Qinhuo Liu

Heihe Watershed Allied Telemetry Experimental Research (HiWATER): Scientific Objectives and Experimental Design

A major research plan entitled “Integrated research on the ecohydrological process of the Heihe River Basin” was launched by the National Natural Science Foundation of China in 2010. One of the key aims of this research plan is to establish a research platform that integrates observation, data management, and model simulation to foster twenty-first-century watershed science in China. Based on the diverse needs of interdisciplinary studies within this research plan, a program called the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) was implemented. The overall objective of HiWATER is to improve the observability of hydrological and ecological processes, to build a world-class watershed observing system, and to enhance the applicability of remote sensing in integrated ecohydrological studies and water resource management at the basin scale. This paper introduces the background, scientific objectives, and experimental design of HiWATER. The instrumental setting and airborne mission plans a...

Monitoring urban expansion with remote sensing in China

The Open Policy and Economic Reform has imposed a profound impact on dynamic changes of land-use patterns in many areas of China over the last two decades. Rapid urbanization and industrialization have resulted in a sharp reduction in arable land acreage. A project was carried out in 1997 under the auspices of the China State Land Administration to monitor the dynamics of urban expansion in 100 municipalities throughout China. This paper describes the implementation of the project and reports on some of the results obtained. Landsat Thematic Mapper (TM) images acquired for 1989/1992 and 1996/1997 were used to examine the scope and the speed of urban expansion in this period. Change detection techniques were employed to identify the areas of urban encroachment. Statistics on preceding states of the expanded urban areas were generated and various thematic maps were produced. The results showed that urban development in southern and coastal areas is the fastest, and the highest speed of urban expansion is as much as 30% per year. Urban growth is estimated at 1.2 million hectares for the whole country during this period, and 0.867 million hectares of arable land have been lost due to urban expansion.

A novel approach for edge detection based on the theory of universal gravity

This paper presents a new, simple and effective low-level processing edge detection algorithm based on the law of universal gravity. The algorithm assumes that each image pixel is a celestial body with a mass represented by its grayscale intensity. Accordingly, each celestial body exerts forces onto its neighboring pixels and in return receives forces from the neighboring pixels. These forces can be calculated by the law of universal gravity. The vector sums of all gravitational forces along, respectively, the horizontal and the vertical directions are used to compute the magnitude and the direction of signal variations. Edges are characterized by high magnitude of gravitational forces along a particular direction and can therefore be detected. The proposed algorithm was tested and compared with conventional methods such as Sobel, LOG, and Canny using several standard images, with and without the contamination of Gaussian white noise and salt & pepper noise. Results show that the proposed edge detector is more robust under noisy conditions. Furthermore, the edge detector can be tuned to work at any desired scale.

An Extended 3-D Radiosity–Graphics Combined Model for Studying Thermal-Emission Directionality of Crop Canopy

Radiosity-graphics combined model (RGM) has been proposed to calculate the radiation regime and bidirectional reflectance distribution function of complex 3D scene, which is limited in visible and near-infrared wavelength (0.3-3 mum) region. In this paper, RGM is extended to thermal region (named as TRGM) based on thermal-radiosity theory and thermal-emission directionality of vegetation canopy. The TRGM has been implemented on Microsoft Windows platform, and a parameterization scheme for crop canopies is introduced in this paper. It is then evaluated by comparing with two row-crop directional thermal emission models and one thermal radiative-transfer model. Field experiment data has been used to validate the TRGM for row structural wheat and maize canopies. The root mean square error of directional brightness temperature (DBT) is smaller than 1.0degC for the wheat canopy and 0.5degC for the maize canopy while the canopy DBTs vary more than 4degC. Model sensitivity analyses have also been conducted to illustrate influences of component temperature distribution, component emissivity, incident atmospheric radiation, and canopy structure on the crop canopy DBT.

