[Show abstract][Hide abstract] ABSTRACT: Remote sensing image fusion is an effective way to extract a large
volume of data from multi-source images. However, traditional image
fusion methods cannot meet the requirements of applications because they
can lose spatial information or distort spectral characteristics. In
this paper, a new wavelet method based on a local algorithm is
presented. The proposed method fuses multi-spectral (MS) and
panchromatic (PAN) images to improve spatial information and preserve
spectral characteristics. The main advantage of the new fusion method is
the exploitation of the dependency between neighboring pixels. SPOT5 MS
and PAN images were employed to execute the fusion methods. To compare
with the new method, the principal component analysis (PCA), wavelet
transformation, and PCA-based wavelet (PCA+W) image fusion methods were
selected. Qualitative and quantitative analyses and classification
accuracy assessment were conducted to evaluate the performance of the
fusion methods. The results demonstrate that the new wavelet method
based on a local algorithm is better than traditional image fusion
methods. The new fusion method can achieve a wide range of balance
between high spatial resolution retention and spectral characteristic
preservation; thus, the new method is suitable for different
[Show abstract][Hide abstract] ABSTRACT: Chlorophyll is one of the most important pigments for plant photosynthesis as a proxy of health of vegetation and photosynthetic capacity. In this study, estimation of chlorophyll content of corn of western regions of Jilin Province is conducted based on both the measured hyperspectral data and the environmental satellite hyperspectral image data (HJ-HSI). Four kinds of vegetation indices including NDVI, MSR, MCAVI/OSAVI and TCAVI/OSAVI, are used for the inversion of chlorophyll. Among these vegetation indices, NDVI performs the best in estimating chlorophyll, followed by MSR and TCAVI/OSAVI. Otherwise, using HSI data based on vegetation indices in modeling performs not very well but the verification accuracy is rather high (R2>0.9). And the slopes of validated models are all less than 0.4, which indicate that these estimates are lower than the measured results.
[Show abstract][Hide abstract] ABSTRACT: In this study, the Surface Energy Balance Algorithms for Land (SEBAL) model and Moderate Resolution Imaging Spectroradiometer (MODIS) products from Terra satellite were combined with meteorological data to estimate evapotranspiration (ET) over the Sanjiang Plain, Northeast China. Land cover/land use was classified by using a recursive partitioning and regression tree with MODIS Normalized Difference Vegetation Index (NDVI) time series data, which were reconstructed based on the Savitzky-Golay filtering approach. The MODIS product Quality Assessment Science Data Sets (QA-SDS) was analyzed and all scenes with valid data covering more than 75% of the Sanjiang Plain were selected for the SEBAL modeling. This provided 12 overpasses during 184-day growing season from May 1st to October 31st, 2006. Daily ET estimated by the SEBAL model was misestimaed at the range of −11.29% to 27.57% compared with that measured by Eddy Covariance system (10.52% on average). The validation results show that seasonal ET from the SEBAL model is comparable to that from ground observation within 8.86% of deviation. Our results reveal that the time series daily ET of different land cover/use increases from vegetation on-going until June or July and then decreases as vegetation senesced. Seasonal ET is lower in dry farmland (average (Ave): 491 mm) and paddy field (Ave: 522 mm) and increases in wetlands to more than 586 mm. As expected, higher seasonal ET values are observed for the Xingkai Lake in the southeastern part of the Sanjiang Plain (Ave: 823 mm), broadleaf forest (Ave: 666 mm) and mixed wood (Ave: 622 mm) in the southern/western Sanjiang Plain. The ET estimation with SEBAL using MODIS products can provide decision support for operational water management issues.
