Net primary production (NPP) is the production of organic compounds from atmospheric or aquatic carbon dioxide, principally through the process of photosynthesis. Climate changes of this magnitude are expected to affect the NPP of the world’s land ecosystems. In this study, we used a light-use efficiency model and linear regression model to describe and analyze the spatial and temporal patterns of terrestrial net primary productivity (NPP) in China during 2002-2010. First, we used the reconstructed 16-day 0.05°MODIS NDVI product (MOD13C1), 0.05°gridded GLDAS (Global Land Data Assimilation System) meteorological data and land use map to estimate the NPP in China. The spatial variability of NPP was analyzed during all periods, growing seasons and different seasons, respectively. Based on regression analysis method, we quantified the trend of NPP change in China during 2002-2010.
The subject intersection becomes one of the hot research topics recently. It is a new direction to integrate the GIS
technologies with Bibliometrics. The literatures concerned with geosciences normally involve some spatial related
information. In this paper, the spatial information of the study area and sampling or observing points was extracted. Then
these data were analyzed and presented by using the GIS technologies. The results indicate that there are big variations
of the spatial distribution. For the whole Qinghai-Tibet plateau, the degree of interest increase as follow: southwest,
northwest, southeast, and northeast. For the regions, Qilian Mountains, Qiangtang plateau, Qinghai-Tibet Road and
Qinghai-Tibet Railway, Qinghai Lake, and Sichuan-Tibet Road are the hotspot regions. There are differences of the
distribution characteristics in the different segments along the latitudinal direction and longitudinal direction. There is
transfer tendency from middle Qinghai-Tibet Plateau to northern Qinghai-Tibet Plateau. Most of sampling and observing
points are close to the traffic lines. The point numbers decrease quickly along with the increasing distance to the traffic
lines.
Gross Primary Production (GPP) is the sum of carbon absorbed by plant canopy. It is a key measurement of carbon mass
flux in carbon cycle studies. Remote sensing based light use efficiency model is a widely used method to estimate
regional GPP. In this study, MODIS-PSN was used to estimate GPP in Heihe River Basin. In order to better the model
accuracy, maximum light use efficiency (ε0) in MODIS-PSN is estimated using local observed carbon flux data and
meteorological data. After adjustment of parameter ε0, MODIS-PSN can correctly estimate GPP for major vegetation
type in the Heihe River Basin. Then, yearly GPP over Heihe River Basin was estimated. The results indicated that about
1.4*1013g carbon enter terrestrial ecosystem through vegetation photosynthesis in the Heihe River Basin one year. In
contrast, there is just 5.73*1013g carbon enter terrestrial ecosystem according to the standard MODIS GPP product,
which is greatly underestimated GPP in the Heihe River Bain.
This research dealt with a daytime integration method with the help of Simple Biosphere Model, Version 2 (SiB2).
The field observations employed in this study were obtained at the Yingke (YK) oasis super-station, which includes an
Automatic Meteorological Station (AMS), an eddy covariance (EC) system and a Soil Moisture and Temperature
Measuring System (SMTMS). This station is located in the Heihe River Basin, the second largest inland river basin in
China. The remotely sensed data and field observations employed in this study were derived from Watershed Allied
Telemetry Experimental Research (WATER). Daily variations of EF in temporal and spatial scale would be detected by
using SiB2. An instantaneous midday EF was calculated based on a remote-sensing-based estimation of surface energy
budget. The invariance of daytime EF was examined using the instantaneous midday EF calculated from a
remote-sensing-based estimation. The integration was carried out using the constant EF method in the intervals with a
steady EF. Intervals with an inconsistent EF were picked up and ET in these intervals was integrated separately. The truth
validation of land Surface ET at satellite pixel scale was carried out using the measurement of eddy covariance (EC) system.
Change detection is the process of identifying difference in the scenes of an object or a phenomenon, by observing the
same geographic region at different times. Many algorithms have been applied to monitor various environmental
changes. Examples of these algorithms are difference image, ratio image, classification comparison, and change vector
analysis. In this paper, a change detection approach for multi-temporal multi-spectral remote sensing images, based on
Independent Component Analysis (ICA), is proposed. The environmental changes can be detected in reduced second and
higher-order dependencies in multi-temporal remote sensing images by ICA algorithm. This can remove the correlation
among multi-temporal images without any prior knowledge about change areas. Different kinds of land cover changes
are obtained in these independent source images. The experimental results in synthetic and real multi-temporal
multi-spectral images show the effectiveness of this change detection approach.
The sampling protocol adopted during a field campaign at an Alpine meadow site (Shandan site), during July 2002 is
based on the so-called "Valeri" protocol (VALERI). The field campaign LAI measurements in Shandan are scaled up to
30×30 m2 raster maps based on Landsat ETM+ imagery. Regression analysis is applied to construct empirical transfer
functions for the determination of Leaf Area Index (LAI) raster imagery from ETM+ Normalized Difference Vegetation
Index (NDVI) and Simple Ratio (SR) data. Subsequently, the scaling up of the LAI raster maps is performed by the
aggregation of the 30x30 m2 data into 1×1 km2 pixels by calculating the average LAI values for the low resolution pixels.
