Qingyan Meng

A comprehensive drought monitoring method integrating MODIS and TRMM data

Abstract Drought is a complex hazard caused by the breaking of water balance and it has always an impact on agricultural, ecological and socio-economic spheres. Although the drought indices deriving from remote sensing data have been used to monitor meteorological or agricultural drought, there are no indices that can suitably reflect the comprehensive information of drought from meteorological to agricultural aspects. In this paper, the synthesized drought index (SDI) is defined as a principal component of vegetation condition index (VCI), temperature condition index (TCI) and precipitation condition index (PCI). SDI integrates multi-source remote sensing data from moderate resolution imaging spectroradiometer (MODIS) and tropical rainfall measuring mission (TRMM) and it synthesizes precipitation deficits, soil thermal stress and vegetation growth status in drought process. Therefore, this method is favorable to monitor the comprehensive drought. In our research, a heavy drought process was accurately explored using SDI in Shandong province, China from 2010 to 2011. Finally, a validation was implemented and its results show that SDI is not only strongly correlated with 3-month scales standardized precipitation index (SPI3), but also with variation of crop yield and drought-affected crop areas. It was proved that this index is a comprehensive drought monitoring indicator and it can contain not only the meteorological drought information but also it can reflect the drought influence on agriculture.

A hybrid method combining pixel-based and object-oriented methods and its application in Hungary using Chinese HJ-1 satellite images

Pixel-based and object-oriented processing of Chinese HJ-1-A satellite imagery (resolution 30 m) acquired on 23 July 2009 were utilized for classification of a study area in Budapest, Hungary. The pixel-based method (maximum likelihood classifier for pixel-level method (MLCPL)) and two object-oriented methods (maximum likelihood classifier for object-level method (MLCOL) and a hybrid method combining image segmentation with the use of a maximum likelihood classifier at the pixel level (MLCPL)) were compared. An extension of the watershed segmentation method was used in this article. After experimenting, we chose an optimum segmentation scale. Classification results showed that the hybrid method outperformed MLCOL, with an overall accuracy of 90.53%, compared with the overall accuracy of 77.53% for MLCOL. Jeffries–Matusita distance analysis revealed that the hybrid method could maintain spectral separability between different classes, which explained the high classification accuracy in mixed-cover types compared with MLCOL. The classification result of the hybrid model is preferred over MLCPL in geographical or landscape ecological research for its accordance with patches in landscape ecology, and for continuity of results. The hybrid of image segmentation and pixel-based classification provides a new way to classify land-cover types, especially mixed land-cover types, using medium-resolution images on a regional, national, or global basis.

An explorative study on the proximity of buildings to green spaces in urban areas using remotely sensed imagery

Urban areas are major places where intensive interactions between human and the natural system occur. Urban vegetation is a major component of the urban ecosystem, and urban residents benefit substantially from urban green spaces. To measure urban green spaces, remote sensing is an established tool due to its capability of monitoring urban vegetation quickly and continuously. In this study: (1) a Building’s Proximity to Green spaces Index (BPGI) was proposed as a measure of building’s neighbouring green spaces; (2) LiDAR data and multispectral remotely sensed imagery were used to automatically extract information regarding urban buildings and vegetation; (3) BPGI values for all buildings were calculated based on the extracted data and the proximity and adjacency of buildings to green spaces; and (4) two districts were selected in the study area to examine the relationships between the BPGI and different urban environments. Results showed that the BPGI could be used to evaluate the proximity of residents to green spaces at building level, and there was an obvious disparity of BPGI values and distribution of BPGI values between the two districts due to their different urban functions (i.e., downtown area and residential area). Since buildings are the major places for residents to live, work and entertain, this index may provide an objective tool for evaluating the proximity of residents to neighbouring green spaces. However, it was suggested that proving correlations between the proposed index and human health or environmental amenity would be important in future research for the index to be useful in urban planning.

An effective Building Neighborhood Green Index model for measuring urban green space

Urban green space forms an integral part of urban ecosystems. Quantifying urban green space is of substantial importance for urban planning and development. Considering the drawbacks of previous urban green space index models, which established either through a grid method or green distribution, and the difficulty of the validation process of earlier urban green space index models, this study exploits the advantages of multisource high-resolution remote sensing data to establish a Building Neighborhood Green Index (BNGI) model. The model which analyzes the spatial configuration of built-up areas and the vegetation is based on the building-oriented method and considers four parameters – Green Index (GI), proximity to green, building sparsity, and building height. Comparing BNGI with GI in different types of characteristic building regions, it was found that BNGI evaluates urban green space more sensitively. It was also found that high-rise low-sparsity area has a lower mean value of BNGI (0.56) as compared with that of low-rise low-sparsity neighborhood (0.62), whereas mean GI (0.24) is equal for both neighborhoods. Taking characteristics of urban building and green types into consideration, BNGI model can be effectively used in many fields such as land suitability analysis and urban planning.

A Soil Moisture Retrieval Method Based on Typical Polarization Decomposition Techniques for a Maize Field from Full-Polarization Radarsat-2 Data

Soil moisture (SM) estimates are important to research, but are not accurately predictable in areas with tall vegetation. Full-polarization Radarsat-2 C-band data were used to retrieve SM contents using typical polarization decomposition (Freeman–Durden, Yamaguchi and VanZly) at different growth stages of maize. Applicability analyses were conducted, including proportion, regression and surface scattering model analyses. Furthermore, the Bragg, the extended Bragg scattering model (X-Bragg) and improved surface scattering models (ISSM) were used to retrieve SM content. The results indicated that the VanZly decomposition method was the best. The proportion of surface scattering in the proportion analysis was highest (>52%), followed by that in the Yamaguchi method (>41%). The R2 (>0.6144) between surface scattering and SM was significantly higher (R2 0.6599) and lower absolute error (AE) (<5.82) and root mean square error (RMSE) (<3.73). The best algorithm was obtained at the sowing stage (R2 = 0.8843, AE = 3.13, RMSE = 1.76). In addition, the X-Bragg model provided better approximation of actual surface scattering without the measured data (better algorithm: R2 = 0.8314, AE = 4.39, RMSE = 2.81).

