Jinfei Wang

A rule-based urban land use inferring method for fine-resolution multispectral imagery

Detailed urban land use mapping requires high-resolution remotely sensed data. The pan-sharpened multispectral IKONOS imagery of 1 m pixel resolution is experimented with for urban land use classification. With the increase of spatial resolution, between-class spectral confusion and within-class spectral variation increase. Spectral-based traditional image classification methods cannot be directly applied to the IKONOS data for urban land use mapping. In this study, a rule-based urban land use inferring method is proposed and tested on 36 samples of typical land use classes and an IKONOS subscene of various classes in London, Ontario, Canada. The proposed method includes two general steps. First, the conventional multispectral classification method is applied to produce a preliminary land cover map. Second, urban land use information is inferred from the combination of several land cover classes existing in a neighbourhood by a rule-based modelling process. The inferring rules involve the percent composition ranges of compatible land cover categories for a certain land use class, the interrelationship of the compatible land covers, and exclusion of incompatible land covers. The results show that the proposed method has successfully identified level II and level III land use classes using the U.S. Geological Survey land use classification system. The proposed method has successfully identified the land use classes in the sample image with over 90% accuracy. For the subscene, the proposed method has produced a land use map with 88.5% overall accuracy.

Maximum Nighttime Urban Heat Island (UHI) Intensity Simulation by Integrating Remotely Sensed Data and Meteorological Observations

Remote sensing of the urban heat island (UHI) effect has been conducted largely through simple correlation and regression between the UHIs spatial variations and surface characteristics. Few studies have examined the surface UHI from a temporal perspective and related it with climatic and meteorological factors. By selecting the city of Beijing, China, as the study area, the purpose of this research was to evaluate the applicability and feasibility of the support vector machine (SVM) technique to model the daily maximum nighttime UHI intensity (MNUHII) based on integration of MODIS land products and meteorological observations. First, a Gaussian surface model was used to calculate the citys MNUHIIs. Then, SVM regression models were developed to predict the MNUHII from the following variables: the normalized difference vegetation index (NDVI), surface albedo, atmospheric aerosol optical depth (AOD), relative humidity (RH), sunshine hour (SH), and precipitation (PREP). Results demonstrate that the accuracy of the SVM regression in predicting the MNUHII was around 0.8°C to 1.3°C; in addition, the SVM regression outperformed the multiple linear regression and the artificial neural network with backpropagation. A scenario analysis indicates that the relationships between the MNUHII and its influencing factors varied with time and season and were impacted by previous precipitation. The RH and AOD were the most important factors that influenced the MNUHII. In addition, previous precipitation could significantly mitigate the MNUHII. The results suggest that future investigations on the surface UHI effect should consider the climatic and meteorological conditions in addition to the surface characteristics.

Assessment of social vulnerability to natural hazards in the Yangtze River Delta, China

China is exposed to a wide range of natural hazards, and disaster losses have escalated over the past decade. Owing to the pressure from natural disasters, along with changes in climate, social conditions, and regional environment, assessment of social vulnerability (SV) to natural hazards has become increasingly urgent for risk management and sustainable development in China. This paper presents a new method for quantifying SV based on the projection pursuit cluster (PPC) model. A reference social vulnerability index (SVI) at the county level was created for the Yangtze River Delta area in China for 1995, 2000, 2005, and 2009. The result of social vulnerability assessment was validated using data of actual losses from natural disasters. The primary findings are as follows: (i) In the study area, the major factors that impact SVI are regional per capita GDP and per capita income. (ii) The study area was more vulnerable in 1995 than in later years. SV of the whole region had decreased over the study period. (iii) Most part of Shanghai and the southeast part of Jiangsu Province had been the least vulnerable within the region. From this least vulnerable zone to the periphery of the region, the situation deteriorated. The highest SVI values in all evaluated years were found in the northern, western, or southern tips of the Yangtze River Delta.

