Qinxue Wang

Land cover classification from MODIS EVI times-series data using SOM neural network

A high-dimensional dataset was built with time-series data of vegetation indexes derived from a Terra-Moderate Resolution Imaging Spectroradiometer (MODIS) sensor used for land use/cover classification. The self-organizing map (SOM) neural network technique can reduce the dimensionality of high-dimensional data, yet keep the same topological characters in the low-dimension space after dimension reduction. In this paper, we first employed the SOM neural network technique to classify land cover types using a 17-dimensional dataset that was generated from 16-day interval MODIS Enhanced Vegetation Index (EVI) data with a spatial resolution of 500 m in eastern China during the growing period of plants. Then, we defined an unlabelled class of neuron. Pixels matched to this type of neuron were regarded as unclassified land cover types, so that we could remove the poorly classified areas. Finally, the classification results were compared with those of the maximum likelihood classification (MLC) method. Comparison showed that the accuracy of the former exceeded that of the latter in classifying a high-dimensional dataset.

A New Method to Define the VI-Ts Diagram Using Subpixel Vegetation and Soil Information: A Case Study over a Semiarid Agricultural Region in the North China Plain.

The VI-Ts diagram determined by the scatter points of the vegetation index (VI) and surface temperature (Ts) has been widely applied in land surface studies. In the VI-Ts diagram, dry point is defined as a pixel with maximum Ts and minimum VI, while wet point is defined as a pixel with minimum Ts and maximum VI. If both dry and wet points can be obtained simultaneously, a triangular VI-Ts diagram can be readily defined. However, traditional methods cannot define an ideal VI-Ts diagram if there are no full ranges of land surface moisture and VI, such as during rainy season or in a period with a narrow VI range. In this study, a new method was proposed to define the VI-Ts diagram based on the subpixel vegetation and soil information, which was independent of the full ranges of land surface moisture and VI. In this method, a simple approach was firstly proposed to decompose Ts of a given pixel into two components, the surface temperatures of soil (Tsoil) and vegetation (Tveg), by means of Ts and VI information of neighboring pixels. The minimum Tveg and maximum Tsoil were then used to determine the wet and dry points respectively within a given sampling window. This method was tested over a 30 km × 30 km semiarid agricultural area in the North China Plain through 2003 using Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) and MODerate-resolution Imaging Spectroradiometer (MODIS) data. The wet and dry points obtained from our proposed method and from a traditional method were compared with those obtained from ground data within the sampling window with the 30 km × 30 km size. Results show that Tsoil and Tveg can be obtained with acceptable accuracies, and that our proposed method can define reasonable VI-Ts diagrams over a semiarid agricultural region throughout the whole year, even for both cases of rainy season and narrow range of VI.

The impacts of a linear wastewater reservoir on groundwater recharge and geochemical evolution in a semi-arid area of the Lake Baiyangdian watershed, North China Plain

Sewage leakage has become an important source of groundwater recharge in urban areas. Large linear wastewater ponds that lack anti-seepage measures can act as river channels that cause the deterioration of groundwater quality. This study investigated the groundwater recharge by leakage of the Tanghe Wastewater Reservoir, which is the largest industrial wastewater channel on the North China Plain. Additionally, water quality evolution was investigated using a combination of multivariate statistical methods, multi-tracers and geochemical methods. Stable isotopes of hydrogen and oxygen indicated high levels of wastewater evaporation. Based on the assumption that the wastewater was under an open system and fully mixed, an evaporation model was established to estimate the evaporation of the wastewater based on isotope enrichments of the Rayleigh distillation theory using the average isotope values for dry and rainy seasons. Using an average evaporation loss of 26.5% for the input wastewater, the estimated recharge fraction of wastewater leakage and irrigation was 73.5% of the total input of wastewater. The lateral regional groundwater inflow was considered to be another recharge source. Combing the two end-members mix model and cluster analysis revealed that the mixture percentage of the wastewater decreased from the Highly Affected Zone (76%) to the Transition Zone (5%). Ion exchange and redox reaction were the dominant geochemical processes when wastewater entered the aquifer. Carbonate precipitation was also a major process affecting evolution of groundwater quality along groundwater flow paths.

