Drought is an abiotic stress that inhibits plant growth and development and, therefore, declines crop productivity, as seen in maize plant. The aim of this investigation was to identify the candidate maize varieties that can be grown under water limited conditions using physio-morphological and yield attributes. Eight genotypes of maize including ‘Suwan4452’ (drought tolerant) as a positive check, ‘CP301’, ‘CP-DK888’, ‘DK7979’, ‘DK9901’, ‘Pac339’, ‘S7328’, and ‘Suwan5’ were selected as test plants. Physiological, biochemical and morphological characteristics at seedling (24 day after sowing; DAS) and reproductive (80 DAS) developmental stages of plants under 20-day water withholding (WD), and yield traits at harvesting period were analysed. Leaf temperature in each genotype increased with the degree of water deficit stress, leading to leaf chlorosis, and reduction in maximum quantum yield of PSII (Fv/Fm), photon yield of PSII (ΦPSII), net photosynthetic rate (Pn), overall growth and yield. Pn and stomatal conductance (gs) in drought tolerant genotype, ‘Suwan4452’, were decreased by 19.1% and 18.6%, respectively, whereas these in drought sensitive, ‘Pac339’, were significantly declined by 53.9% and 61.8%, respectively. Physio-morphological parameters, growth performance and yield-related traits of maize genotypes grown under water deficit conditions and well-watered conditions were subjected to Ward’s cluster method for identification of water deficit tolerant cultivars. Maintaining photosynthetic abilities, osmotic adjustment and CWSI in drought tolerant genotypes of maize were evidently demonstrated to keep overall growth performance and yield attributes. Based on multivariate cluster analysis and PCA (principal component analysis), ‘Suwan4452’, ‘CP-DK888’ and ‘S7328’ were categorized as drought tolerant genotypes whereas ‘Suwan5’, ‘Pac339’, ‘DK7979’, ‘CP301’ and ‘DK9901’ were identified as drought susceptible cultivars. Hybrid maize cvs. ‘CP-DK888’ and ‘S7328’ may further be suggested to be grown in the rainfed area without irrigation.

Unmanned aerial vehicles (UAVs) equipped with multi-sensors are one of the most innovative technologies for measuring plant health and predicting final yield in field conditions, especially in the water deficit situation in rain-deprived regions. The objective of this investigation was to evaluate the individual plant and canopy-level measurements using UAV imageries in three different genotypes, Suwan4452 (drought-tolerant), Pac339, and S7328 (drought-sensitive) of maize (Zea mays L.) at vegetative and reproductive stages under WW (well-watered) and WD (water deficit) conditions. At the vegetative stage, only CWSI (crop water stress index) of Pac339 and S7328 under WD increased significantly by 1.86- and 1.69-fold over WW, whereas the vegetation indices (EVI2 (Enhanced Vegetation Index 2), OSAVI (Optimized Soil-Adjusted Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), NDRE (Normalized Difference Red Edge Index), and NDVI (Normalized Difference Vegetation Index)) derived from UAV multi-sensors did not vary. At the reproductive stage, CWSI in drought-sensitive genotype (S7328) under WD increased by 1.92-fold over WW. All the vegetation indices (EVI2, OSAVI, GNDVI, NDRE, and NDVI) of Pac339 and S7328 under WD decreased when compared with those of Suwan4452. NDVI derived from GreenSeeker® handheld and NDVI from UAV data was closely related (R2 = 0.5924). An increase in leaf temperature (Tleaf) and reduction in NDVI of WD stressed maize plants was observed (R2 = 0.5829) leading to yield loss (R2 = 0.5198). In summary, a close correlation was observed between the physiological data of individual plants and vegetation indices of canopy level (collected using a UAV platform) in drought-sensitive genotypes of maize crops under WD conditions, thus indicating its effectiveness in the classification of drought-tolerant genotypes.