Chinese Geographical Science 02/2013; 23(1). · 0.73 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: This article is an elementary exploration and practice of applying the theory and method of ecological footprint to assess regional ecological security. The ecological footprint from 1996 to 2008 and ecological security of Jilin Province of China were calculated and analyzed based on the theory of ecological footprint. Furthermore, the concept and model of ecological footprint pressure index (EFPI) as well as the grade system of regional ecological security was put forward based on ecological footprint so as to analyze the regional ecological security, and some measures to slow down the growth of the ecological footprint and maintain the regional ecological security were put forward at last. The following results were obtained. (1) It is feasible to analyze regional ecological security based on EFPI, which can reflect the degree of ecological security from two respects of carrying capacity and pressure. (2) Among 13 years from 1996 to 2008, the per capita ecological capacity in three regions appears to be a reducing tendency; the per capita ecological footprint shows an increasing tendency.
Remote Sensing, Environment and Transportation Engineering (RSETE), 2012 2nd International Conference on; 01/2012
[Show abstract][Hide abstract] ABSTRACT: The eddy covariance technique provides continuous measurements of plot-level net ecosystem carbon exchange (NEE) across a wide range of vegetation types. However, these NEE estimates only represent fluxes at the tower footprint scale. To quantify the NEE over regions or continents, flux tower measurements need to be up-scaled to large areas. In the present study, we propose a new NEE model solely based on Moderate Resolution Imaging Spectroradiometer data, including enhanced vegetation index (EVI), land surface water index (LWSI), land surface temperature (LST), and Terra nighttime LST′. Site-specific data from the deciduous-dominated Harvard Forest flux site were used. Analysis covered six years (2001–2006) of CO2 flux data. The data of the first four years were used for model building and the rest as validation set. Compared with the model based solely on EVI, we also introduced LST and LSWI into the new model. The results showed that this method could further improve the precision (R2 and RMSE reached 0.857 and 1.273, respectively) and generally capture the expected seasonal patterns of NEE.
[Show abstract][Hide abstract] ABSTRACT: It is difficult to analyse soil properties quantitatively with multispectral remote sensing data. An alternative solution is to determine the main spectral characteristic control points of soil hyperspectral reflectance curves by sensitivity analysis methods. Hyperspectral reflectance is simulated using the control points based on multispectral reflectance collected from satellites in this study. The laboratory hyperspectral reflectance and its continuum-removed curve of Phaeozem and the parent material (PM) of samples collected from Heilongjiang Province, China were analysed, and the spectral characteristic control points determined. Hyperspectral simulating linear and quadratic models based on laboratory reflectance were then built. Results show that montmorillonite and illite are the dominant minerals in Phaeozem PM. Organic matter content determines the spectral characteristics of Phaeozem and makes it suitable for reflectance simulation in the spectrum range of 1000 nm and less; the higher the organic matter content the greater the spectral absorption area. There are two absorption valleys at 500 and 660 nm, which determine the spectral characteristic control points of Phaeozem between 450 and 930 nm, namely 450, 500, 590, 660 and 930 nm. Both the linear and quadratic simulation models built with the characteristic control points accurately describe Phaeozem reflectance, which proves that the characteristic control points are selected reasonably and representatively. The hyperspectral simulation method based on multispectral reflectance closely represents the characteristics of Phaeozem hyperspectral reflectance, partly removes noise and improves the precision of predicting organic matter content. Therefore the method is feasible and useful for data compression of Phaeozem hyperspectral reflectance, soil and vegetation indices building, and quantitative remote sensing in the Phaeozem Zone, northeast China.
