variable
spatial resolutions in recent years. In this paper, the multiscale Kalman filter (MKF) is introduced to fuse the biophysical
products from different kinds of remote sensing data. The multiscale Kalman filter allows us to model explicitly and very
efficiently the spatial dependence and scaling properties of remote sensing data, and can be used to produce optimal
estimation of biophysical variables at any desired spatial scale given uncertain and sparse observations at different scales.
Taking leaf area index as an example, our method is tested by fusing LAI products from MODIS and Landsat ETM+,
and the results show that the method can be used to fuse effectively different biophysical variables inverted from
different sensors.
The paper put forward the fault remote sensing information and extraction model based on the different spatial characteristics and combination of these characteristics in order to improve its efficiency in making the computer information processing system self-adapted and automatic. Guided by the mathematical morphology, data synthesis and artificial intelligence, and generalizing the artificial intelligence technology like nerve calculation and geographic information analysis model, the paper set up the obtaining, expression and analysis mechanism of the fault space information and knowledge. Simulating the intensive geosciences analysis and space decision-making process of the geosciences experts in the understanding, information extract of the remote sensing information.
Due to the complexity and non-regularity of tree shapes, traditional digital photogrammetry using stereo matching method is difficult to obtain the accurate tree height, This fact therefore limits the application of the aerial digital photogrammetry technology in the power line survey. This paper presents a method of tree height extraction from large viewing aerial image using the knowledge of segmented tree crown. This method is based on a rough digital surface model (DSM) of tree crowns and the exterior orientation of the image. The basic steps of this method is that the DSM is first used to find the region of interest in the image based on the exterior orientation, and then the edges of the distinct trees or branches are extracted using image segmentation technology. An algorithm that uses both the rough DSM height information and exterior orientation data to calculate the accurate heights of the segmented trees or branches is presented. The algorithm assumes that most of the trees are upright, and the projection in the large viewing angle images of the crown and branches can therefore be used to calculate their heights relative to the averaged DSM height. Hence, the accurate height of the trees around the rough DSM can be refined. Some experimental results are given with the image captured from multi-angular imaging system mounted on a helicopter in which a Position and Orientation System (POS) is onboard to record the exterior element of the cameras. The experimental results demonstrated that this algorithm can largely improve the accuracy of tree height extraction. The application in power line monitoring system is promising.
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