Cuizhen Wang

Mapping paddy rice with multitemporal ALOS/PALSAR imagery in southeast China

Mapping rice cropping areas with optical remote sensing is often a challenge in tropical and subtropical regions because of frequent cloud cover and rainfall during the rice growing season. Synthetic aperture radar (SAR) is a potential alternative for rice mapping because of its all-weather imaging capabilities. The recent Phased Array-type L-band SAR (PALSAR) sensor onboard the Advanced Land Observing Satellite (ALOS) acquires multipolarization and multitemporal images that are highly suitable for rice mapping. In this pilot study, we demonstrate the feasibility of this sensor in mapping the rice planting area in Zhejiang Province, southeast China. High-resolution ALOS/PALSAR images were acquired at three rice growing stages (transplanting, tillering and heading) and were applied in a support vector machine (SVM) classifier to map rice and other land use surfaces. The results show that, based on the 1:10 000 land use/land cover (LULC) survey map, the rice fields can be mapped with a conditional Kappa value of 0.87 and at users and producers accuracies of 90% and 76%, respectively. The large commission error primarily came from confusion between rice and dryland crops or orchards because of their similar backscatter amplitudes in the rice growing season. The relatively high rice mapping accuracy in this study indicates that the new ALOS/PALSAR data could provide useful information in rice cropping management in subtropical regions such as southeast China.

Characterizing L-Band Scattering of Paddy Rice in Southeast China With Radiative Transfer Model and Multitemporal ALOS/PALSAR Imagery

Rice is a major food supply in southeast China. With increased population and urbanization, reliable rice mapping is critical in this region. Because of frequent cloud cover and precipitation during the rice-growing season, it is difficult to conduct large-area rice monitoring with optical remote sensing techniques. L-band synthetic aperture radar (SAR), with its all-weather day and night imaging and canopy penetration capabilities, provides a unique alternative. In this study, a first-order radiative transfer model was developed to simulate L-band scattering properties of paddy rice. Three Advanced Land Observing Satellite (ALOS)/Phased-Array-Type L-band Synthetic Aperture Radar (PALSAR) images in dual-polarization mode (HH and HV) acquired in early tillering (June 28, 2007), tillering (August 13, 2007), and heading (September 28, 2007) stages were processed to test the temporal variation of rice backscatter. It was found that plant height and leaf mass amount were the two major structural parameters that contributed to rice backscatter in PALSAR images. The variation of the simulated HH backscatter matched with PALSAR observations in sample fields, although the simulated backscatter coefficients were around 3 dB lower than image-extracted values. Leaf volume scattering and leaf-ground double bounce were found as the two major scattering components in L-band HH polarization and increased with leaf layer height and density. This paper demonstrated that L-band HH backscatter was more sensitive to rices structural variation than the VV backscatter and may therefore be more useful in rice mapping and modeling studies.

Biophysical estimation in tropical forests using JERS-1 SAR and VNIR imagery. II. Aboveground woody biomass

Accurate estimates of aboveground biomass in tropical forests are important in carbon sequestration and global change studies. Tropical forest biomass estimation with microwave remote sensing is limited because of the strong scattering and attenuation properties of the green canopy. In this study a microwave/optical synergistic model was developed to quantify these effects to Synthetic Aperture Radar (SAR) signals and to better estimate woody structures, which are closely related to aboveground biomass. With a Leaf Area Index (LAI) retrieved from Japan Earth Resources Satellite (JERS)‐1 Very Near Infrared Radiometer (VNIR) imagery, leaf scattering and attenuation to woody scattering were quantified and removed from the total backscatter in a modified canopy scattering model. Woody scattering showed high sensitivity to biomass >100 tonnes/ha in tropical forests. Tree height and stand density were derived from the JERS‐1 SAR image with a root mean square error (RMSE) of 4 m and 161 trees/ha, respectively. Aboveground biomass was calculated using a general allometric equation. Biomass in secondary dry dipterocarps (Dipterocarpaceae family of tropical lowland deciduous trees) was overestimated. The modelled biomass in mixed deciduous and dry evergreen forests fit better with ground measurements. In mountainous areas with steep slopes, the topographic effects in the SAR image could not be properly corrected and therefore the results are unreliable.