International Journal of Remote Sensing 07/2011; 32(13):3819-3834. · 1.36 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Research on the optical characteristics of water color constituents in Chagan Lake of Jilin Province, Northeast China was
carried out in order to investigate the variability of the spectra absorption parameters as inputs to bio-optical models and
remote sensing algorithms for converting observed spectral signals into water quality information. Samples of total particulates,
non-algal particles and colored dissolved organic matter (CDOM) were first prepared by quantitative filter technique (QFT)
and then absorption coefficients of these color producing agents were determined by spectrophotometry. Spectral characteristics
of absorption coefficients by total particulate matter, spectral specific absorption dependency on chlorophyll concentration
(Chl-a) of phytoplankton, spectral absorption slopes variation for CDOM and non-algal particles and their corresponding reasons
were examined and clarified over five months of 2009 and 2010 in this study. Results suggest that total particulate spectral
absorption in Chagan Lake is mainly dominated by non-algal particles in most cases, but phytoplankton could be the dominant
contributor when chlorophyll concentration is high (up to 84.48 mg/m3 in autumn 2010). The specific absorption coefficients of phytoplankton particulate (a*ph(λ)) dependency on Chl-a is significantly variable due to relative contributions of package effect and accessory pigments, and the parameters of power
function are clearly biased on a long time span. The sources of variability in spectral absorption slopes of CDOM and non-algal
particles are mainly attributed to the changing proportions of high molecular weight humic acids and mineral suspended sediments
in waters, respectively.
Keywordscolored dissolved organic matter–spectra absorption–water constituents–spectral slope–Chagan Lake
Chinese Geographical Science 06/2011; 21(3):334-345. · 0.73 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Contaminants in the snow can be used to reflect regional and global environmental pollution caused by human activities. However, so far, the research on space-time monitoring of snow contamination concentration for a wide range or areas difficult for human to reach is very scarce. In the present paper, based on the simulated atmospheric deposition experiments, the spectroscopy technique method was applied to analyze the effect of different contamination concentration on the snow reflectance spectra. Then an evaluation of snow contamination concentration (SCC) retrieval methods was conducted using characteristic index method (SDI), principal component analysis (PCA), BP neural network and RBF neural network method, and the estimate effects of four methods were compared. The results showed that the neural network model combined with hyperspectral remote sensing data could estimate the SCC well.
Guang pu xue yu guang pu fen xi = Guang pu 05/2011; 31(5):1318-21. · 0.27 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The concentrations of chlorophyll-a (Chl-a) and total suspended matter (TSM) are major water quality parameters that can be retrieved using remotely sensed data. Water sampling works were conducted on 15 July 2007 and 13 September 2008 concurrent with the Indian Remote-Sensing Satellite (IRS-P6) overpass of the Shitoukoumen Reservoir. Both empirical regression and back-propagation artificial neural network (ANN) models were established to estimate Chl-a and TSM concentration with both in situ and satellite-received radiances signals. It was found that empirical models performed well on the TSM concentration estimation with better accuracy (R (2) = 0.94, 0.91) than their performance on Chl-a concentration (R (2) = 0.62, 0.75) with IRS-P6 imagery data, and the models accuracy marginally improved with in situ spectra data. Our results indicated that the ANN model performed better for both Chl-a (R (2) = 0.91, 0.82) and TSM (R (2) = 0.98, 0.94) concentration estimation through in situ collected spectra; the same trend followed for IRS-P6 imagery data (R (2) = 0.75 and 0.90 for Chl-a; R (2) = 0.97 and 0.95 for TSM). The relative root mean square errors (RMSEs) from the empirical model for TSM (Chl-a) were less than 15% (respectively 27.2%) with both in situ and IRS-P6 imagery data, while the RMSEs were less than 7.5% (respectively 18.4%) from the ANN model. Future work still needs to be undertaken to derive the dynamic characteristic of Shitoukoumen Reservoir water quality with remotely sensed IRS-P6 or Landsat-TM data. The algorithms developed in this study will also need to be tested and refined with more imagery data acquisitions combined with in situ spectra data.