Rice is an important economic and staple crop in several developing countries. Indica rice cultivars, ‘KDML105’ and ‘RD6’ are clear favourites, popular throughout world for their cooking quality, aroma, flavour, long grain, and soft texture, thus consequently dominate major plantation area in Northeastern region of Thailand. The objective of present study was to validate UAV (unmanned aerial vehicle)-derived information of rice crop traits with ground truthing non-destructive measurements in these rice varieties throughout whole life span under field environment. Plant height of cv. ‘KDML105’ was more than cv. ‘RD6’ for each respective stage. Whereas, number of tillers per clump in ‘KDML105’ exhibited stability at each developmental stage, which was in contrast to ‘RD6’ (increased continuously). Moreover, 1,000 grain weight, total grain weight and aboveground biomass were higher in ‘KDML105’ than in ‘RD6’ by 1.20, 1.82 and 3.82 folds. Four vegetative indices, ExG, EVI2, NDVI and NDRE derived from UAV platform proved out to be excellent parameters to compare KDML105 and RD6, especially in the late vegetative and reproductive developmental stages. Positive relationships between NDVI and NDRE, NDRE and total yield traits, as well as NDVI and aboveground biomass were demonstrated. In contrast, total chlorophyll pigment in cv. ‘RD6’ was higher than in cv. ‘KDML105’ leading to negative correlation with NDVI. ‘KDML105’ reflected rapid adaptation to Northeastern environments, leading to maintenance of plant height and yield components. Vegetation indices derived from UAV platform and ground truth non-destructive data exhibited high correlation. ‘KDML105’ was rapidly adapted to NE environments when compared with ‘RD6’, leading to maintenance of physiological parameters (detecting by UAV), the overall growth performances and yield traits (measuring by ground truth method). This study advocates harnessing and adopting the approach of UAV platform along with ground truthing non-destructive measurements of assessing a species/cultivars performance at broad land-use scale.

Cassava is a tropical storage root crop, a source of carbohydrate and alternative energy. It has been classified as “drought tolerant plant” for the whole life cycle, except during the root initiation stage (120-150 DAP). Leaf area index (LAI) is one of the most parameters representing the overall growth and yield prediction in cassava. The aim of this investigation was to validate the physiological and growth performance of cassava in responses to water deficit stress in the field trial as well as to investigate the leaf area index as an important factor to cassava growth and storage root bulking. Leaf relative water content in cassava declined significantly upon a long period of water withholding, and regulated non-photochemical quenching (NPQ), leading to chlorophyll degradation, reduced number of leaves and limited leaf area index (LAI) and loss of storage root yield when compared with well-irrigated plants. Non-destructive leaf area estimation model under water deficit stress condition using spectral reflectance to determine the LAI and VIs was validated. The Ratio Vegetation Index (RVI) was suitable model with high coefficient of determination (R2 = 0.89). However, the RVI as LAI at 150 DAP (120 d water withholding) could be considered as the critical point to indicate cassava growth and yield performance. Based on the results, cassava growth, biomass and yield in the different environments may further be investigated, taking into consideration the genotypic variation and using remote sensing technology for rapid monitoring and accurate and cost-effective data assessment. ********* In press - Online First. Article has been peer reviewed, accepted for publication and published online without pagination. It will receive pagination when the issue will be ready for publishing as a complete number (Volume 47, Issue 3, 2019). The article is searchable and citable by Digital Object Identifier (DOI). DOI link will become active after the article will be included in the complete issue. *********

The effect of surface soil temperature on cassava physiological characteristics under different water regimes was studied. The aim of this research was to study the relationship between the surface soil temperature and cassava physiological characteristics under irrigated and non-irrigated conditions. The field experiment plots were conducted in Khon Kaen province. The surface soil temperatures and plant physiology during the three to five months after growing were measured using infrared thermography and Licor 6400XT, respectively. The data were then compared and correlated between different water regimes. The results showed that the surface soil temperature of the non-irrigated plot was higher than the irrigated plot. A negative correlation was observed among surface soil temperature, net photosynthesis, stomatal conductance and transpiration rate. However, the positive correlation between the surface soil temperature, the air vapor pressure deficit and the leaf temperature were detected. The critical of surface soil temperature affected to the cassava physiological characteristics was at about 30°C. When the surface soil temperature increased more than 30°C, the air vapor pressure deficit and the leaf temperature increased, while the net photosynthesis, stomata conductance and transpiration rate were gradually decreasing. Therefore, the surface soil temperature was an important factor affecting cassava physiology and growth development. Water and soil management, therefore could reduce surface soil temperature in hot season and leading to good growth development and yield increasing. Keywords: Infrared thermography, Cassava, Plant physiology, Surface soil temperature