Extracting seismic anomalies based on STD threshold method using outgoing Longwave Radiation data

In this paper, STD (standard deviation) threshold method was proposed using to detect OLR (Outgoing Longwave Radiation) seismic anomalies. OLR data describe the radiation information from the top of the atmosphere, which be thought to reflect the energy changes of earth-atmosphere system prior to earthquakes. The method to identifying seismic anomaly and non-seismic anomaly has been proposed in this work. Based on this, Wenchuan Sichuan, May 12, 2008, Ms8.0 and Delingha, Qinghai, November 10, 2008, Ms6.3 have been studied respectively using STD threshold method. The results indicate that the seismic infrared radiation anomalies can be detected using STD threshold method. And these anomalies can reflect the process of earthquake preparation. At the same time, the spatial distributions of OLR anomalies have some indication effect on judge the seismogenic structure and the epicenter.

Fractional vegetation cover estimation over large regions using GF-1 satellite data

This paper evaluates the usefulness of the WFV (Wide Field View) imager onboard GF-1 satellite in vegetation mapping. Fractional vegetation cover (FVC) is an important surface microclimate parameter for characterizing land surface vegetation cover. Three kinds of remote sensing inversion models (NDVI regression model, spectral mixture analysis (SMA) model and dimidiate pixel model) were used to derive FVC with the GF-1/WFV data. The verification indicates that the FVC results based on the dimidiate pixel model are well agreement with the in situ measurements. And the estimated FVC result in Beijing-tianjin-hebei region demonstrate that the GF-1/WFV data are fit for studying vegetation over large regions.

Leaf Area Index Estimation Using Chinese GF-1 Wide Field View Data in an Agriculture Region

Leaf area index (LAI) is an important vegetation parameter that characterizes leaf density and canopy structure, and plays an important role in global change study, land surface process simulation and agriculture monitoring. The wide field view (WFV) sensor on board the Chinese GF-1 satellite can acquire multi-spectral data with decametric spatial resolution, high temporal resolution and wide coverage, which are valuable data sources for dynamic monitoring of LAI. Therefore, an automatic LAI estimation algorithm for GF-1 WFV data was developed based on the radiative transfer model and LAI estimation accuracy of the developed algorithm was assessed in an agriculture region with maize as the dominated crop type. The radiative transfer model was firstly used to simulate the physical relationship between canopy reflectance and LAI under different soil and vegetation conditions, and then the training sample dataset was formed. Then, neural networks (NNs) were used to develop the LAI estimation algorithm using the training sample dataset. Green, red and near-infrared band reflectances of GF-1 WFV data were used as the input variables of the NNs, as well as the corresponding LAI was the output variable. The validation results using field LAI measurements in the agriculture region indicated that the LAI estimation algorithm could achieve satisfactory results (such as R2 = 0.818, RMSE = 0.50). In addition, the developed LAI estimation algorithm had potential to operationally generate LAI datasets using GF-1 WFV land surface reflectance data, which could provide high spatial and temporal resolution LAI data for agriculture, ecosystem and environmental management researches.

Spatial and Temporal Analysis of the Mitigating Effects of Industrial Relocation on the Surface Urban Heat Island over China

Urbanization is typically accompanied by the relocation and reconstruction of industrial areas due to limited space and environmental requirements, particularly in the case of a capital city. Shougang Group, one of the largest steel mill operators in China, was relocated from Beijing to Hebei Province. To study the thermal environmental changes at the Shougang industrial site before and after relocation, four Landsat images (from 2000, 2005, 2010 and 2016) were used to calculate the land surface temperature (LST). Using the urban heat island ratio index (URI), we compared the LST values for the four images of the investigated area. Following the relocation of Shougang Group, the URI values decreased from 0.55 in 2005 to 0.21 in 2016, indicating that the surface urban heat island effect in the area was greatly mitigated; we infer that this effect was related to steel production. This study shows that the use of Landsat images to assess industrial thermal pollution is feasible. Accurate and rapid extraction of thermal pollution data by remote sensing offers great potential for the management of industrial pollution sources and distribution, and for technical support in urban planning departments.

Modelling building proximity to greenery in a three-dimensional perspective using multi-source remotely sensed data

Urban vegetation is important for the well-being of urban residents. Remotely sensed datasets can be used to efficiently quantify urban green spaces (UGSs) across broad spatial extents. Different methods have been developed to quantitatively describe UGSs using remotely sensed datasets. However, few studies have taken the vertical dimension into consideration in evaluating human interactions with nearby greenery. In this study, a new index, called the ‘3D building proximity to greenery index’ (3DBPGI), is proposed to evaluate the proximity of a building to its nearby urban greenery within a buffer distance by accounting for the building’s height and different vegetation types. The 3DBPGI values for buildings in a Hungarian city, Székesfehérvár, were calculated. The results of the case study show that this index can indicate to some extent the human proximity to greenery for each building block in urban areas, which further can help planners to find critical areas for urban greening programmes.