Environmental Monitoring and Assessment 04/2011; 184(3):1449-70. · 1.68 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: The estimation of crop chlorophyll content could provide technical support for precision agriculture. Canopy spectral reflectance was simulated for different chlorophyll levels using radiative transfer models. Then with multiperiod measured hyperspectral data and corresponding chlorophyll content, after extracting six wavelet energy coefficients from the responded bands, an evaluation of soybean chlorophyll content retrieval methods was conducted using multiple linear regression, BP neural network, RBF neural network and PLS method. The estimate effects of the three methods were compared afterwards. The result showed that the three methods based on wavelet analysis have an ideal effect on the chlorophyll content estimation. R2 of validated model of multiple linear regression, BP neural network, RBF neural network and PLS method were 0. 634, 0. 715, 0. 873 and 0.776, respectively. PLS based on Gaussian kernel function and RBF NN methods were better with higher precision, which could estimate chlorophyll content stably.
Guang pu xue yu guang pu fen xi = Guang pu 02/2011; 31(2):371-4. · 0.27 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: It is important to better understand the spatial distribution of non-point source pollution and related influencing factors in China. On the bases of field sampling, using GIS techniques, this paper analyzed the spatial characteristics of non- point pollution and its relation to land use structure, taking Muling river sub-basin in Sanjiang Plain as the study area. The result shows that the value of TSS, TN, and TP exceed the country criteria and the discrepancy in space is little. In the sub- basin which are controlled mainly by a single land use type, surface water quality of woodland controlled sub-basin is better than that of cropland controlled sub-basin. The concentration of pollutants in combined structure (cropland, woodland and grassland) is highest in three land use structures. This may be due to the complex surface background and other point source pollution in this study area.
[Show abstract][Hide abstract] ABSTRACT: There is a need for improved and up-to-date land use/land cover (LULC) data sets over an intensively changing area in the
Amur River Basin (ARB) in support of science and policy applications focused on understanding of the role and response of
the LULC to environmental change issues. The main goal of this study was to map LULC in the ARB using MODIS 250-m Normalized
Difference Vegetation Index (NDVI), Land Surface Vegetation Index (LSWI), and reflectance time series data for 2001 and 2007.
Another goal was to test the consistency of the classification results using relatively coarse resolution MODIS imagery data
in order to develop a methodology for rapid production of an up-to-date LULC data set. The results on MODIS land cover were
evaluated using existing land use/cover data as derived from Landsat TM data. It was found that the MODIS 250-m NDVI data
sets featured sufficient spatial, spectral and temporal resolution to detect unique multi-temporal signatures for the region’s
major land cover types. It turned out that MODIS 250 NDVI time series data have high potential for large-basin land use/land
cover monitoring and information updating for purposes of environmental basin research and management.
KeywordsAmur River Basin–MODIS–Vegetation Index–land use/land cover–Savitzky-Golay filter–LSWI
Geography and Natural Resources 01/2011; 32(1):9-15.
[Show abstract][Hide abstract] ABSTRACT: The Songnen Plain is an important commodity grain product base in China
for which a spatiotemporal pattern of actual evapotranspiration (ETa)
would provide critical important information to evaluate crop growth
status and water use efficiency. ETa over the Songnen Plain in the 2008
growing season (from May to September) was mapped using the moderate
resolution imaging spectroradiometer time-series products based on the
surface energy balance algorithm for land model and the Penman-Monteith
equation. The estimated ETa was validated using eddy covariance surface
data. The calculated and observed ETa values were highly consistent with
a total difference of 18.26% in the whole growing season. Therefore, the
ETa retrieval method based on remote sensing technology could satisfy
the requirements for regional ETa estimation over the Songnen Plain. The
total ETa over the Songnen Plain in the 2008 growing season ranged from
182.7 to 1002.4 mm, and the average value for the whole study area was
591.1 +/- 122.2 mm (standard deviation). ETa exhibited obvious spatial
variation, gradually increasing from low values in the southwest to
higher values in the east and northeast. Monthly ETa varied with
meteorological conditions, land covers, root-zone soil moisture, and
vegetation phenology. Higher monthly ETa values appeared in June, July,
and August with a maximum value of 139.5 mm observed in July. The
average monthly ETa for water-body, woodland, and wetland was much
higher than cropland and grassland during the growing season. Grassland
obtained the lowest monthly ETa due to the scarcity of rainfall and
lower groundwater level.
