Significant spatial co-distribution pattern discovery

Given instances (spatial points) of different spatial features (categories), significant spatial co-distribution pattern discovery aims to find subsets of spatial features whose spatial distributions are statistically significantly similar to each other. Discovering significant spatial co-distribution patterns is important for many application domains such as identifying spatial associations between diseases and risk factors in spatial epidemiology. Previous methods mostly associated spatial features whose instances are frequently located together; however, this does not necessarily indicate a similarity in the spatial distributions between different features. Thus, this paper defines the significant spatial co-distribution pattern discovery problem and subsequently develops a novel method to solve it effectively. First, we propose a new measure, dissimilarity index, to quantify the difference between spatial distributions of different features under the spatial neighbor relation and then employ it in a distribution clustering method to detect candidate spatial co-distribution patterns. To further remove spurious patterns that occur accidentally, the validity of each candidate spatial co-distribution pattern is verified through a significance test under the null hypothesis that spatial distributions of different features are independent of each other. To model the null hypothesis, a distribution shift-correction method is presented by randomizing the relationships between different features and maintaining spatial structure of each feature (e.g., spatial auto-correlation). Comparisons with baseline methods using synthetic datasets demonstrate the effectiveness of the proposed method. A case study identifying co-morbidities in central Colorado is also presented to illustrate the real-world applicability of the proposed method.     

An improved NDVI-based threshold method for estimating land surface emissivity using MODIS satellite data

This work estimated the land surface emissivities (LSEs) for MODIS thermal infrared channels 29 (8.4–8.7 μm), 31 (10.78–11.28 μm), and 32 (11.77–12.27 μm) using an improved normalized difference vegetation index (NDVI)-based threshold method. The channel LSEs are expressed as functions of atmospherically corrected reflectance from the MODIS visible and near-infrared channels with wavelengths ranging from 0.4 to 2.2 μm for bare soil. To retain the angular information, the vegetation LSEs were explicitly expressed in the NDVI function. The results exhibited a root mean square error (RMSE) among the estimated LSEs using the improved method, and those calculated using spectral data from Johns Hopkins University (JHU) are below 0.01 for channels 31 and 32. The MODIS land surface temperature/emissivity (LST/E) products, MOD11_L2 with LSE derived via the classification-based method with 1 km resolution and MOD11C1 with LSE retrieved via the day/night LST retrieval method at 0.05° resolution, were used to validate the proposed method. The resultant variances and entropies for the LSEs estimated using the proposed method were larger than those extracted from MOD11_L2, which indicates that the proposed method better described the spectral variation for different land covers. In addition, comparing the estimated LSEs to those from MOD11C1 yielded RMSEs of approximately 0.02 for the three channels; however, more than 70% of pixels exhibited LSE differences within 0.01 for channels 31 and 32, which indicates that the proposed method feasibly depicts LSE variation for different land covers.     

Using QuickBird imagery and a production efficiency model to improve crop yield estimation in the semi-arid hilly Loess Plateau,China

Crop yield is a key element in rural development and an indicator of national food security. A method that could estimate crop yield over large hilly areas would be highly desirable. Methods including high spatial resolution satellite imagery have the potential to achieve this objective. This paper describes a method of integrating QuickBird imagery with a production efficiency model (PEM) to estimate crop yield in Zhonglianchuan, a hilly area on Loess Plateau, China. In the PEM model, crop yield is a function of the photosynthetic active radiation (PAR), fraction of absorbed photosynthetically active radiation (f_APAR) and light-use efficiency (LUE). Based on the high spatial resolution QuickBird imagery, a land cover classification is used to attribute a class-specific LUE. The f_APAR is related to spectral vegetation indices (SVI), which can be derived from the satellite images. The LUE, f_APAR and incident PAR data were combined to estimate the crop yield. Farmer-reported crop yield data in 80 representative plots were used to validate the model output. The results indicated QuickBird imagery can improve the accuracy of predicted results relative to the Landsat TM image. The predicted yield approximated well with the data reported by the farmers (r² = 0.86; n = 80). The spatial distributions of crop yield derived here also offers valuable information to manage agricultural production and understand ecosystem functioning.     