Parametrized BRDF for atmospheric and topographic correction and albedo estimation in Jiangxi rugged terrain, China

A method is presented for bi‐directional reflectance distribution function (BRDF) parametrization for topographic correction and surface reflectance estimation from Landsat Thematic Mapper (TM) over rugged terrain. Following this reflectance, albedo is calculated accurately. BRDF is parametrized using a land‐cover map and Landsat TM to build a BRDF factor to remove the variation of relative solar incident angle and relative sensor viewing angle per pixel. Based on the BRDF factor and radiative transfer model, solar direct radiance correction, sky diffuse radiance and adjacent terrain reflected radiance correction were introduced into the atmospheric‐topographic correction method. Solar direct radiance, sky diffuse radiance and adjacent terrain reflected radiance, as well as atmospheric transmittance and path radiance, are analysed in detail and calculated per pixel using a look‐up table (LUT) with a digital elevation model (DEM). The method is applied to Landsat TM imagery that covers a rugged area in Jiangxi province, China. Results show that atmospheric and topographic correction based on BRDF gives better surface reflectance compared with sole atmospheric correction and two other useful atmospheric‐topographic correction methods. Finally, surface albedo is calculated based on this topography‐corrected reflectance and shows a reasonable accuracy in albedo estimation.

Scale effect and scale correction of land-surface albedo in rugged terrain

Influence of topography must be corrected when fine-scale remote sensing data are used to estimate surface albedo in rugged terrain. In the case of coarse-scale satellite remote sensing data, the topographic effect on albedo estimation is generally ignored because the influence to albedo estimation of overall slope in coarse-scale pixels is usually considered negligible; however, because of the scale effect in albedo, neglecting within-pixel topology may cause errors in albedo estimation from coarse-scale data. This paper investigates the scale effect on land-surface albedo estimations, particularly in the case of converting fine-scale albedo to coarse-scale albedo in rugged terrain. Starting from the definition of difference-scale albedo in rugged terrain, a method is presented to convert fine-scale surface albedo to coarse-scale albedo, then deriving a factor to correct coarse-scale land-surface albedo. The performance and accuracy analysis of the method are investigated by using a simulated digital elevation model (DEM) with different mean slopes, as well as real DEM and Thematic Mapper images. Results show that the method is effective for scale-effect correction of land-surface albedo in rugged terrain.

Evaluation of the NCEP and MODIS Atmospheric Products for Single Channel Land Surface Temperature Retrieval With Ground Measurements: A Case Study of HJ-1B IRS Data

In this paper, two atmospheric profile sources were assessed for land surface temperature (LST) retrieval purposes for the HJ-1B IRS (Infrared Scanner) single-channel thermal infrared (TIR) data. One profile source is the National Center for Environmental Prediction (NCEP) operational global analysis data, and the other source is the Moderate Resolution Imaging Spectroradiometer (MODIS) atmospheric profiles product (MOD07). The atmospheric profiles were used as the input to the MODTRAN 4 radiative transfer model to calculate the atmospheric parameters involved in LST retrieval. The LST retrievals from the HJ-1B IRS data were compared with ground measured temperatures obtained from a series of field campaigns in Hebei province, China, from May to September of 2010. Ground measurements were performed over four land-cover types: bare soil, full-cover wheat, full-cover corn, and water surfaces. A total of 11 points of measurements was collected over a period of eight days. The results indicate that the LST derived from HJ-1B IRS data using either the NCEP or MOD07 profiles showed good agreement with the ground LSTs, with an root mean square error (RMSE) of 1.16 and 1.21 K for the NCEP and MOD07, respectively. In addition, we found that the MOD07 profiles may cause greater error for the atmospheric parameters estimation in the TIR domain for the regions of higher altitude due to a lack of data at the lower altitude levels. Thus, we proposed a method for combination of the MOD07 and NCEP profiles for LST retrieval. The results show that the combined profile is able to produce more reliable results than the use of only one type of profile because the combination offers both high spatial resolution and the necessary level of accuracy. This result implies that the combined profiles may be highly useful for accurate LST retrieval when local soundings are not available and particularly for sensors with only one thermal channel.