The up-scaled data are used to validate the MODIS LAI product at the Shandan site. A power regression model
(LAI=2.3758*NDVI3.5216, R2=0.66, P<0.01), established between field measured LAI and ETM+ NDVI, elicits a high
statistical significance. A linear regression model (LAI=0.1798*SR-0.3574, R2=0.55, P<0.01) is established between
field measured LAI and ETM+ SR. The MODIS LAI product correlates best with the ETM+ LAI transfer function
obtained with NDVI data. Its R2 reaches 0.46, its slope 0.97, but the intercept is 0.7, which suggests that MODIS LAI is
systematically underestimated. The results illustrate that LAI measured with a LAI-2000 instrument at the VALERI
Shandan site leads to an underestimation of the MODIS LAI product. A plausible cause for the systematic
underestimation related with the LAI field measurements is discussed.
The Digital Elevation Model (DEM) are terrain elevations at regularly spaced horizontal intervals, i.e., an a
grid of regularly spaced elevations. With the development of computing technology, the methods of data
acquisition, data storage and data processing speed for DEMs get along well. Heihe river lies in the northwest
of China and is a continental river. It roots from snow-ice and disappears in deserts. The drainage area is about
140,000 square kilometers and the river is the main water source for the living. The upper reaches are
mountainous and the runoff is very important. The middle reaches are oasis. At present, many hydrological
and ecological models are introduced. The catchment basin and stream network data acquired from DEMs are
main input data for many surface hydrological models. So the quality and resolution of DEMs are significant.
Software ENVI Version 4.2 furnishes with the DEMs Extraction Module. The paper compared several
methods for extracting DEMs of the upper reaches. We extracted the elevation data from the ASTER---stereo
images with the module and created the DEM. Secondly; we collected the DEM based on the contour map of
Heihe river. The comparison of the DEMs quality was carried out between the DEM from the contour map,
the DEM extracted by ENVI Version 4.2. To a certain extent, the DEM from Aster imagery can reflect the
terrain and be used in hydrological models.
Leaf area index (LAI) is a critical vegetation parameter for the global and regional scale studies of the climatic and environmental change. There are many methods that can be used to get LAI. In this paper, the method, developed by Qi et al. (2000) was selected. The process includes three steps: the first step is model inversion, using BRDF model to produce LAI with pixels chose randomly in one vegetation type region; the second step is quality control, removing the outliers, fitting equations using the LAI from second step and satellite data NDVI; the third step is LAI mapping, selecting the best equation and applying it to the whole region to mapping spatial LAI distribution. The main objective of this paper is to get one method that can be used in Arid and Semi-arid Northwestern China to derive LAI in the case of lack of LAI measurements. The results derived by the above approach were compared with ones derived from the empirical method (Sellers et al. 1996) and the LAI measured in field. The results suggested that the method can get good result and R2 was 0.7947, though they were greater than field measurements. The results from empirical method were closer to the measurements than ones from Qi's method, but the higher the values of NDVI were, the greater the values of estimated LAI were than LAI measurements, when the values of NDVI were greater than a certain values (here 0.74). However, the result derived from Qi's method is closer to the LAI measured in field. In general, this method was feasible in arid and semi-arid northwestern China and can get satisfactory results.
Community land model or common land model (CLM) describes the exchange of the fluxes of energy, mass and momentum between the earth's surface and the planetary boundary layer. This model is used to simulate the environmental changes in China. Hence, it requires a complete parameters field of the land surface. The present paper focuses on making the surface datasets of CLM in China. In the present paper, vegetation was divided into 39 Plant Function Types (PFTs) of China from its classification map. The land surface datasets were created using vegetation type, five land cover types (lake, wetland, glacier, urban and vegetated), monthly maximum Normalized Difference Vegetation Index (NDVI) derived from SPOT_VGT data and soil properties data. The percentages of glacier, lake and wetland were derived from their own vector maps of China. The fractional coverage of PFTs was derived from China vegetation map. Time-independent vegetation biophysical parameters, such as canopy top and bottom heights and other vegetation parameters related to photosynthesis, were based on the values documented in literatures. The soil color dataset was derived from landuse and vegetation data based on their correspondent relationship. The soil texture (clay%, sand% and silt%) came from global dataset. Time-dependent vegetation biophysical parameters, such as leaf area index(LAI) and fractional absorbed photosynthetically active radiation(FPAR), were calculated from one year of NDVI monthly maximum value composites for the China region based on equations given in Sellers et al. (1996a,b) and Los et al. (2000). The resolution of these datasets for CLM is 1km.