Water deficit stress is a major factor that inhibits the overall growth and development in cassava (Manihot esculenta), leading to decreased storage root yield. We conducted a study to investigate whether thermal sensing could be used to indicate water deficit stress and the health and yield of cassava crops in field. The objective of the study was to use thermal imaging to determine relationship between crop water stress index (CWSI) and physiological changes, and to identify the critical CWSI point in fields of cassava cv. Rayong 9 under well-irrigated and water-deficit conditions. At the time of storage root initiation (85 DAP [day after planting]), thermal imagery was collected and the physiological changes and growth characters were measured prior to storage root harvesting (162 DAP). Thermal infrared imager was used to measure the canopy temperature and CWSI of cassava plants. Net photosynthetic rate (Pn), stomatal conductance (gs) and transpiration rates (Tr) of cassava plants under water deficit conditions for 29 d (114 DAP) were significantly decreased, leading to delayed plant growth as compared to those under well-irrigated conditions. In contrast, air vapor pressure deficit (VPDair) and CWSI in drought-stressed plants were higher than well irrigated plants. High correlations between Tr/gs/Pn and CWSI were observed. The study concludes that CWSI is a sensitive indicator of water deficit stress caused due to stomatal function. Abbreviations: CWSI: crop water stress index; DAP: day after planting; Pn: net photosynthetic rate; gs: stomatal conductance; Tr: transpiration rate; VPDair: air vapor pressure; RMSE: root mean square error

Soil salinity is one of the main agricultural problems which expand to larger areas. Soil scientists categorize salinity level by electrical conductivity (EC) measurement. However, field measurements of EC require extensive time, cost and experiences. Remote sensing is one suitable option to investigate and collect spatial data in larger areas. Many researches estimated soil moisture through microwave, but there are fewer studies which mentioned about direct relationship between EC and backscattering coefficient (BC). Thus, this study aims to propose the estimation of EC directly from BC of microwave. The relationship between EC obtained from field survey and BC from microwave is non-linear, artificial neural network (ANN) is one technique proposed in this study to figure out EC and BC relationship. ANN uses multilayer of interconnected processing resulting in EC value with high accuracy which is acceptable. For this reason, ANN model can be successfully utilized as an effective tool for EC estimation from microwave.

Nowadays, hyper spectral image software be- comes widely used. Although hyper spectral images provide abundant information about bands, their high dimensionality also substantially increases the computational burden. An important task in hyper spectral data processing is to reduce the redundancy of the spectral and spatial information without losing any valuable details. In this paper, we present band selection technical using principal components analysis (PCA) and information gain (IG) for hyper spectral image such as small multi-mission satellite (SMMS). Band selection method in our research not only serves as the first step of hyper spec- tral data processing that leads to a significant reduction of computational complexity, but also a invaluable research tool to identify optimal spectral for different satellite applications. In this paper, an integrated PCA and IG method is proposed for hyper spectral band selection. Based on tests in a SMMS hyper spectral image, this new method achieves good result in terms of robust clustering.

Although hyperspectral imagery provides abundant information about bands, their high dimensionality also substantially increases the computational burden. An interesting task in hyperspectral data processing is to reduce the redundancy of the spectral and spatial information without loss of any valuable details. In this paper, a band selection technique with principal components analysis, maxima-minima functional, and information gain for hyperspectral imagery such as small multi-mission satellite imagery is presented. Band selection method in the present research does not only serve as the first step of hyperspectral data processing that leads to a significant reduction of computational complexity but also an invaluable research tool to identify optimal spectral for different satellite applications. In this paper, an integrated PCA, maxima-minima functional method and information gain is proposed for hyperspectral band selection. Based on tests in SMMS hyperspectral imagery, this new method achieves good result in terms of robust clustering.

Anomaly detection has always been a hot research field of data mining.Anomaly detection is important in many fields.Automatic determination of the anomaly cluster is often needed to eliminate that anomaly cluster.In this paper, a method has been developed to determine the anomaly regions in satellite image using a data mining algorithm based on the co-occurrence matrix technique in order to determinate that anomaly.Our method consists of four stages, the first stage estimate a number of cluster by co-occurrence matrix, the second stage cluster dataset by automatic clustering algorithm, the third stage detect anomalous clusters by threshold value and the final stage defines clusters, which are lower than threshold value, to be anomalous clusters.The proposed method was tested using data from unknown number of clusters with multispectral satellite image in Thailand.The results from the tests confirm the effectiveness of the proposed method in finding the anomaly regions.

