Chen-Chieh Feng

Estimation of Root Zone Soil Moisture Using Apparent Thermal Inertia With MODIS Imagery Over a Tropical Catchment in Northern Thailand

Understanding the variability of soil moisture content (SMC) is important for studying ecohydrological processes because it provides insights into surface water and energy balances. To comprehend the dynamics of SMC under different land use/cover types in tropical environments, this study proposes an apparent thermal inertia (ATI) approach with the Moderate Resolution Imaging Spectroradiometer (MODIS) imagery for estimating root zone SMC. Root zone SMC at depths of 10 cm, 100 cm, and 200 cm were estimated for seven sites over a northern Thailand catchment from 2005 to 2008, and compared with in situ observations. Pearson correlation coefficient and the Nash-Sutcliffe efficiency coefficient between the ATI-MODIS retrieved SMC and the measurements were respectively 0.80, 0.84, and 0.84, and 0.57, 0.537, and 0.492 for the 10 cm, 100 cm, and 200 cm depths. Root Mean Square Errors were 0.055, 0.025 and 0.029 (m3 m-3) for the three respective depths. Potential issues of the mixed land cover types within the 1 km MODIS pixel were examined; the mixed agricultural land cover types at two of the seven sites with agricultural activities such as irrigation water use might have affected the SMC estimation. Overall, the ATI-MODIS approach performed well, particularly for the 100 cm depth.

Potential malaria reemergence, northeastern Thailand.

To the Editor: The emergence and reemergence of infectious diseases are major problems for healthcare systems worldwide. Unfortunately, because accurate prediction of the occurrence of such diseases is difficult, if not impossible, surveillance and control can be carried out only after the outbreak has occurred. Predicting the likelihood of a disease outbreak should make it possible to start surveillance programs before outbreaks occur and to initiate control programs before the population has become seriously affected. We used data on changes in land use patterns to predict the likelihood of malaria reemergence in northeastern Thailand. Because natural rubber is of major economic importance and cannot be replaced by synthetic alternatives, the demand for and production of this commodity has consistently increased (1). This situation has led to changes in agricultural practices in various countries in Southeast Asia; rubber production has increased in Myanmar, Laos, Thailand, and Vietnam (1,2). Northeastern Thailand (Isaan) is a relatively poor area, and most rubber plantations belong to smallholders and provide them with a large source of income (3). In 1993, ≈284 km2 of northeastern Thailand were covered by rubber plantations; this area increased to 422 km2 in 1998 and to 948 km2 by 2003 (3). Since then, planting has increased exponentially so that, by 2006, the total area planted with rubber was >2,463 km2; new plantings expanded another ≈1,345 km2 from 2004 to 2006 and increased to a total of 5,029 km2 in 2007 (3). The plants mature ≈6 years after planting; at that stage, the trees can reach 10–12 m in height, although the growth rate depends on the physical and biotic environment (4). Deforestation in northeastern Thailand early in the last century led to an extreme reduction in the incidence of malaria (5) because the main vector mosquito in this area, Anopheles dirus sensu stricto, is forest dwelling and requires a shaded environment for its survival and reproduction (6). Currently, the northeastern part of the country is relatively free of autochthonous malaria cases except for 3 provinces that border Cambodia and Laos (5), Srisaket, Ubon Ratchathani, and Surin. In Srisaket and Ubon Ratchathani, 25% and 31%, respectively, of malaria cases are imported, particularly from Cambodia (7). Mosquitoes are sensitive to changes in environmental conditions, such as shade, temperature, and humidity. These conditions are often influenced by land use change, such as conversion of rice paddies to rubber plantations (8). In addition to providing economic benefits for the population, rubber plantations also provide suitable habitats for A. dirus s.s., perhaps even better habitats than those found in the original rain forest; new plantations lead to increased mosquito density and disease incidence (8). Thus, planting large tracts of rubber potentially increases the likelihood of the reemergence of malaria in northeastern Thailand, although a malaria vector such as A. dirus s.s. could return without reemergence of the disease (9). Should malaria return, the greatly reduced contact between the local Isaan population and Plasmodium spp. over the past ≈50 years suggests that malaria would enter a highly susceptible population, potentially leading to major health problems at the individual and regional levels. This possibility is of particular concern because several strains of Plasmodium in Thailand and surrounding countries are multidrug resistant, which leads to treatment difficulties (5). Each land use change creates different microclimatic conditions, which directly and indirectly affect the occurrence and distribution of malaria (10). Whether malaria will return as a major health threat likely depends on the size and fragmentation of the individual plantation areas. The required size of a plantation for the survival of the vector population is unclear, but large areas of plantation tend to offer dense vegetation and, therefore, high humidity and shade, which provide suitable environmental conditions for larval habitats, even during the dry season (8). Conversely, during the rainy season, conditions at the edges of fragmented forests, where human settlements are often located, become favorable for larval habitats, rendering villagers susceptible to the disease (6). In addition to changes in habitat and microclimate, social or political changes in the region may affect the transborder movement of malaria into Thailand with consequences for potential reemergence (7). Although the association between rubber plantations and malaria is well known in Southeast Asia, the potential for reemergence should receive substantially more attention from economic, agricultural, and environmental planning bodies. Changes in land use and land cover have the potential to facilitate the transmission of disease to humans. Understanding the influence of land use change on malaria occurrence is critical for shaping future surveillance and control strategies.

