Soil hydraulic parameters estimated from satellite information through data assimilation

Leaf area index (LAI) and actual evapotranspiration (ETa) from satellite observations were used to estimate simultaneously the soil hydraulic parameters of four soil layers down to 60 cm depth using the combined soil water atmosphere plant and genetic algorithm (SWAP-GA) model. This inverse model assimilates the remotely sensed LAI and/or ETa by searching for the most appropriate sets of soil hydraulic parameters that could minimize the difference between the observed and simulated LAI (LAIsim) or simulated ETa (ETasim). The simulated soil moisture estimates derived from soil hydraulic parameters were validated using values obtained from soil moisture sensors installed in the field. Results showed that the soil hydraulic parameters derived from LAI alone yielded good estimations of soil moisture at 3 cm depth; LAI and ETa in combination at 12 cm depth, and ETa alone at 28 cm depth. There appeared to be no match with measurement at 60 cm depth. Additional information would therefore be needed to better estimate soil hydraulic parameters at greater depths. Despite this inability of satellite data alone to provide reliable estimates of soil moisture at the lowest depth, derivation of soil hydraulic parameters using remote sensing methods remains a promising area for research with significant application potential. This is especially the case in areas of water management for agriculture and in forecasting of floods or drought on the regional scale

Geoinformatics in mangrove monitoring: damage and recovery after the 2004 Indian Ocean tsunami in Phang Nga, Thailand

In the aftermath of the 2004 Indian Ocean Tsunami, it has been proven that mangrove ecosystems provide protection against coastal disasters by acting as bioshields. Satellite data have been effectively used to detect, assess, and monitor the changes in mangroves during the pre- and post- tsunami periods. However, not much information regarding mangrove restoration or reforestation is available. Rather than undertaking time-consuming fieldwork, this study proposed using geoinformatic technologies such as Remote Sensing (RS), Geographic Information System (GIS), and Global Positioning System (GPS) to monitor the mangrove recovery. The analysis focused only on the tsunami-impacted mangrove areas along the western coast of the Tai Muang, Takuapa and Khuraburi Districts of Phang Nga Province, southern region of Thailand. The results consisted of 2 parts, first: the supervised classification of main land uses, namely forest, mangrove, agricultural land, built-up area, bare soil, water body, and miscellaneous covers in ASTER images, was conducted using the maximum likelihood method with higher than 75 % for overall accuracy. Once the confusion between classes was improved in post-processing, the accuracy of mangrove class was greater than 85 % for all dates. The results showed that the mangrove area in 2005 was reduced by approximately 5 % (1054.5 ha) from 2003 due to the impact of the 2004 Indian Ocean Tsunami. Although the recovery program (replacing the same species of dead mangrove trees, mainly the <i>Rhizophora apiculata</i> Bl and <i>Rhizophora mucronata</i> Poir, in situ) had started by mid-2005, the areas gradually decreased to approximately 7–8 % in 2006 and 2010 compared with the reference year of 2003. Second, the recovery trend was observed in the Normalized Difference Vegetation Index (NDVI) fluctuation curve and the supporting field survey data. The recovery patterns were summarized into 2 categories: (i) gradually recovery, and (ii) fluctuating recovery. The gradually recovery category that implied the homogeneous pattern or uniform reforestation was observed in the seriously damaged area where most of the mangrove trees were swept away during the tsunami. This pattern covered approximately 50.35 % of the total reforested area. The NDVI time series of the uniform or homogeneous reforested mangrove at the sampled plots has gradually increased after 2005. The fluctuating recovery category that implied the heterogeneous pattern or non-uniform reforestation was observed in partially damaged areas where some of the mangrove trees were swept away and broken but still some trees were remained in the area. The heterogeneous patterns covered approximately 49.65 % of the total reforested area

Soil moisture estimation from inverse modeling using multiple criteria functions

Soil hydraulic parameters are essential inputs to agricultural and hydrologic models for simulating soil moisture. These parameters however are difficult to obtain especially when the application is aimed at the regional scale. Laboratory and field methods have been used for quantifying soil hydraulic parameters but they are proved to be laborious and expensive. An emerging alternative of estimating soil hydraulic parameters is soil moisture model inversion using remote sensing (RS) data. Although soil hydraulic parameters could not be derived directly from remote sensing, they could be quantified by the inverse modeling of RS data. In this study, we conducted a multi-criteria inverse modeling approach to estimate the rootzone soil hydraulic parameters in a rainfed rice field at depths 3, 12, 28 and 60 cm, respectively. The conditioning data used in the inverse modeling are leaf area index (LAI) and actual evapotranspiration (ETa) from satellite imageries, and soil moisture (SM) data from in situ measurements. The performances of all the model inversion experiments were evaluated against observed soil moisture in the field, and measured LAI during the growing season. The results showed that using remotely sensed LAI and ETa in the inverse modeling provided a good matching between observed and simulated soil moisture down to 28 cm depth from the soil surface. With the addition of soil moisture information from the site, the model inversion significantly improved the soil moisture simulation up to a depth of 60 cm

<RESEARCH REPORT>Decision Support System Research and Development Network for Agricultural and Natural Resource Management in Thailand : A TRF-DSS Experience

This paper aims to introduce the Decision Support System (DSS), which is an area informatics approach to area studies, and the research network complex based on DSS's application in Thailand. In particular, the paper presents and discusses current activities of the DSS research network at the national level in Thailand rather than being a theoretical and analytical study of the DSS mechanism or its cases. DSS has wide applications in Thailand, extending to various fields such as agricultural and natural resource management, economic and social management, historical and cultural preservation, and so on. DSS generally refers to a support system embedded into a computer system for providing intellectual resources with the appropriate numerical model necessary for decision making in production activities. Agricultural and natural resources have been the foundation of social and economic development in Thailand since the country's first national plan in the 1960s. Decision making to maintain agricultural productivity as well as to protect natural resources requires well-integrated data sets. The Thailand Research Fund (TRF) established a DSS research and development network (TRF-DSS) in 2002 to help various research teams in Thailand develop DSS tools and components for addressing agricultural and natural resource management issues. Using a “systems” approach, the DSS framework allows researchers and users to identify and integrate key components as well as to define databases and model-base management systems. This paper focuses on the TRF-DSS research and development network, which consists of 12 universities, two line agencies in Thailand, and a line agency in Cambodia. During 2002–10, a total of 59 research projects were granted a budget allocation of 140.1 million baht, and a budget of 15.4 million baht was allocated to support activities of the network. In addition, 10 projects were funded to carry out postproject activities, with a total budget of 1.5 million baht. More than 20 DSS tools were developed and implemented by various users, ranging from policy makers to provincial and local government agencies engaged in short- and long-term planning and management. Most DSS tools were designed to allow the integration of biophysical and socioeconomic data as well as the decision support modules for alternatives evaluation and analysis. These approaches support the choices of dynamic simulation models as well as multi-criteria analyses for modelbase software development. They also allow users to evaluate various alternatives in agricultural and natural resource management. Networking is a powerful platform for DSS research and development and may be applied to other types of research and development agenda in Thailand, such as area study projects. DSS tools contribute to an understanding of sustainability of production systems against a background of climate change, poverty reduction, and food security