Yonghua Qu

An Optimal Sampling Design for Observing and Validating Long-Term Leaf Area Index with Temporal Variations in Spatial Heterogeneities

A sampling strategy to define elementary sampling units (ESUs) for an entire site at the kilometer scale is an important step in the validation process for moderate-resolution leaf area index (LAI) products. Current LAI-sampling strategies are unable to consider the vegetation seasonal changes and are better suited for single-day LAI product validation, whereas the increasingly used wireless sensor network for LAI measurement (LAINet) requires an optimal sampling strategy across both spatial and temporal scales. In this study, we developed an efficient and robust LAI Sampling strategy based on Multi-temporal Prior knowledge (SMP) for long-term, fixed-position LAI observations. The SMP approach employed multi-temporal vegetation index (VI) maps and the vegetation classification map as a priori knowledge. The SMP approach minimized the multi-temporal bias of the VI frequency histogram between the ESUs and the entire site and maximized the nearest-neighbor index to ensure that ESUs were dispersed in the geographical space. The SMP approach was compared with four sampling strategies including random sampling, systematic sampling, sampling based on the land-cover map and a sampling strategy based on vegetation index prior knowledge using the PROSAIL model-based simulation analysis in the Heihe River basin. The results indicate that the ESUs selected using the SMP method spread more evenly in both the multi-temporal feature space and geographical space over the vegetation cycle. By considering the temporal changes in heterogeneity, the average root-mean-square error (RMSE) of the LAI reference maps can be reduced from 0.12 to 0.05, and the relative error can be reduced from 6.1% to 2.2%. The SMP technique was applied to assign the LAINet ESU locations at the Huailai Remote Sensing Experimental Station in Beijing, China, from 4 July to 28 August 2013, to validate three MODIS C5 LAI products. The results suggest that the average R2, RMSE, bias and relative uncertainty for the three MODIS LAI products were 0.60, 0.33, −0.11, and 12.2%, respectively. The MCD15A2 product performed best, exhibiting a RMSE of 0.20, a bias of −0.07 and a relative uncertainty of 7.4%. Future efforts are needed to obtain more long-term validation datasets using the SMP approach on different vegetation types for validating moderate-resolution LAI products in time series.

A dynamic Bayesian network data fusion algorithm for estimating leaf area index using time-series data from in situ measurement to remote sensing observations

Leaf area index (LAI) products retrieved from remote sensing observations have been widely used in the fields of ecosphere, atmosphere etc. However, because satellite-observed images are captured instantaneously and sometimes screened by cloud, some current LAI products are inherently discontinuous in time and their accuracy may not meet the needs of users well. To solve these problems, we proposed a dynamic Bayesian network (DBN)-based data fusion algorithm that integrates dynamic crop growth information, a canopy reflectance (CR) model and remote sensing observations from the perspective of Bayesian probability. Using the proposed algorithm, LAI was estimated using data sets from both field measurements for winter wheat in Beijing, China, and MODIS reflectance data at two American flux tower sites. Results showed good agreement between the LAI estimated by the DBN-based data fusion method and the true ground LAI, with a correlation coefficient of (R) 0.95 and 0.96, respectively, and a corresponding root mean square error (RMSE) of 0.35 and 0.49, respectively. In addition, the LAI estimated by the DBN-based data fusion method formed a continuous time series and was consistent with the variety law of vegetation growth at both plot and flux tower site scales. It has been demonstrated that the proposed DBN-based data fusion algorithm has the potential to be used to accurately estimate LAI and to fill the temporal gap by integrating information from multiple sources.

The study on the method of monitoring and analyzing mineral environment with remote sensing images

The mineral environment of the DeXing Copper, in JiangXi Province in China, is monitored and analyzed by making use of the field spectral data and remote sensing images, TM data as well as ETM data, in different mineral developmental period. The location of the mine tailings is identified and its change in area and volume versus the time is calculated as well. A method to use DEM (Digital Elevation Model) for analyzing the change of the volume for the pollution source and its impact to the local environment is proposed in this paper. It provides the quantitative description for the mineral environmental pollution. This method can be used to monitor the open mineral environment, which has the same environmental problem like DeXing Copper Mine. Its beneficial for the local government to supervise the mineral environmental changes and be aware of the pollution status dynamically and lively.

An Upscaling Algorithm to Obtain the Representative Ground Truth of LAI Time Series in Heterogeneous Land Surface

Upscaling in situ leaf area index (LAI) measurements to the footprint scale is important for the validation of medium resolution remote sensing products. However, surface heterogeneity and temporal variation of vegetation make this difficult. In this study, a two-step upscaling algorithm was developed to obtain the representative ground truth of LAI time series in heterogeneous surfaces based on in situ LAI data measured by the wireless sensor network (WSN) observation system. Since heterogeneity within a site usually arises from the mixture of vegetation and non-vegetation surfaces, the spatial heterogeneity of vegetation and land cover types were separately considered. Representative LAI time series of vegetation surfaces were obtained by upscaling in situ measurements using an optimal weighted combination method, incorporating the expectation maximum (EM) algorithm to derive the weights. The ground truth of LAI over the whole site could then be determined using area weighted combination of representative LAIs of different land cover types. The algorithm was evaluated using a dataset collected in Heihe Watershed Allied Telemetry Experimental Research (HiWater) experiment. The proposed algorithm can effectively obtain the representative ground truth of LAI time series in heterogeneous cropland areas. Using the normal method of an average LAI measurement to represent the heterogeneous surface produced a root mean square error (RMSE) of 0.69, whereas the proposed algorithm provided RMSE = 0.032 using 23 sampling points. The proposed ground truth derived method was implemented to validate four major LAI products.