Estimating winter wheat biomass by assimilating leaf area index derived from fusion of Landsat-8 and MODIS data

Abstract A sufficient number of satellite acquisitions in a growing season are essential for deriving agronomic indicators, such as green leaf area index (GLAI), to be assimilated into crop models for crop productivity estimation. However, for most high resolution orbital optical satellites, it is often difficult to obtain images frequently due to their long revisit cycles and unfavorable weather conditions. Data fusion algorithms, such as the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Enhanced STARFM (ESTARFM), have been developed to generate synthetic data with high spatial and temporal resolution to address this issue. In this study, we evaluated the approach of assimilating GLAI into the Simple Algorithm for Yield Estimation model (SAFY) for winter wheat biomass estimation. GLAI was estimated using the two-band Enhanced Vegetation Index (EVI2) derived from data acquired by the Operational Land Imager (OLI) onboard the Landsat-8 and a fusion dataset generated by blending the Moderate-Resolution Imaging Spectroradiometer (MODIS) data and the OLI data using the STARFM and ESTARFM models. The fusion dataset had the temporal resolution of the MODIS data and the spatial resolution of the OLI data. Key parameters of the SAFY model were optimised through assimilation of the estimated GLAI into the crop model using the Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm. A good agreement was achieved between the estimated and field measured biomass by assimilating the GLAI derived from the OLI data (GLAIL) alone (R2xa0=xa00.77 and RMSExa0=xa0231xa0gxa0m−2). Assimilation of GLAI derived from the fusion dataset (GLAIF) resulted in a R2 of 0.71 and RMSE of 193xa0gxa0m−2 while assimilating the combination of GLAIL and GLAIF led to further improvements (R2xa0=xa00.76 and RMSExa0=xa0176xa0gxa0m−2). Our results demonstrated the potential of using the fusion algorithms to improve crop growth monitoring and crop productivity estimation when the number of high resolution remote sensing data acquisitions is limited.

Land use and land cover change detection using satellite remote sensing techniques in the mountainous Three Gorges Area, China

The Three Gorges Area (TGA), along the Yangtze River, China has been experiencing drastic land use and land cover (LU/LC) changes since the commencement of the construction of the Three Gorges Dam in 1994. These changes may have environmental impacts. However, information about the changes is limited and difficult to obtain. In this paper, optical satellite imagery is used to detect LU/LC changes during the study period 1987–2006 for the complicated, high-relief environment. A case study was conducted in Zigui County of Hubei Province within the TGA. A site-specific procedure using a decision rule-based classification method and a post classification change detection technique is developed to combine spectral and spatial knowledge in the classification of multi-temporal images. Using the decision rule-based classification method, overall accuracies of LU/LC maps of seven classes between 73.4% and 89.5% were obtained, increased by 4–5% over that using the traditional method. The results show that the main trend in LU/LC change in the study area throughout the monitoring period was a steady reduction in natural vegetation areas. About 32% of the total area of natural vegetation, including forest, shrub and grass, was lost to built up areas, crop fields and orchards.

The Use of Multipolarized Spaceborne SAR Backscatter for Monitoring the Health of a Degraded Mangrove Forest

Abstract To determine whether multipolarized spaceborne synthetic aperture radar could be used to monitor the health of a mangrove forest, leaf area index, as well as other biophysical parameter data, from stands dominated by white mangrove (Laguncularia racemosa) and located within a degraded mangrove forest were examined in relation to backscatter coefficients from ENVISAT synthetic aperture radar scenes. The results indicate that polarization and, to a lesser extent, incident angle play a significant role in the ability to estimate both leaf area index and mean tree height. No significant linear coefficients of determination were observed between the recorded parameters and the backscatter coefficient from any of the copolarized scenes. With regards to leaf area index, r2 values of 0.82 and 0.73 were calculated for the cross-polarized data at two incident angles. For mean tree height, the linear coefficient of determination was much higher for the smaller incident angle data than for the larger incident angle data. No significant relationships were identified for stem density, basal area, or mean diameter at breast height. It is postulated that the inability of the copolarized ENVISAT advanced synthetic aperture radar data to differentiate between dead mangrove stands and healthy ones is the result of equally high backscatter resulting from strong scattering from trunk–ground double bounce and crown volume, respectively.

A method for obtaining and applying classification parameters in object-based urban rooftop extraction from VHR multispectral images

Object-based methods of urban feature extraction from high spatial resolution remotely sensed data rely on semantic inference of spatial and contextual classification parameters in scenes of regular spatial or material composition. In this study, a supervised statistics-based method of determining and applying discretive parameters of rooftops in urban scenes of irregular composition is presented. After preprocessing to pansharpen IKONOS image data, the method includes the following steps: (1) image segmentation; (2) supervised object-based classification into broad spectral classes including impervious surfaces; (3) spectral, spatial, textural and contextual parameters are developed from statistical comparison of the sample rooftop and other impervious surface objects and (4) these parameters are implemented in a fuzzy logic rule base to separate rooftops from other impervious surfaces. Classification of a test scene results in 93% accuracy of rooftop identification, demonstrating the applicability of the method to the discrimination of spectrally similar but semantically variable classes.