Vegetation greenness trend (2000 to 2009) and the climate controls in the Qinghai-Tibetan Plateau

Abstract The Qinghai-Tibetan Plateau has been experiencing a distinct warming trend, and climate warming has a direct and quick impact on the alpine grassland ecosystem. We detected the greenness trend of the grasslands in the plateau using Moderate Resolution Imaging Spectroradiometer data from 2000 to 2009. Weather station data were used to explore the climatic drivers for vegetation greenness variations. The results demonstrated that the region-wide averaged normalized difference vegetation index (NDVI) increased at a rate of 0.036     yr − 1 . Approximately 20% of the vegetation areas, which were primarily located in the northeastern plateau, exhibited significant NDVI increase trend ( p -value < 0.05 ). Only 4% of the vegetated area showed significant decrease trends, which were mostly in the central and southwestern plateau. A strong positive relationship between NDVI and precipitation, especially in the northeastern plateau, suggested that precipitation was a favorable factor for the grassland NDVI. Negative correlations between NDVI and temperature, especially in the southern plateau, indicated that higher temperature adversely affected the grassland growth. Although a warming climate was expected to be beneficial to the vegetation growth in cold regions, the grasslands in the central and southwestern plateau showed a decrease in trends influenced by increased temperature coupled with decreased precipitation.

Assessment of Tropical Forest Degradation with Canopy Fractional Cover from Landsat ETM+ and IKONOS Imagery

Abstract Tropical forests are being subjected to a wide array of disturbances in addition to outright deforestation. Selective logging is one of the most common disturbances ongoing in the Amazon, which results in significant changes in forest structure and canopy integrity. Assessing forest canopy fractional cover (fc) is one way of measuring forest degradation caused by selective logging. In this study we applied a linear mixture model to a vegetation index domain to map canopy fractional cover in tropical forests in the Amazonian state of Mato Grosso, Brazil. The modified soil adjusted vegetation index (MSAVI) was selected as the optimal vegetation index in the model because it is most linearly related to green canopy abundance up to leaf area index = 4.0. In the canopy fc map derived from the Landsat Enhanced Thematic Mapper Plus (ETM+) image, the fc distribution ranged from 0 to 0.4 in clear-cut areas, higher than 0.8 in undisturbed forests, and a wider range of 0.3–1.0 in degraded forests. The fc ma...

A digital earth prototype system: DEPS/CAS

Abstract Digital Earth is an information expression of the real Earth, and is a new way of understanding the Earth in the twenty-first century. This paper introduces a Digital Earth Prototype System (DEPS) developed at the Chinese Academy of Sciences (CAS) and supported by the Knowledge Innovation Program of the Chinese Academy of Sciences. Discussions are made to the theoretical model and technical framework of the Digital Earth, and its related key technologies on spatial information processing, spatial data warehouse technology, virtual reality technology, high-performance and parallel computing. The DEPS consists of seven sub-systems including the spatial data, metadata, model database, Grid geoscience computing, spatial information database, maps service and virtual reality. Meanwhile, we developed a series of application systems such as the environment monitoring for the Olympic Games 2008 in Beijing, natural disasters evaluation, digital city, digital archeology, Asia regional aerosol and climate change. The DEPS/CAS displayed the application ability and potential of the Digital Earth in three levels: the global, national and regional.

