Half of Asian tropical forests were disturbed in the last century resulting in the dominance of secondary forests in Southeast Asia. However, the rate at which biomass accumulates during the recovery process in these forests is poorly understood. We studied a forest landscape located in Khao Yai National Park (Thailand) that experienced strong disturbances in the last century due to clearance by swidden farmers. Combining recent field and airborne laser scanning (ALS) data, we first built a high-resolution aboveground biomass (AGB) map of over 60 km 2 of forest landscape. We then used the random forest algorithm and Landsat time series (LTS) data to classify landscape patches as non-forested versus forested on an almost annual basis from 1972 to 2017. The resulting chronosequence was then used in combination with the AGB map to estimate forest carbon recovery rates in secondary forest patches during the first 42 years of succession. The ALS-AGB model predicted AGB with an error of 14 % at 0.5 ha resolution (RMSE = 45 Mg ha −1 ) using the mean top-of-canopy height as a single predictor. The mean AGB over the landscape was 291 Mg ha −1 , showing a high level of carbon storage despite past disturbance history. We found that AGB recovery varies non-linearly in the first 42 years of the succession, with an increasing rate of accumulation through time. We predicted a mean AGB recovery rate of 6.9 Mg ha −1 yr −1 , with a mean AGB gain of 143 and 273 Mg ha −1 after 20 and 40 years, respectively. This rate estimate is about 50 % larger than the rate prescribed for young secondary Asian tropical rainforests in the 2019 refinement of the 2006 IPCC guidelines for national greenhouse gas inventories. Our study hence suggests that the new IPCC rates, which were based on limited data from Asian tropical rainforests, strongly underestimate the carbon potential of forest regrowth in tropical Asia. Our recovery estimates are also within the range of those reported for the well-studied Latin American secondary forests under similar climatic conditions. This study illustrates the potential of ALS data not only for scaling up field AGB measurements but also for predicting AGB recovery dynamics when combined with long-term satellite data. It also illustrates that tropical forest landscapes that were disturbed in the past are of utmost importance for the regional carbon budget and thus for implementing international programs such as REDD+.Published by Copernicus Publications on behalf of the European Geosciences Union.

This study analyzes 24 climate extreme indices over North Thailand using observed data for daily maximum and minimum temperatures and total daily rainfall for the 1960-2010 period, and HadCM3 Global Climate Model (GCM) and PRECIS Regional Climate Model simulated data for the 1960-2100 period. A statistical downscaling tool is employed to downscale GCM outputs. Variations in and trends of historical and future climates are identified using the nonparametric Mann-Kendall trend test and Sen's slope. Temperature extreme indices showed a significant rising trend during the observed period and are expected to increase significantly with an increase in summer days and tropical nights in the future. A notable decline in the number of cool days and nights is also expected in the study area while the number of warm days and nights is expected to increase. There was an insignificant decrease in total annual rainfall, number of days with rainfall more than 10 and 20 mm. However, the annual rainfall is projected to increase by 9.65% in the future 2011-2099 period compared to the observed 1960-2010 period.

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Abstract:To date many positioning systems are available to determine or track a user's location; three main categories include Global Positioning System (GPS), wide area location system and indoor positioning system. GPS has its limitation (poor signals) in indoor or urban uses, while wide-area location systems are cellular networks dependent. For indoor positioning system many approaches like infrared sensing, radio frequency, ultrasonic etc. are proposed; each of these methods has their own advantages and disadvantages. Considering cost-effectiveness, speed and accuracy a recent interest is growing on using wireless technology. A Wireless Local-Area Network (WLAN) based positioning system has some distinct advantages like low-cost and wider area coverage. This researrch propose a system to determine the location of a mobile terminal or a handheld PDA in high speed, low-cost wireless networks by using the wireless communications infrastructure. The experimental set up used an indoor wireless facility of an auditorium, where signals from three Access Points (APs) were recorded to train a position determination model to calculate and map a position. Grid model was applied and compare the resulted position of a client. A handheld PDA equipped with application software was the client device. The accuracy assessment has been performed to identify the distance errors and the average distance error was found lowest for the grid model. The results of the experiments reveal that the accuracy of 0.05 m can be achieved.

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Background: India has a rising rate of malaria as well as a high mortality rate despite awareness and efforts being focused on the issue. Some regions are profoundly affected than others, such as in Odisha, where the prevalence of malaria is nearly a third of the whole country. This study investigated the influence of climate factors on the incidence of malaria in the Sundargarh district in the state of Odisha, India. Methods: Block-wise observed station rainfall data was sourced from the Special Relief Commissioners' (SRC) web portal. Gridded surface maximum temperature and relative humidity data were accessed from the European Center for Medium-range Weather Forecast (ECMWF) reanalysis data archive. Malaria incident data were collected from the Directorate of Public Health, Government of Odisha. WEKA machine learning tool with two classifier techniques, Multi-Layer Perceptron (MLP) and J48 with 10-fold cross-validation, percentile split (66%), and supplied test options, were used for the Malaria prediction. A comparative analysis was carried out on both techniques to ascertain the superior model amongst the two, concerning the prediction accuracy of malaria in the context of a varying climate. Classifier accuracy, Root Mean Square Error (RMSE), Kappa, and ROC scores were the indicators used for the analysis. Results: The results suggested that J48 had exhibited a better skill to MLP and illustrated less error with a positive kappa. In particular, the 10-fold cross-validation method had better performance over the percentile Spilt (66%) and supplied test options. J48 demonstrated less error (RMSE = 0.6), better kappa = 0.63, and higher accuracy = 0.71), suggesting it as most suitable model. Further, seasonal temperature and humidity variation had shown a better association with malaria incidents in comparison to rainfall.Conclusion: The performance of the machine learning methods for Sundargarh was particularly better during the monsoon and post-monsoon when the events are at the peak. The results were encouraging for the utilization of climate forecast for prediction of malaria incidences. It is thus recommended that the J48 classifier machine learning technique could be adopted for the development of malaria early warning system.

IJICT is a refereed journal in the field of information and communication technology (ICT), providing an international forum for professionals, engineers and researchers. IJICT reports the new paradigms in this emerging field of technology and envisions the future developments in the frontier areas. The journal addresses issues for the vertical and horizontal applications in this area.IJICT is an Open Access-only journal and article processing charges (APCs) apply.

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