Papaya is an economically important plant in Thailand for domestic consumption and export. However, papaya is extremely susceptible to disease caused by the papaya ring spot virus. Although transgenic papaya has been developed, commercial cultivation of transgenic plants in Thailand is still illegal. One concern is cross-pollination to conventional varieties. In this study, windborne-pollen dispersion of papaya (Carica papaya L.) was investigated using geographic information systems (GIS) and remotely sensed data. Pollen traps were placed around a papaya plot in eight geographic directions, with radiuses varying from 5 to 900 m from the plot. Pollen counts were made for 12 different dates, and data were input into a GIS database. The distribution of pollen and its relation to land use were analyzed using land use data obtained from Quickbird imagery acquired during 2007. Comparative analyses of pollen dispersal, wind direction, and speed were made using data collected from a micro-climatic station set up at a papaya plot. The furthest distance from the plot that pollen was found was at 0.9 km, a distance at which only 1 pollen grain was found. The number of pollen grains carried by wind decreased as distance increased. The direction of dispersal was not in accordance with wind direction data. Most pollen grains were found in agricultural areas and bare land. The total number of pollen grains found in exposed areas was considerably higher than the total found in areas sheltered by dense tree lines.

In the end of the day, meteorological data and environmental data becomes widely used such as plant varieties selection system. Variety plant selection for planted area is of almost importance for all crops, including varieties of sugarcane. Since sugarcane have many varieties. Variety plant non selection for planting may not be adapted to the climate or soil conditions for planted area. Poor growth, bloom drop, poor fruit, and low price are to be from varieties which were not recommended for those planted area. This paper presents plant varieties selection system for planted areas in Thailand from meteorological data and environmental data by the use of decision tree techniques. With this software developed as an environmental data analysis tool, it can analyze resulting easier and faster. Our software is a front end of WEKA that provides fundamental data mining functions such as classify, clustering, and analysis functions. It also supports pre-processing, analysis, and decision tree output with exporting result. After that, our software can export and display data result to Google maps API in order to display result and plot plant icons effectively. Keywords—Plant Varieties Selection System, decision tree, Expert Recommendation

In this study, model testing, calibration, and validation of the Modified Universal Soil Loss Equation (MUSLE) model were carried out in Khun Satan catchment, Thailand, for the estimation of sediment yield in plots of different slopes using the S factor from the classic Universal Soil Loss Equation (USLE) and the McCool model, as the calibration parameter. In situ experimental plots were established with five different inclinations (9, 16, 25, 30, and 35%), with the other model parameters (e.g., erodibility, conservation practice, etc) being treated as constants. Sediment yields were recorded from 27 rainfall events between July and October 2003. It was found that both the classic USLE and the McCool models over-estimated sediment yields at all slope angles. However, the classic USLE produced a smaller relative error (RE) than the McCool model at plots with slopes of 9 and 16%, while the McCool model performed better at plots with slopes over 16% inclination. The calibration of the model using the S factor was then made for two slope range intervals, and the slope algorithm was later modified. The calibrated S factors were used in the prototype model for slope ranges of 9 to 16% using classic USLE and for slopes from 16 to 35% using the McCool model. The results revealed that an acceptable accuracy can be obtained through model calibration. The model validation based on paired t-test, on the other hand, showed that there was no difference (α = 0.05) between measured and estimated sediment yield using both models. This result indicates that if data on various slope gradients are limited, MUSLE needs to be calibrated before application, especially with respect to topographic factors, in order to obtain an accurate estimate of the sediment yield from individual rainfall events.

Occurrence of shadowy pixels in remote sensing images is a common phenomenon particularly with passive sensors. In these cases, analysts may treat these pixels as a separate land cover class. This may result in the loss of information present in the shadowy pixels A better approach may be to correct light intensity values in shadowy pixels and use the light-corrected image to produce a land cover map. Most light intensity correction algorithms are not designed to optimize the classification performance. Consequently, the accuracy of a resulting land cover map may be degraded. As a result, this paper proposes a new approach to simultaneously determine the land cover map and determine the light intensity value of shadowy pixels based on a Markov random field model. With this approach, the light intensity correction is performed such that the classification accuracy is maximized. The outputs of the proposed algorithm are a land cover map and shadow-free remote sensing image.