The complicate observations and multi-parameter land information constructions on allied telemetry experiment (COMPLICATE)

The Complicate Observations and Multi-Parameter Land Information Constructions on Allied Telemetry Experiment (COMPLICATE) comprises a network of remote sensing experiments designed to enhance the dynamic analysis and modeling of remotely sensed information for complex land surfaces. Two types of experimental campaigns were established under the framework of COMPLICATE. The first was designed for continuous and elaborate experiments. The experimental strategy helps enhance our understanding of the radiative and scattering mechanisms of soil and vegetation and modeling of remotely sensed information for complex land surfaces. To validate the methodologies and models for dynamic analyses of remote sensing for complex land surfaces, the second campaign consisted of simultaneous satellite-borne, airborne, and ground-based experiments. During field campaigns, several continuous and intensive observations were obtained. Measurements were undertaken to answer key scientific issues, as follows: 1) Determine the characteristics of spatial heterogeneity and the radiative and scattering mechanisms of remote sensing on complex land surfaces. 2) Determine the mechanisms of spatial and temporal scale extensions for remote sensing on complex land surfaces. 3) Determine synergist inversion mechanisms for soil and vegetation parameters using multi-mode remote sensing on complex land surfaces. Here, we introduce the background, the objectives, the experimental designs, the observations and measurements, and the overall advances of COMPLICATE. As a result of the implementation of COMLICATE and for the next several years, we expect to contribute to quantitative remote sensing science and Earth observation techniques.

Retrieval of a Temporal High-Resolution Leaf Area Index (LAI) by Combining MODIS LAI and ASTER Reflectance Data

This paper aims to retrieve temporal high-resolution LAI derived by fusing MOD15 products (1 km resolution), field-measured LAI and ASTER reflectance (15-m resolution). Though the inversion of a physically based canopy reflectance model using high-resolution satellite data can produce high-resolution LAI products, the obstacle to producing temporal products is obvious due to the low temporal resolution of high resolution satellite data. A feasible method is to combine different source data, taking advantage of the spatial and temporal resolution of different sensors. In this paper, a high-resolution LAI retrieval method was implemented using a dynamic Bayesian network (DBN) inversion framework. MODIS LAI data with higher temporal resolution were used to fit the temporal background information, which is then updated by new, higher resolution data, herein ASTER data. The interactions between the different resolution data were analyzed from a Bayesian perspective. The proposed method was evaluated using a dataset collected in the HiWater (Heihe Watershed Allied Telemetry Experimental Research) experiment. The determination coefficient and RMSE between the estimated and measured LAI are 0.80 and 0.43, respectively. The research results suggest that even though the coarse-resolution background information differs from the high-resolution satellite observations, a satisfactory estimation result for the temporal high-resolution LAI can be produced using the accumulated information from both the new observations and background information.

Retrieval of 30-m-Resolution Leaf Area Index From China HJ-1 CCD Data and MODIS Products Through a Dynamic Bayesian Network

The leaf area index (LAI) is a characteristic parameter of vegetation canopies. This parameter is significant in research on global climate change and ecological environments. The China HJ-1 satellite has a revisit cycle of four days and provides CCD (HJ-1 CCD) data with a resolution of 30 m. However, the HJ-1 CCD is incapable of obtaining observations at multiple angles. This is problematic because single-angle observations provide insufficient data for determining the LAI. This article proposes a new method for determining the LAI using the HJ-1 CCD data. The proposed method uses background knowledge of the dynamic land surface processes that is extracted from MODerate resolution Imaging Spectroradiometer (MODIS) LAI data with a resolution of 1 km. The proposed method was implemented in a dynamitic Bayesian network scheme by integrating an LAI dynamic process model and a canopy reflectance model with the remotely sensed data. The validation was conducted using field LAI data collected in the Guantao County of the Hebei Province in China. The results showed that the determination coefficient between the estimated and the measured LAI was 0.791, and the RMSE was 0.61. The results suggest that this algorithm can be widely applied to determine high-resolution leaf area indexes using data from the China HJ-1 satellite even if the information from single-angle observations are insufficient for quantitative application.

Studies on urban areas extraction from landsat TM images

In this paper, extracting urban areas from Landsat TM images is studied. We proposed a new method for urban and rural residential areas extraction. A classification example based on barren index(BI) is given in this article. We classified the image with several methods and compared the classification results. The result indicates this proposed method based on BI has obvious advantages over conventional multi-band spectral data classification. Classification map created by this method suffers less from a lack of spatial coherency and has a high classification accuracy.

Study on hybrid inversion scheme under Bayesian network

A hybrid inversion scheme for estimating surface variables of vegetation is presented under Bayesian Network theory, and then is used to estimate chlorophyll content of winter wheat leaves and Leaf Area Index (LAI) of canopy. Results using data simulated by coupled models----PROSAIL and those with additional Gaussian white noise show that both LAI and Cab can be estimated with an appreciated accuracy under the proposed scheme, except that there are about 10% of total points falling into failure inversion. Then an uncertain data handling method is employed to solve the failure problem, as a result the failure points are removed successfully though the RMSE of estimated the two variables is larger slightly. The presented hybrid inversion scheme is a knowledge inferring mechanism in principle, so the updated information content in the inversion process is quantitatively calculated thanks to the concept of entropy introduced from thermodynamics..

The construction of J2EE-based Spectrum Knowledge Base System for Typical Object in China

The Spectrum Knowledge Base System (SKBS) for Typical Object in China, built up by taking advantage of J2EE technology, is capable of providing the functionalities in spectrum analysis, query and comparison. More importantly, the spectrum scale effect, especially the scale extension, can be achieved in SKBS, which is based on the model-driven theory with the support of the prior knowledge.