Jia Du

Wetlands shrinkage, fragmentation and their links to agriculture in the Muleng-Xingkai Plain, China.

In the past five decades, the wetlands in the Muleng-Xingkai Plain, Northeast China, have experienced rapid shrinkage and fragmentation. In this study, wetlands cover change and agricultural cultivation were investigated through a time series of thematic maps from 1954, and Landsat satellite images representing the last five decades (1976, 1986, 1995, 2000, and 2005). Wetlands shrinkage and fragmentation were studied based on landscape metrics and the land use changes transition matrix. Furthermore, the driving forces were explored according to socioeconomic development and major natural environmental factors. The results indicate a significant decrease in the wetlands area in the past five decades, with an average annual decrease rate of 9004 ha/yr. Of the 625,268 ha of native wetlands in 1954, approximately 64% has been converted to other land use types by 2005, of which conversion to cropland accounts for the largest share (83%). The number of patches decreased from 1272 (1954) to 197 (1986) and subsequently increased to 326 (2005). The mean patch size changed from 480 ha (1954) to 1521 ha (1976), and then steadily decreased to 574 ha (2005). The largest patch index (total core area index) indicates wetlands shrinkage with decreased values from 31.73 (177,935 ha) to 3.45 (39,421 ha) respectively. Climatic changes occurred over the study period, providing a potentially favorable environment for agricultural development. At the same time population, groundwater harvesting, and fertilizer application increased significantly, resulting in wetlands degradation. According to the results, the shrinkage and fragmentation of wetlands could be explained by socioeconomic development and secondarily aided by changing climatic conditions.

Retrieval of total suspended matter (TSM) and chlorophyll-a (Chl-a) concentration from remote-sensing data for drinking water resources

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 (R2 = 0.94, 0.91) than their performance on Chl-a concentration (R2 = 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 (R2 = 0.91, 0.82) and TSM (R2 = 0.98, 0.94) concentration estimation through in situ collected spectra; the same trend followed for IRS-P6 imagery data (R2 = 0.75 and 0.90 for Chl-a; R2 = 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.

Evapotranspiration estimation based on MODIS products and surface energy balance algorithms for land (SEBAL) model in Sanjiang Plain, Northeast China

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.

Spatiotemporal Variations of Lake Surface Temperature across the Tibetan Plateau Using MODIS LST Product

Satellite remote sensing provides a powerful tool for assessing lake water surface temperature (LWST) variations, particularly for large water bodies that reside in remote areas. In this study, the MODIS land surface temperature (LST) product level 3 (MOD11A2) was used to investigate the spatiotemporal variation of LWST for 56 large lakes across the Tibetan Plateau and examine the factors affecting the LWST variations during 2000–2015. The results show that the annual cycles of LWST across the Tibetan Plateau ranged from −19.5 °C in early February to 25.1 °C in late July. Obvious diurnal temperature differences (DTDs) were observed for various lakes, ranging from 1.3 to 8.9 °C in summer, and large and deep lakes show less DTDs variations. Overall, a LWST trend cannot be detected for the 56 lakes in the plateau over the past 15 years. However, 38 (68%) lakes show a temperature decrease trend with a mean rate of −0.06 °C/year, and 18 (32%) lakes show a warming rate of (0.04 °C/year) based on daytime MODIS measurements. With respect to nighttime measurements, 27 (48%) lakes demonstrate a temperature increase with a mean rate of 0.051 °C/year, and 29 (52%) lakes exhibit a temperature decrease trend with a mean rate of −0.062 °C/year. The rate of LWST change was statistically significant for 19 (21) lakes, including three (eight) warming and 17 (13) cooling lakes for daytime (nighttime) measurements, respectively. This investigation indicates that lake depth and area (volume), attitude, geographical location and water supply sources affect the spatiotemporal variations of LWST across the Tibetan Plateau.

Using Partial Least Squares-Artificial Neural Network for Inversion of Inland Water Chlorophyll-a

Accurate remote estimation of chlorophyll-a (CHL) concentration for turbid inland waters is a challenging task due to their optical complexity. In situ spectra (n=666) measured with ASD and Ocean Optics spectrometers from three drinking water sources in Indiana, USA, were used to calibrate the partial least squares model (PLS), artificial neural network model (ANN), and the three-band model (TBM) for CHL estimates; model performances are validated with three independent datasets (n=360) from China. The PLS-ANN model resulted in accurate model calibration ( R2=0.94; Range=0.2-296.6 μg/l of CHL), outperforming the PLS (R2=0.87), ANN (R2=0.91), and TBM (R2=0.86). With an independent validation dataset, the PLS-ANN yielded relatively high accuracy (RMSE: 6.12 μg/l; rRMSE=42.12%; range=0.45-97.2 μg/l of CHL), while TBM yielded acceptable accuracy (RMSE: 8.85 μg/l; rRMSE=63.21%). With simulated ESA/MERIS and EO-1/Hyperion spectra, the PLS-ANN also (MERIS: R2=0.84; Hyperion: R2=0.88) outperforms the TBM (MERIS: R2=0.69; Hyperion: R2=0.76) for model calibration. For validation, the PLS-ANN achieves good performance with simulated spectra (MERIS: RMSE=7.83 μg/l, rRMSE=48.79%; Hyperion: RMSE=6.98 μg/l, rRMSE=45.57%) as compared to the TBM (MERIS: RMSE=10.39 μg/l, rRMSE=68.92%; Hyperion: RMSE=9.54 μg/l, rRMSE=65.35%). Nevertheless, considering the large and diverse datasets, the TBM is a robust semiempirical algorithm. Based on our observations, both the PLS-ANN and TBM are effective approaches for CHL estimation in turbid waters.