Thailand is located between 5°30' and 20°30'N latitudes and between 97°30' and 105°30'E longitudes. Geographically, the country can be divided into northern, northeastern, central, and southern regions. Most of the country experiences distinct wet and dry climates, except some parts of the southern region, which experience a wet and humid climate. Of the country’s total area (514,000 km2), 41% is under agricultural use (Office of Agricultural Economics, 1999) with 92% of it being rainfed. Drought normally occurs during the hot season (March–April) and sometimes during dry season (November–April) due to inadequate rains. In recent times, the occurrence of drought has increased in Thailand, threatening sustainability of agricultural production. According to Department of Local Administration (1998), droughts of varying intensity occur in 67 out of 76 provinces of Thailand almost every year. During the period from 1987 to 1997, drought impacted a total of 5.44 million ha of agricultural land, causing $1.4 billion in losses. Droughts of varying intensity or severity occur in different regions of Thailand. A drought is categorized as severe, moderate, slight, or none drought using a radiative index (RI) determined during the rainy season (May– October). The RI for a region is determined using the number of rainy days, percentage of irrigated area, groundwater availability, topography, land use, soil, drainage density, and watershed size. If RI ranges from 1.0 to 1.2 for 15 consecutive days for a region or area, the region is said to be affected by slight drought. If RI exceeds 1.2 for 30 consecutive days, the region is considered to be affected by moderate drought, and if RI exceeds 1.0 for more than 30 consecutive days, severe drought is said to have occurred in the region. Using these criteria, the percentage of area affected by different drought categories has been determined in Thailand. It can be observed from table 25.1 that the northeastern region is the most droughtprone in Thailand. A drought index, D, is also used to monitor drought conditions in Thailand.

The advancement of satellite remote sensing has offered greater potential for mapping volcanic deposits. Although the development of weather‐independent microwave remote sensing has made the frequent detection over large area detection of deposits using SAR intensity image is sometimes hindered by ambiguities and noise. The ambiguities occur in volcanic deposit areas covered by young vegetation and that give either high or low backscatter depending upon their orientation. For this reason coherent images were integrated with SAR intensity images to extract more reliable information about volcanic deposited area. Besides, the layover areas due to the viewing geometry of SAR make difficulties to map the volcanic deposits on every side of the mountain. To avoid the influence of layover effects fusion techniques of ascending and descending pass SAR intensity and coherent images were developed. Using the fused images with an optical image, a color composite was developed to identify the areas affected by an eruption. In this color composite, especially vegetation damages can be easily identified.

The new method developed in this study was the use of nighttime AVHRR thermal data as an additional channel in classifying land cover types with respect to improve classification accuracy. The NDVI, ordinary brightness temperatures and land surface temperature, were examined using two-classification algorithms namely Maximum Likelihood and Decision Tree. Various band inputs were comparatively assessed for the classification accuracy. The results of classification in both cool and hot seasons showed that overall accuracy obtained by using combination of day and nighttime data was better than that obtained by using only daytime data. Using a combination of three bands including NDVI, daytime LST, and nighttime LST gave the best accuracy. Using three bands and only daytime data yielded a lower accuracy compared to when using only two bands, but with day and nighttime data. Although an overall accuracy of using only daytime and using both day and nighttime data was not remarkably different, the accuracies when adding a nighttime data were notably increased such as in a case of forest and built up classes. The nighttime data can well classify (1) forest from densely grown crops, (2) deciduous forest in hot season from sparsely growing crops and harvested agricultures, and (3) built up from harvested land. The results also indicated that the proposed approach using nighttime data was effective as a new classification method in vegetation environment associated with landscape. In addition, this study suggested an integrated approach involving day and nighttime data to monitor urbanization and heat island.

Abstract How to make road extraction automatically remains a great challenge up to now. Published researches show that existing approaches are partly available for dealing with shadowed parts of roads especially to rural roads. In this paper, a new approach is proposed to apply laser range data to automatically extract urban roads from digital images. The extraction process is composed of three steps. The first step is working on laser images, where parameters like height and edges of high objects are obtained from the original laser images. At the same time, a new concept called “associated road line (ARL) graph” is developed to assist the road extraction from digital images. The second step deals with digital images, where road edges are obtained through Canny operator. The result proved that ARL graph is a homeomorphous mapping of real road line (RRL) graph. The gaps between segments of RRL are bridged through parts of its ARL through topological transformation. Finally, the shadowed parts of RRL are reconstructed with the help of spline approximate algorithm. The preliminary result proved that this approach is effective and has a potential advantage for efficient extraction of roads from complex patterns of urban road network.