An ecological study of Bithynia snails, the first intermediate host of Opisthorchis viverrini in northeast Thailand

Infection with the food-borne trematodiasis, liver fluke Opisthorchis viverrini, is a major public health concern in Southeast Asia. While epidemiology and parasitic incidence in humans are well studied, ecological information on the O. viverrini intermediate hosts remains limited. This study aimed to investigate the factors affecting the distribution and abundance of the first intermediate host, Bithynia siamensis goniomphalos snails. Water quality and snails were sampled in 31 sites in Muang District, Khon Kaen Province, Thailand from June 2012 to January 2013 to characterize the B.s. goniomphalos snail habitats. Species relative abundance and Shannons diversity and evenness indices were employed to describe snail compositions and diversities across different habitat types. Statistical analyses were conducted to examine the extent to which the water quality variables and species interactions account for the relative abundance of B.s. goniomphalos snails. The results showed that the freshwater habitats of ponds, streams and rice paddies possessed significantly different abiotic water qualities, with water temperature and pH showing distinct statistical differences (P<0.05). Different habitats had different snail diversity and species evenness, with high B.s. goniomphalos snail abundance at rice paddy habitats. The differences in snail abundance might be due to the distinct sets of abiotic water qualities associated with each habitat types. The relative abundance of B.s. goniomphalos snails was found to be negatively correlated with that of Filopaludina martensi martensi snails (r=-0.46, P<0.05), underscoring the possible influence of species interaction on B.s. goniomphalos snail population. Field work observations revealed that rice planting seasons and irrigation could regulate snail population dynamics at rice paddy habitats. This study provides new ecological insights into the factors affecting Bithynia snail distribution and abundance. It bridges the knowledge gap in O. viverrini disease ecology and highlights the potential effect of anthropogenic irrigation practices on B.s. goniomphalos snail ecology.

An ontology design pattern for surface water features

Surface water is a primary concept of human experience but concepts are captured in cultures and languages in many different ways. Still, many commonalities exist due to the physical basis of many of the properties and categories. An abstract ontology of surface water features based only on those physical properties of landscape features has the best potential for serving as a foundational domain ontology for other more context-dependent ontologies. The Surface Water ontology design pattern was developed both for domain knowledge distillation and to serve as a conceptual building-block for more complex or specialized surface water ontologies. A fundamental distinction is made in this ontology between landscape features that act as containers (e.g., stream channels, basins) and the bodies of water (e.g., rivers, lakes) that occupy those containers. Concave (container) landforms semantics are specified in a Dry module and the semantics of contained bodies of water in a Wet module. The pattern is implemented in OWL, but Description Logic axioms and a detailed explanation is provided in this paper. The OWL ontology will be an important contribution to Semantic Web vocabulary for annotating surface water feature datasets. Also provided is a discussion of why there is a need to complement the pattern with other ontologies, especially the previously developed Surface Network pattern. Finally, the practical value of the pattern in semantic querying of surface water datasets is illustrated through an annotated geospatial dataset and sample queries using the classes of the Surface Water pattern.

How Do Snails Meet Fish? Landscape Perspective Needed to Study Parasite Prevalence

We echo the viewpoints presented in the recent editorials and forums in EcoHealth in 2009 and 2010 that understanding the links between water and health using biogeography can provide insights into the patterns of and the processes that give rise to the distribution of disease prevalence. In particular, we underscore the need to integrate disease ecology and biogeography using landscape ecological approaches. We use opisthorchiasis, a major public health problem in Southeast Asia, to illustrate our opinions.

Combining Geo‐SOM and Hierarchical Clustering to Explore Geospatial Data

Geo-SOM is a useful geovisualization technique for revealing patterns in spatial data, but is ineffective in supporting interactive exploration of patterns hidden in different Geo-SOM sizes. Based on the divide and group principle in geovisualization, the article proposes a new methodology that combines Geo-SOM and hierarchical clustering to tackle this problem. Geo-SOM was used to “divide” the dataset into several homogeneous subsets; hierarchical clustering was then used to “group” neighboring homogeneous subsets for pattern exploration in different levels of granularity, thus permitting exploration of patterns at multiple scales. An artificial dataset was used for validating the methods effectiveness. As a case study, the rush hour motorcycle flow data in Taipei City, Taiwan were analyzed. Compared with the best result generated solely by Geo-SOM, the proposed method performed better in capturing the homogeneous zones in the artificial dataset. For the case study, the proposed method discovered six clusters with unique data and spatial patterns at different levels of granularity, while the original Geo-SOM only identified two. Among the four hierarchical clustering methods, Wards clustering performed the best in pattern discovery. The results demonstrated the effectiveness of the approach in visually and interactively exploring data and spatial patterns in geospatial data.