Object-Based Crop Classification with Landsat-MODIS Enhanced Time-Series Data

Cropland mapping via remote sensing can provide crucial information for agri-ecological studies. Time series of remote sensing imagery is particularly useful for agricultural land classification. This study investigated the synergistic use of feature selection, Object-Based Image Analysis (OBIA) segmentation and decision tree classification for cropland mapping using a finer temporal-resolution Landsat-MODIS Enhanced time series in 2007. The enhanced time series extracted 26 layers of Normalized Difference Vegetation Index (NDVI) and five NDVI Time Series Indices (TSI) in a subset of agricultural land of Southwest Missouri. A feature selection procedure using the Stepwise Discriminant Analysis (SDA) was performed, and 10 optimal features were selected as input data for OBIA segmentation, with an optimal scale parameter obtained by quantification assessment of topological and geometric object differences. Using the segmented metrics in a decision tree classifier, an overall classification accuracy of 90.87% was achieved. Our study highlights the advantage of OBIA segmentation and classification in reducing noise from in-field heterogeneity and spectral variation. The crop classification map produced at 30 m resolution provides spatial distributions of annual and perennial crops, which are valuable for agricultural monitoring and environmental assessment studies.

Impacts of Urbanization on Surface Runoff of the Dardenne Creek Watershed, St. Charles County, Missouri

Accurately documenting urban growth and evaluating its hydrological impact are of great interest for urban planning and water/land resource management. St. Charles County, a suburb of St. Louis, Missouri, has undergone significant urban expansion in recent decades. Rapid urban sprawl in the Dardenne Creek watershed within the county has had a profound influence on surface runoff. We examined the patterns of land use/land cover (LULC) change in this watershed using Landsat TM/ETM+ imagery in 1982, 1987, 1991, 1999, and 2003. Calibrated with the observed hydrological data in 2003, a Long-Term Hydrologic Impact Assessment (L-THIA) model was used to evaluate the effect of LULC change on surface runoff. Results indicated a rapid increase of urban areas in the watershed, from 3.4% in 1982 to 27.3% in 2003, dominated by changes in the lower portion of the watershed close to the metropolitan area. Model simulations suggest >70% increase in average direct runoff in the watershed from 1982 to 2003, and the runoff increase is highly correlated with urban expansion. This work helps raise awareness of the scale of hydrologic impacts of urbanization in this watershed, and provides a simple calibrated tool for local planners to assess potential hydrological impacts of future planning and development activities.

Detecting winter wheat phenology with SPOT-VEGETATION data in the North China Plain

Monitoring phenological change in agricultural land improves our understanding of the adaptation of crops to a warmer climate. Winter wheat–maize and winter wheat–cotton double-cropping are practised in most agricultural areas in the North China Plain. A curve-fitting method is presented to derive winter wheat phenology from SPOT-VEGETATION S10 normalized difference vegetation index (NDVI) data products. The method uses a double-Gaussian model to extract two phenological metrics, the start of season (SOS) and the time of maximum NDVI (MAXT). The results are compared with phenological records at local agrometeorological stations. The SOS and MAXT have close agreement with in situ observations of the jointing date and milk-in-kernel date respectively. The phenological metrics detected show spatial variations that are consistent with known phenological characteristics. This study indicates that time-series analysis with satellite data could be an effective tool for monitoring the phenology of crops and its spatial distribution in a large agricultural region.

Analysis of temporal radar backscatter of rice: A comparison of SAR observations with modeling results

In this study an established microwave backscatter model is used to predict the radar backscatter behavior of rice and to understand the interaction between backscatter and the rice canopy during its growth cycle. The emphasis of this study is to understand the effect of physical plant parameters on the backscatter signatures as a function of polarization and how these signatures vary during a complete growth cycle of rice. Inputs to the backscatter model included the physical parameters of rice as obtained through field measurements. These measurements were acquired within a few days of multiple RADARSAT acquisitions of the Zhaoqing test site in southern China. RADARSAT observations and the modeling results were then compared and analyzed. The results show that the interaction mechanisms change during the rice growth cycle and polarimetric and (or) multi-polarization measurements at C band will contribute to rice monitoring programs.