[Show abstract][Hide abstract] ABSTRACT: Marshes in the Sanjiang Plain of Northeast China have undergone dramatic loss and fragmentation over the past decades. This
paper analyzed the loss and fragmentation of these marshes for the period 1954–2005 using historical land-cover information
and remote sensing data. In 1954, marshes covered one-third of the total land area but have decreased by 77% over the 50year
period. Results showed two distinct periods of impact: 1954–1986 and 1987–2005. In the earlier period, the number of marsh
patches fell from 4,799 to 1,476 (−69.2%), and total marsh area decreased from 35,270km2 to 13,893km2 (−60.6%). In the latter period, the number of marshes declined from 1,476 to 1,037 (−29.7%), and the total area decreased
from 13,893km2 to 8,100km2 (−41.7%). The rapid decrease in the number and area of marshes during 1954–1986 was largely attributed to extensive agricultural
reclamation under the “Food First” agricultural policy. This resulted in many negative ecological consequences. In contrast,
the slower reduction of marsh areas during 1987–2005 was due to the implementation of governmental policies for protecting
and restoring marshes. Increasing air temperature would otherwise have enhanced crop yields and stimulated the conversion
of marsh into crops.
KeywordsAgriculture–Climatic changes–Marsh loss–Policy–Remote sensing
[Show abstract][Hide abstract] ABSTRACT: Spectral characteristics and the magnitudes of light absorption by suspended particulate matter were determined by spectrophotometry in this optically complex Lake Chagan waters for the purpose of surveying the natural variability of the absorption coefficients to parameterize the bio-optical models for converting satellite or in-situ water reflectance signatures into water quality information. Experiments were carried out on seasonal frozen Lake Chagan, one representative inland case-2 water body in Northeast of China. Particulate absorption properties analyzed using the field data on July 15th and October 12th 2009 were measured using the quantitative filter technique to produce absorption spectra containing several fractions that could be attributed to two main optical active constituents (OACs) phytoplankton pigments and non-algal particulates (mineral sediments, and organic detritus). Results suggested that the suspended particulate matter (SPM) concentration was higher while phytoplankton biomass (chlorophyll-a concentration) was lower in July and that in October. The spectral shape of total suspended particulate matter resembled that of non-algal particulates which contributed greater than phytoplankton in total particulate absorption during both periods. An obvious absorption peak occurring at around 440 nm exhibited an increase in phytoplankton contribution in October. Non-algal particulate absorption at 440 nm (a(NAP) (440)) had better correlation with total suspended particulate matter concentration than that with chlorophyll-a over the two periods. Light absorption by phytoplankton pigments in the Chagan lake region was generally lower than that of non-algal components. Chl. a dominating phytoplankton pigment composition functioned exponentially with its absorption coefficients at 440 and 675 nm specifically, the average values of which in July were 0.146 8 m2 x mg(-1) and 0.050 3 respectively while in October they were 0.153 3 and 0.013 2 m2 x mg(-1) varying regionally and seasonally due to the changes in specific composition, light and nutrient conditions.