Evaluating MODIS phenology product for rotating croplands through ground observations

Abstract The MODIS global vegetation phenological product (MCD12Q2) has been widely evaluated for forestlands. However, little is reported for croplands. We evaluate the MODIS phenological product during 2001 to 2009 in combination with ground-based phenological data for wheat/maize rotation systems in the North China Plain (NCP). The spatial patterns of phenological dates for winter wheat and summer maize are consistent between MODIS and site-observed data across the NCP. The results show that MODIS phenological dates correlate well with observed phenological dates for both winter wheat ( R 2 = 0.90 ) and summer maize ( R 2 = 0.93 ) at five ground stations, which testified that the MODIS vegetation phenology product can realistically detect the key growing stages of winter wheat (greening, heading, grain-filling, and maturity) and summer maize (emergence, tasseling, grain-filling, and maturity), respectively. Planting areas identified from the MODIS phenology product are also compared with statistical data from 48 administrative zones in 2009. Correlation suggests the acceptance of MODIS identifiable area having R 2 = 0.73 , 0.65 for winter wheat – summer maize, respectively. Meanwhile, a slight underestimation in planting area, by 8% for winter wheat and by 5% for summer maize, is found across the NCP. Overall evaluation criteria suggest that the MODIS MCD12Q2 product can provide generally reliable information of crop phenology for double crop areas.

Land-cover Classification Using ASTER Multi-band Combinations Based on Wavelet Fusion and SOM Neural Network

In this study, we developed a land-cover classification methodology using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) visible near-infrared (VNIR), shortwave infrared (SWIR), and thermal infrared (TIR) band combinations based on wavelet fusion and the selforganizing map (SOM) neural network methods, and compared the classification accuracies of different combinations of ASTER multi-band data. A wavelet fusion concept named ARSIS (Amelioration de la Resolution Spatiale par Injection de Structures) was used to fuse ASTER data in the preprocessing stage. In order to apply the wavelet fusion method to ASTER data, the principal components of ASTER VNIR data were computed. The first principal component was used as the base image for wavelet fusion. In our experiments, the spatial resolution of ASTER VNIR, SWIR, and TIR data was adjusted to the same 15 m. SOM classification accuracy was increased from 83 percent to 93 percent by this fusion, and classification accuracy increased along with the increase of band numbers. Classification accuracy reaches the highest value when all 14 bands are used, but classification accuracy closely approached the highest value when three VNIR bands, three SWIR bands, and two TIR bands were used. A similar tendency was also obtained by the maximum likelihood classification (MLC) method, but the classification accuracies of MLC over all band combinations were considerably obviously lower than those obtained by the SOM method.

Recent trends in nitrogen flows with urbanization in the Shanghai megacity and the effects on the water environment

The objectives of this study are to diagnose and prevent environmental problems that threaten urban sustainability, the impact of changes in lifestyle (diet, domestic sanitation, and motorization), and production style (agriculture, industry, and services) with the rapid urbanization on regional nitrogen (N) flows, and the water environment was quantitatively evaluated. The megacity Shanghai was chosen as a case study to investigate the temporal changes in nitrogen flow during 1980–2008 by a multidisciplinary approach (a field survey, a regional nitrogen mass balance model, input-output analysis, etc.). Although the total potential nitrogen load in Shanghai has decreased in the 2000s and water pollution problems seem to have improved, the problem has shifted and expanded to affect a wider area through the food/product chain and water/air movement. Further effective solutions that aim at material cycles are necessary and have to be implemented on a large scale.

Relationship between Evapotranspiration and Land Surface Temperature under Energy- and Water-Limited Conditions in Dry and Cold Climates

Remotely sensed land surface temperature- (LST-) dependent evapotranspiration (ET) models and vegetation index- (VI-) LST methods may not be suitable for ET estimation in energy-limited cold areas. In this study, the relationship of ET to LST was simulated using the process-based Simultaneous Heat and Water (SHAW) model for energy- and water-limited conditions in Mongolia, to understand the differences in ET processes under these two limiting conditions in dry and cold climates. Simulation results from the SHAW model along with ground observational data showed that ET and LST have a positive relationship when air temperature () is less than or equal to the temperature () above which plants transpire and have a negative relationship when is greater than under the energy-limited condition. However, ET and LST maintain a negative relationship with changes in under the water-limited condition. The differences in the relationship between ET and LST under the energy-limited and water-limited conditions could be attributed to plant transpiration and energy storage in moist/watered soil and plants. This study suggests that different strategies should be used to estimate ET under the energy-limited condition in dry and cold climates.