Regional Leaf Area Index Retrieval Based on Remote Sensing: The Role of Radiative Transfer Model Selection

Physically-based approaches for estimating Leaf Area Index (LAI) using remote sensing data rely on radiative transfer (RT) models. Currently, many RT models are freely available, but determining the appropriate RT model for LAI retrieval is still problematic. This study aims to evaluate the necessity of RT model selection for LAI retrieval and to propose a retrieval methodology using different RT models for different vegetation types. Both actual experimental observations and RT model simulations were used to conduct the evaluation. Each of them includes needleleaf forests and croplands, which have contrasting structural attributes. The scattering from arbitrarily inclined leaves (SAIL) model and the four-scale model, which are 1D and 3D RT models, respectively, were used to simulate the synthetic test datasets. The experimental test dataset was established through two field campaigns conducted in the Heihe River Basin. The results show that the realistic representation of canopy structure in RT models is very important for LAI retrieval. If an unsuitable RT model is used, then the root mean squared error (RMSE) will increase from 0.43 to 0.60 in croplands and from 0.52 to 0.63 in forests. In addition, an RT model’s potential to retrieve LAI is limited by the availability of a priori information on RT model parameters. 3D RT models require more a priori information, which makes them have poorer generalization capability than 1D models. Therefore, physically-based retrieval algorithms should embed more than one RT model to account for the availability of a priori information and variations in structural attributes among different vegetation types.

Finer Resolution Land-Cover Mapping Using Multiple Classifiers and Multisource Remotely Sensed Data in the Heihe River Basin

Land-cover datasets are crucial for research on eco-hydrological processes and earth system modeling. Many land-cover datasets have been derived from remote-sensing data. However, their spatial resolutions are usually low and their classification accuracy is not high enough, which are not well suited to the needs of land surface modeling. Consequently, a comprehensive method for monthly land-cover classification in the Heihe river basin (HRB) with high spatial resolution is developed. Moreover, the major crops in the HRB are also distinguished. The proposed method integrates multiple classifiers and multisource data. Three types of data including MODIS, HJ-1/CCD, and Landsat/TM and Google Earth images are used. Compared to single classifier, multiple classifiers including thresholding, support vector machine (SVM), object-based method, and time-series analysis are integrated to improve the accuracy of classification. All the data and classifiers are organized using a decision tree. Monthly land-cover maps of the HRB in 2013 with 30-m spatial resolution are made. A comprehensive validation shows great improvement in the accuracy. First, a visual comparison of the land-cover maps using the proposed method and standard SVM method shows the classification differences and the advantages of the proposed method. The confusion matrix is used to evaluate the classification accuracy, showing an overall classification accuracy of over 90% in the HRB, which is quite higher than previous approaches. Furthermore, a ground campaign was performed to evaluate the accuracy of crop classification and an overall accuracy of 84.09% for the crop classification was achieved.

A single-channel algorithm for land surface temperature retrieval from HJ-1B/IRS data based on a parametric model

Land surface temperature (LST) is required for a wide variety of scientific studies, from climatology to hydrology and ecology. This paper proposes a single-channel parametric model (SC-PM) algorithm for retrieving land surface temperature from the HJ-1B/IRS thermal infrared data. The SC-PM algorithm is based on the parametric model (PM) developed by Ellicott et al. (2009), the coefficients of PM are updated for HJ-1B/IRS, and the altitude is considered when extracting atmospheric profile from NCEP data. The proposed algorithm is evaluated by simulated data and MODIS LST products. The results show an root mean square error (RMSE) of 0.22K for the simulated data, and 1.73K for the MODIS LST product. This indicates the algorithm is suitable for producing HJ-1B/IRS LST product.