Guang pu xue yu guang pu fen xi = Guang pu 01/2011; 31(1):162-7. · 0.27 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Lake Chagan represents a complex situation of major optical constituents
and emergent spectral signals for remote sensing analysis of water
quality in the Songnen Plain. As such it provides a good test of the
combined radiometric correction methods developed for optical remote
sensing data to monitor water quality. Landsat thematic mapper (TM) data
and in situ water samples collected concurrently with satellite overpass
were used for the analysis, in which four important water quality
parameters are considered: chlorophyll-a, turbidity, total dissolved
organic matter, and total phosphorus in surface water. Both empirical
regressions and neural networks were established to analyze the
relationship between the concentrations of these four water parameters
and the satellite radiance signals. It is found that the neural network
model performed at better accuracy than empirical regressions with TM
visible and near-infrared bands as spectral variables. The relative root
mean square error (RMSE) for the neural network was < 10%, while the
RMSE for the regressions was less than 25% in general. Future work is
needed on establishing the dynamic characteristic of Chagan Lake water
quality with TM or other optical remote sensing data. The algorithms
developed in this study need to be further tested and refined with
multidate imagery data
[Show abstract][Hide abstract] ABSTRACT: In remote chlorophyll-a (chl-a) retrieval in Case-II waters, there always exists some limitations from empirical parameters in the estimating models and uncertainties from the chl-a specific absorption coefficient. In this study, we present a newly improved three-band model in a case study in Shitoukoumen Reservoir for direct calculation of chl-a without any empirical parameters derived from regression. Inherent optical properties of the reservoir were investigated to determine parameters in the improved model. Results show that taking into account variations from chl-a specific absorption, as well as absorption by pure water, the improved model performed well in the field study used for model calibration and also robustly in two other field studies. The findings underline the rationale behind the model and demonstrate a potentially general solution for assessing chl-a in Case-II waters. In the evaluation of P6 data, the traditional near-infrared-to-red reflectance ratio (band 4/band 3) could estimate chl-a with root mean square error below 2.17μg l, which confirms that P6 data could be used potentially to determine chl-a concentration.
International Journal of Remote Sensing 09/2010; 31:4609-4623. · 1.36 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: This study applied a multivariate model based on three simulated sensors to estimating water quality variables in Shitoukoumen
Reservoir, Changchun City, Jilin Province, China, including concentration of total suspended matter, concentration of chlorophyll-a and non-pigment matter absorption. Two field campaigns for spectra measurements with a total of 40 samples were carried out
on June 13 and September 23, 2008. The in-situ spectra were recalculated to the spectral bands and sensitivities of the instruments
applied in this paper, i.e. Landsat TM, Alos and P6, by using the average method. And the recalculated spectra were used for
estimating water quality variables by the single model and multivariate model. The results show that the multivariate model
is superior to the single model as the multivariate model takes the combined effects of water components into consideration
and can estimate water quality variables simultaneously. According to R
2 and RMSE, Alos is superior to other sensors for water quality variables estimation although the precision of non-pigment matter absorption
inversion performed the second.
Keywordsremote sensing-inland water quality-Alos-water components absorption-absorption coefficient
Chinese Geographical Science 08/2010; 20(4):337-344. · 0.73 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Atmospheric water vapor (AWV) content is closely related to precipitation that in turn has effects on the productivity of agricultural, forestry and range land. MODIS images have been used for AWV retrieval, and the method uses either two (0.841-0.876 mum and 0.915-0.965 mum) or three (0.841-0.876, 0.915-0.965 and 1.230-0-1.250 mum) MODIS channel ratios. We applied both methods to the MODIS data over Northeast China acquired from June to August, 2008 to retrieve AWV content, and the results were validated on ground observed data from 10 radio sonde stations characterized by various land cover. The bulk results indicate that the two-channel ratio outperformed the three-channel ratio based on the coefficient of determination R2 = 0.81 vs. 0.78. The validation results for individual land cover types also support this observation with R2 = 0.92 vs. 0.84 for woodland, 0.82 vs. 0.79 for cropland, 0.90 vs. 0.86 for grassland and 0.673 vs. 0.669 for urban areas. The spatial distribution of AWV derived using the two-channel ratio method was correlated to land-use classification data, and a high correlation was evident when other conditions were similar. With the exception of dry cropland, the amount of average water vapor content over different land use types demonstrates a consistent order: water-body > paddy-field > woodland > grassland > barren for the analyzed multi-temporal MODIS data. This order partially matches the evapotranspiration pattern of underlying surface, and future work is required for analyzing the association of the landscape pattern with AWV in the region.
Proceedings of SPIE - The International Society for Optical Engineering 08/2010;