Leaf area index (LAI) is an important characteristic of vegetation and a critical vegetation parameter for the global and regional scale studies of the climatic and environmental change. There are many methods that can be used to get LAI, generally, they belong to the three types: filed measurement; empirical and modeling methods. In this paper, we try to get one method that can be used in Arid and Semi-arid Northwestern China to derived LAI in the case of lack of LAI measurements. The empirical method was selected to derive LAI for different type vegetation from SPOT-VGT and landuse data. The study area was the Heihe River basin that has a large-scale area and diverse vegetation types. There were 7 types of vegetation to be mapping LAI using the methodology. They were irrigated, dry, forest, shrub, dense grass, moderate-dense grass and alkaline lands. The parameters of vegetations were modified based on the study area and vegetation types. The results were compared with the whole China LAI map and filed measured LAI. The results suggested that the method was feasible in arid and semi-arid northwestern China. And the results could be greatly improved if using big scale vegetation class map or plant function type data, and the parameters were derived based on the vegetation types in their own region.
An accurate land cover mapping is a prerequisite to run all biospheric models. In this paper, NDVI (Normalized Difference Vegetation Index) and NDWI (Normalized Difference Water Index) time-series of data sets derived from 1-km SPOT/VEGETATION products were used to compile the land cover map of northwest China. The unsupervised classification technique of ISODATA was applied to classify the land cover classification system. With the assumption of the 1:100000 land use map of northwest China interpreted from TM images as the truth, the accuracy of the SPOT/VEGETATION land cover map was evaluated by validating 47 sampling units randomly selected in the whole mapping region. Each sample is a square unit of 25km´25km. The validation results showed an approving accuracy of the land cover map of northwest China. In addition, the combination of NDVI and NDWI vegetation indexes is an effective method on large regional land cover mapping. Meanwhile, three major problems are addressed for explaining the reasons that influence the accuracy of land cover mapping in this region
In addition to investigate the rainfall over the Tibetan Plateau, Microwave Imager observations on board of the satellite Tropical Rainfall Measurement Mission (TRMM) have been also used to retrieve land surface parameters, such as sur-face temperature (Te), vegetation water content (Wc), and volumetric soil surface moisture (Mv). A three dimensional Look-up Table (LUT) scheme, by using one band brightness temperature, a 'Polarization Index' (PI), and an 'Index for Soil Wetness' (ISW), was developed for this purpose, which can retrieve the three basic parameters Te, Wc, and Mv simultaneously. Considered that there are still clouds as well as heavy rainfall disturbance in deriving surface parameters over the Tibetan Plateau, 10-day composite TMI images were used. For the five months from May to September 1998, the distribution of surface parameters of each ten days on the mesoscale intensive experimental region of GAME-Tibet was evaluated. The results were compared with field observations; particularly, for the most concerned surface soil moisture, the results are quite acceptable. 3-D LUT is an easy and effective method to be used in the passive microwave remote sensing.
The estimation of snow parameters such as snow extent, snow depth and snow water equivalent are very important. They are parameters in land surface schemes and are very useful in snow disaster assessment. Passive microwave remote sensing has advantages in retrieving these parameters, especially snow depth. However, this technique has not been applied to monitor snow in Tibetan Plateau so far. So since last winter we tried to operationally monitor snow in this area by using SSM/I data, providing daily snow depth maps to the concerning sections of local government. In the meantime, the in-situ measurements of snow depth data in the Tibetan Plateau were collected to validate the retrieval algorithm employed in this study. In the paper, SSM/I images before and after a heavy snowfall were analyzed and compared with MODIS images. The results showed that the snow extent from SSM/I data is consistent with that from MODIS data, and snow depths from SSM/I are helpful for the assessment of snow disaster. However, compared with in-situ observations SSM/I derived snow depths are significantly overestimated. Since passive microwave remote sensing is almost transparently to atmosphere and cloud, it will play an important role in monitoring snow in the Tibetan Plateau, wih the retreival algorithm being improved. This will be more dominant when AMSR data are available.
So far, there is not an operational algorithm to estimate the snow water equivalent from passive microwave remote sensing data (SSM/I) in the Tibetan Plateau. In this study the SSM/I brightness temperature data in January 1993 are used to estimate SWE at this region. The frequencies of SSM/I data are used to retrieval snow depth are 19 and 37GHz in horizontal polarization. The results have shown all existing algorithms overestimated the snow depth in the Tibetan plateau. This paper analyzed the reasons of overestimation of snow depth from several aspects, such as the water content of snowpack, large water bodies (e.g. lakes), and the abnormal field snow depth data. After eliminating some futile data (including the passive microwave brightness temperature values and snow depth data in the weather stations), an improved algorithm has been established to retrieval the snow depth from the difference of 19 and 37GHz brightness temperature in horizontal polarization. Here, snow density is obtained by a time function of fresh snow density. The snow depth and density were converted to the snow water equivalent, and are regarded as the ground truth. In finally, the TB vertically polarized differences of 19 and 37GHz are regressed with the SWE. Using the statistical method, a simple and practical algorithm is developed to estimate the snow water equivalent from the differences of 19 and 37GHz in vertical polarization.
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