Ontology‐based Qualitative Feature Analysis: Bays as a Case Study

A methodology for analyzing geographic data using the techniques of: (1) qualitative geometric abstraction; and (2) ontological analysis of geographic features is described. The first technique is a bottom-up approach to extract qualitative spatial relations from geographic representations (raster or vector) while the second technique is a top-down approach to determine which qualitative relations can possibly hold between the parts of the geographic features. The process of analyzing geographic data includes the extraction of both the features and the qualitative relations among features. These qualitative relations are then used to classify the geographic features within the “space” of ontological possibilities. In this article bays in Wisconsin and their cartographic representation are used as a running example and the subject of a case study.

Representation and discovery of building patterns: a three-level relational approach

ABSTRACT Building patterns exhibited collectively by a group of buildings are fundamental to understanding urban forms, classifying urban scenes, analyzing urban landscapes, and generalizing maps. The existing studies have used geometric homogeneity or regularity to represent and discover limited patterns for map generalization, or used interval and rectangle algebra to represent relations between spatial objects. These approaches, however, cannot illustrate how patterns are produced by using syntax or grammar (i.e. relations between buildings) to link words (i.e. buildings) into sentences (i.e. building patterns), making it impossible to represent and discover building patterns with diverse structures. This study presents a relation-based approach to formalize and discover arbitrary building patterns at three abstract levels. At the bottom level, a relative and local frame of reference is defined, and 169 basic relations are derived to represent relative positions between buildings. At the middle level, the 169 relations, qualitative angle description, and qualitative size are combined to formalize important semantic relations between two buildings, which include collinear, perpendicular, and parallel relations. At the top level, the relations at the bottom and middle levels are used to formalize three types of building patterns, including collinear patterns, the structured patterns with acceptable names, and other patterns of interest. Algorithms implementing the three levels of relations are presented and applied to demonstrate the effectiveness of the proposed approach in discovering building patterns from databases and querying building patterns. The results indicate that the relational approach is generic to effectively represent and discover building patterns with arbitrary structures. In addition, it complements the existing geometric methods for recognizing building patterns, and the interval and rectangle algebra for representing building relations.

Patterns and trends in land-use land-cover change research explored using self-organizing map

Research on land change has a long history, has generated numerous publications and continues to receive international research attention. To facilitate the understanding of the patterns and trends of land-change research, this article uses a content-based text-retrieval approach and self-organizing map to analyse more than 700 peer-reviewed remote-sensing and natural-science papers on land-use/cover change (LUCC) from the past two decades. We present the results in map-like displays and discuss papers within the identified clusters to examine the research activities. A new cluster of research, which has emerged in the last 5 years of analysis, has focused on mixed-pixel issues for land-use/cover mapping, particularly in the context of forest catchments. Studies of LUCC consequences after 2000 have been concerned with the effects of forest conversion on soil-nutrient pools and nitrate cycling. Incorporating information on resolutions and extents into the representations reveals a dominant scale of analysis for some research activities. Analysing time frames of examination in the papers suggests that research on long-term LUCC consequences started to use presettlement land survey records. Few attempts, however, have been made to investigate the uncertainties in the historical sources of information for LUCC research, thereby presenting a future research topic.

Classifying natural-language spatial relation terms with random forest algorithm

ABSTRACT The exponential growth of natural language text data in social media has contributed a rich data source for geographic information. However, incorporating such data source for GIS analysis faces tremendous challenges as existing GIS data tend to be geometry based while natural language text data tend to rely on natural language spatial relation (NLSR) terms. To alleviate this problem, one critical step is to translate geometric configurations into NLSR terms, but existing methods to date (e.g. mean value or decision tree algorithm) are insufficient to obtain a precise translation. This study addresses this issue by adopting the random forest (RF) algorithm to automatically learn a robust mapping model from a large number of samples and to evaluate the importance of each variable for each NLSR term. Because the semantic similarity of the collected terms reduces the classification accuracy, different grouping schemes of NLSR terms are used, with their influences on classification results being evaluated. The experiment results demonstrate that the learned model can accurately transform geometric configurations into NLSR terms, and that recognizing different groups of terms require different sets of variables. More importantly, the results of variable importance evaluation indicate that the importance of topology types determined by the 9-intersection model is weaker than metric variables in defining NLSR terms, which contrasts to the assertion of ‘topology matters, metric refines’ in existing studies.