DECODE: a new method for discovering clusters of different densities in spatial data

When clusters with different densities and noise lie in a spatial point set, the major obstacle to classifying these data is the determination of the thresholds for classification, which may form a series of bins for allocating each point to different clusters. Much of the previous work has adopted a model-based approach, but is either incapable of estimating the thresholds in an automatic way, or limited to only two point processes, i.e. noise and clusters with the same density. In this paper, we present a new density-based cluster method (DECODE), in which a spatial data set is presumed to consist of different point processes and clusters with different densities belong to different point processes. DECODE is based upon a reversible jump Markov Chain Monte Carlo (MCMC) strategy and divided into three steps. The first step is to map each point in the data to its mth nearest distance, which is referred to as the distance between a point and its mth nearest neighbor. In the second step, classification thresholds are determined via a reversible jump MCMC strategy. In the third step, clusters are formed by spatially connecting the points whose mth nearest distances fall into a particular bin defined by the thresholds. Four experiments, including two simulated data sets and two seismic data sets, are used to evaluate the algorithm. Results on simulated data show that our approach is capable of discovering the clusters automatically. Results on seismic data suggest that the clustered earthquakes, identified by DECODE, either imply the epicenters of forthcoming strong earthquakes or indicate the areas with the most intensive seismicity, this is consistent with the tectonic states and estimated stress distribution in the associated areas. The comparison between DECODE and other state-of-the-art methods, such as DBSCAN, OPTICS and Wavelet Cluster, illustrates the contribution of our approach: although DECODE can be computationally expensive, it is capable of identifying the number of point processes and simultaneously estimating the classification thresholds with little prior knowledge.     

面向车载导航地图的动态交通信息存储模型

支持实时交通信息是新一代动态导航应用的基本特征。该文以现有导航电子地图物理存储格式中较成熟的分层道路模型为基础,结合线性参照方法提出多层路网统一编码方法以及基于该方法的动态分层路网存储模型DyHiRD,解决交通信息在冗余数据模型下的一致性更新问题。实验结果表明,该模型的更新效率满足支持大型城市交通信息服务的终端动态导航应用需求。     

The application of a binary division procedure to the classification of forest subcategories using MODIS time-series data during 2000–2010 in China

Forests account for more than 23% of China’s total area. As the most important terrestrial ecosystem, forests have tremendous ecological value. However, it remains difficult to classify forest subcategories at the national scale. In this study, a newly developed binary division procedure was used to categorize forest areas, including their spatiotemporal dynamics, during the period 2000–2010. Time-series images acquired using the Moderate Resolution Imaging Spectroradiometer (MODIS), together with auxiliary data on land use, climate zoning, and topography, were utilized. Hierarchical classification and zoning were combined with remote-sensing auto-classification. Based on the forest extent mask, the state-level forest system was divided into four classes and 18 subcategories. The method achieved an acceptable overall accuracy of 73.1%, based on a comparison to the sample points of China’s fourth forest general survey data set. In 2010, the total forest area was 1.755 × 10⁶ km², and the total area of and shrubs was 4.885 × 10⁵ km². The total area of woodland increased by 2536.25 km² during the decade 2000–2010. The shrub subcategories exhibited almost no change during this time period; however, significant changes in forest area occurred in the mountainous region of Northeast China as well as in the hilly regions of Southern China. The main transformations took place in cold-temperate and temperate mountainous deciduous coniferous forest, subtropical deciduous coniferous forest, subtropical evergreen coniferous forest, and temperate and subtropical deciduous broadleaved mixed forests. The binary division procedure proposed herein can be used not only to rapidly classify more forest subcategories and monitor their dynamic changes, but also to improve the classification accuracy compared with global and national land-cover maps.     

Analyzing Regional Inequality of Socioeconomic Development in China with Nighttime Light

Satellite\|derived nighttime light (NTL)data have been extensively used as an efficient proxy measure for monitoring urbanization dynamics and socioeconomic activity.This is because remotely sensed NTL signals can be quantitatively connected to demographic and socioeconomic variables.The recently composited cloud\|free NTL imagery derived from the Visible Infrared Imaging Radiometer Suite (VIIRS)provides spatially detailed observations of human settlements.We quantitatively estimated socioeconomic development inequalities across 30 provinces andmunicipalities in mainland China using VIIRS NTL data associated with both regional gross domestic product (GDP)and population census data.We quantitatively investigated relations between NTL,GDP,and population using a linear regression model.Our results suggest that NTL have significant positive correlations with GDP and population at different levels.Several inequality coefficients were derived from VIIRS data and statistical data at multiple spatial scales.NTL\|derived inequality coefficients enabled us to elicit more detailed information on differences in regional development at multiple levels.Our study of provinces and municipalities revealed that county\|level inequality was more significant than city\|level.The results of population\|weighted NTL inequality indicate an obvious regional disparity with NTL distribution being more unequal in China’s undeveloped western regions compared with eastern regions.Our findings suggest that given the timely and spatially explicit advantages of VIIRS,NTL data are capable of providing comprehensive information regarding inequality at multiple levels,which is not possible through the use of traditional statistical sources.