An Evaluation System for Building Footprint Extraction From Remotely Sensed Data

This study proposes a multi-criteria and hierarchical evaluation system for building extraction from remotely sensed data. Most of current evaluation methods are focused on classification accuracy, while the other dimensions of extraction accuracy are usually ignored. The proposed evaluation system consists of three components: 1) the matched rate, including evaluation metrics for the traditional classification accuracy (e.g., completeness, correctness, and quality); 2) the shape similarity that describes the resemblance between reference and extracted buildings, including image-based and polygon-based metrics; and 3) the positional accuracy which is measured by distances at feature points such as buildings centroid. The system also hierarchically evaluates extracted buildings at per-building, per-scene, and overall levels. To reduce the redundancy among different metrics, principal component analysis and correlation analysis are employed for metrics selection and aggregation. Four different building extraction methods, using high-resolution optical imagery and/or LiDAR data, are implemented to test the proposed system. The experiment demonstrates that the proposed system is more consistent with human vision compared to traditional classification accuracy metrics. This system can highlight perceptible differences between the extracted building footprints and the reference data, even if this difference is insignificant measured by the traditional metrics.

Time series decomposition of remotely sensed land surface temperature and investigation of trends and seasonal variations in surface urban heat islands

Previous time series methods have difficulties in simultaneous characterization of seasonal, gradual, and abrupt changes of remotely sensed land surface temperature (LST). This study proposed a model to decompose LST time series into trend, seasonal, and noise components. The trend component indicates long-term climate change and land development and is described as a piecewise linear function with iterative breakpoint detection. The seasonal component illustrates annual insolation variations and is modeled as a sinusoidal function on the detrended data. This model is able to separate the seasonal variation in LST from the long-term (including gradual and abrupt) change. Model application to nighttime Moderate Resolution Imaging Spectroradiometer (MODIS)/LST time series during 2000-2012 over Beijing yielded an overall root-mean-square error of 1.62K between the combination of the decomposed trend and seasonal components and the actual MODIS/LSTs. LST decreased (similar to -0.086K/yr, p<0.1) in 53% of the study area, whereas it increased with breakpoints in 2009 (similar to 0.084K/yr before and similar to 0.245K/yr after 2009) between the fifth and sixth ring roads. The decreasing trend was stronger over croplands than over urban lands (p<0.05), resulting in an increasing trend in surface urban heat island intensity (SUHII, 0.0220.006K/yr). This was mainly attributed to the trends in urban-rural differences in rainfall and albedo. The SUHII demonstrated a concave seasonal variation primarily due to the seasonal variations of urban-rural differences in temperature cooling rate (related to canyon structure, vegetation, and soil moisture) and surface heat dissipation (affected by humidity and wind).

Estimating plant area index for monitoring crop growth dynamics using Landsat-8 and RapidEye images

Abstract This study investigates the use of two different optical sensors, the multispectral imager (MSI) onboard the RapidEye satellites and the operational land imager (OLI) onboard the Landsat-8 for mapping within-field variability of crop growth conditions and tracking the seasonal growth dynamics. The study was carried out in southern Ontario, Canada, during the 2013 growing season for three annual crops, corn, soybeans, and winter wheat. Plant area index (PAI) was measured at different growth stages using digital hemispherical photography at two corn fields, two winter wheat fields, and two soybean fields. Comparison between several conventional vegetation indices derived from concurrently acquired image data by the two sensors showed a good agreement. The two-band enhanced vegetation index (EVI2) and the normalized difference vegetation index (NDVI) were derived from the surface reflectance of the two sensors. The study showed that EVI2 was more resistant to saturation at high biomass range than NDVI. A linear relationship could be used for crop green effective PAI estimation from EVI2, with a coefficient of determination ( R 2 ) of 0.85 and root-mean-square error of 0.53. The estimated multitemporal product of green PAI was found to be able to capture the seasonal dynamics of the three crops.