Land use/land cover (LULC) classification with MODIS time series data and validation in the Amur River Basin

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.

Spatial Distribution of Diffuse Attenuation of Photosynthetic Active Radiation and Its Main Regulating Factors in Inland Waters of Northeast China

Light availability in lakes or reservoirs is affected by optically active components (OACs) in the water. Light plays a key role in the distribution of phytoplankton and hydrophytes, thus, is a good indicator of the trophic state of an aquatic system. Diffuse attenuation of photosynthetic active radiation (PAR) (Kd(PAR)) is commonly used to quantitatively assess the light availability. The PAR and the concentration of OACs were measured at 206 sites, which covered 26 lakes and reservoirs in Northeast China. The spatial distribution of Kd(PAR) was depicted and its association with the OACs was assessed by grey incidences(GIs) and linear regression analysis. Kd(PAR) varied from 0.45 to 15.04 m−1. This investigation revealed that reservoirs in the east part of Northeast China were clear with small Kd(PAR) values, while lakes located in plain areas, where the source of total suspended matter (TSM) varied, displayed high Kd(PAR) values. The GIs and linear regression analysis indicated that the TSM was the dominant factor in determining Kd(PAR) values and best correlated with Kd(PAR) (R2 = 0.906, RMSE = 0.709). Most importantly, we have demonstrated that the TSM concentration is a reliable measurement for the estimation of the Kd(PAR) as 74% of the data produced a relative error (RE) of less than 0.4 in a leave-one-out cross validation (LOO-CV) analysis. Spatial transferability assessment of the model also revealed that TSM performed well as a determining factor of the Kd(PAR) for the majority of the lakes. However, a few exceptions were identified where the optically regulating dominant factors were chlorophyll-a (Chl-a) and/or the chromophroic dissolved organic matter (CDOM). These extreme cases represent lakes with exceptionally clear waters.

Characteristics and sources analysis of riverine chromophoric dissolved organic matter in Liaohe River, China

Chromophoric dissolved organic matter (CDOM) in riverine systems can be affected by environmental conditions and land-use, and thus could provide important information regarding human activities in surrounding landscapes. The optical properties of water samples collected at 42 locations across the Liaohe River (LHR, China) watershed were examined using UV-Vis and fluorescence spectroscopy to determine CDOM characteristics, composition and sources. Total nitrogen (TN) and total phosphorus (TP) concentrations at all sampling sites exceeded the GB3838-2002 (national quality standards for surface waters, China) standard for Class V waters of 2.0 mg N/L and 0.4 mg P/L respectively, while trophic state index (TSIM) indicated that all the sites investigated were mesotrophic, 64% of which were eutrophic at the same time. Redundancy analysis showed that total suspended matter (TSM), dissolved organic carbon (DOC), and turbidity had a strong correlation with CDOM, while the other parameters (Chl a, TN, TP and TSIM) exhibited weak correlations with CDOM absorption. High spectral slope values and low SUVA254 (the specific UV absorption) values indicated that CDOM in the LHR was primarily comprised of low molecular weight organic substances. Analysis of excitation-emission matrices contour plots showed that CDOM in water samples collected from upstream locations exhibited fulvic-acid-like characteristics whereas protein-like substances were most likely predominant in samples collected in estuarine areas and downstream from large cities. These patterns were interpreted as indicative of water pollution from urban and industrial activities in several downstream sections of the LHR watershed.

Land use/cover characterizaitoin with MODIS time series data with hybrid classification mothed over Australia for 2001 and 2003

Improved and up-to-date land use/land cover (LULC) data sets are needed over the whole country of Australia to support science and policy applications focused on understanding the role and response of the LULC to environmental change. The main goal of this study was to map LULC in Australia using MODIS 250 m Normalized Difference Vegetation Index (NDVI), Land Surface Vegetation Index (LSWI) and reflectance time series data of 2000 and 2003. NDVI time-series were filtered by the Savitzky-Golay algorithm in the present study to smooth out noise. A combination of unsupervised ISODATA and a hierarchical decision tree classification were performed on 2 years 12-month time-series MODIS data. Also, Australian Vegetation Map and other land use/land cover data set were used as labeling reference during the classification process. The MODIS land cover products were evaluated using existing land use/cover data derived from Landsat TM as reference data (AUS-2000), also LULC information derived from 11 scenes of Landsat-5 TM data were used as validation data source. The overall classification accuracy was 76.4%. It turned out that our result is acceptable because the relative high resolution of MODIS data and more prior knowledge was applied.

Application of wavelet transform on hyperspectral reflectance for soybean lai estimation in the songnen plain, China

In this study, we present spectral measurements of soybean LAI and their estimation from reflectance spectra data in Songnen Plain. Soybean canopy reflectance and its derivative were subsequently used in a linear regression analysis against LAI on one by one spectral reflectance. It was found that determination coefficient for LAI was high in blue, red and near infrared spectral region, and it was low in green spectral region, however LAI obtained its high determination coefficient in blue, green and red edge spectral region, especially in red edge region. Regression models were established based upon spectral vegetation indices and wavelet energy coefficient. It was found that wavelet transforms is an effective method for hyperspectral reflectance variables extraction to retrieve LAI, and the best multivariable regressions R2 up to 0.90 for LAI. Further studies are still needed to refine the methods for determining and estimating corn bio-physical/chemical parameters or other physiological parameters of different vegetation as well in the future.