Effect of reforestation on nitrogen and phosphorus dynamics in the catchment ecosystems of subtropical China: the example of the Hanjiang River basin

BACKGROUND To enable effective management and decision making for the sustainable use of water resources, we successfully integrated factors such as dams, land use and soil properties as well as management factors in the Hanjiang River basin, a subtropical catchment of China, into the SWAT model to simulate water cycles as well as the distribution, movement, and transformations of nutrients. RESULTS The accuracy of the model was validated by monitoring data over the Hanjiang River. The validated model was then used to evaluate the effects of the Reforestation of Cultivated Land (RFCL) initiative. The simulation results showed that RFCL would cause an obvious decrease in surface runoff (-23.6%, P < 0.01) but an increase in groundwater (71.8%, P < 0.01) and percolation out of the soil (24.7%, P < 0.01). The total water yield does not change significantly (-4.4%), but the decrease in total sediment loading is substantial (-56.2%, P < 0.01). The simulation results also show that RFCL would greatly decrease the organic N (-42.6%, P < 0.01), NO(3) yield in surface flow (-37.1%, P < 0.01), and the NO(3) yield in subsurface flow (-25.5%, P < 0.01), whereas the NO(3) yield in groundwater flow would increase (107%, P < 0.01). In terms of phosphorus, RFCL would cause both organic phosphorus (-38.2%, P < 0.01) and the phosphorus yield from the soil (-33.3%, P < 0.01) to decrease. CONCLUSION The results suggest that RFCL is an effective policy for watershed environment management, which might have a relatively small effect on river discharge but that the purification effects on water quality in the river would be remarkable.

Land cover classification using moderate resolution imaging spectrometer-enhanced vegetation index time-series data and self-organizing map neural network in Inner Mongolia, China

The Moderate Resolution Imaging Spectroradiometer (MODIS) data offers a unique combination of spectral, temporal, and spatial resolution in comparison to other global sensors. The MODIS Enhanced Vegetation Index (EVI) product has several advantages, which make it suitable for regional land cover mapping. This paper investigates the application of MODIS EVI time-series data for mapping temperate arid and semi-arid land cover at a moderate resolution (500 m), for regional land-cover/land-use monitoring purposes. A 16-day composite EVI time-series data for 2003 (22 March 2003 - 30 September 2003) was adopted for the study. A land cover map was generated for the Inner Mongolia Autonomous Region using 7 tiles of MODIS EVI time-series data and Self-Organizing Map (SOM) neural network classification. Land-use GIS data, Landsat TM/ETM, and ASTER data were employed as reference data. The results show that the overall accuracy of land cover classification is about 84% with a Kappa coefficient of 0.8170. These results demonstrate that the SOM neural network model could work well for the multi-temporal MODIS EVI data, and suggest a potential of using MODIS EVI time-series remote sensing data to monitor desertification in Inner Mongolia with limited ancillary data and little labor-input in comparison with using high-spatial resolution remote sensing data.

Recent trends of nitrogen flow of typical agro-ecosystems in China--major problems and potential solutions.

BACKGROUND To diagnose problems that threaten regional sustainability and to devise appropriate treatment measures in Chinas agro-ecosystems, a study was carried out to quantify the nitrogen (N) flow in Chinas typical agro-ecosystems and develop potential solutions to the increasing environmental N load. RESULTS The analysis showed that owing to human activity in the agro-ecosystems of Changjiang River Basin the mean total input of anthropogenic reactive N (i.e. chemical fertiliser, atmospheric deposition and bio-N fixation) increased from 4.41 × 10(9) kg-N in 1980 to 7.61 × 10(9) kg-N in 1990 and then to 1.43 × 10(10) kg-N in 2000, with chemical fertiliser N being the largest contributor to N load. Field investigation further showed that changes in human behaviour and rural urbanisation have caused rural communities to become more dependent on chemical fertilisers. In rural regions, around 4.17 kg-N of per capita annual potential N load as excrement was returned to farmlands and 1.38 kg-N directly discharged into river systems, while in urbanised regions, around 1.00 kg-N of per capita annual potential N load as excrement was returned to farmlands and 5.62 kg-N discharged into river systems in urban areas. CONCLUSION The findings of the study suggest that human activities have significantly altered the N cycle in agro-ecosystems of China. With high population density and scarce per capita water resources, non-point source pollution from agro-ecosystems continues to put pressure on aquatic ecosystems. Increasing the rate of organic matter recycling and fertiliser efficiency with limited reliance on chemical fertilisers might yield tremendous environmental benefits.