Effect of differential forest management on land-use change (LUC) in a tropical hill forest of Malaysia.

Forest ownership is considered as a vital aspect for sustainable management of forest and its associated biodiversity. The Global Forest Resources Assessment 2015 reported that privately owned forest area are increasing on a global scale, but deforestation was found very active in privately owned hill forest areas of Malaysia. Penang State was purposively chosen as it has been experiencing rapid and radical changes due to urban expansion over the last three decades. In this study, analyses of land-use changes were done by PCI Geomatica using Landsat images from 1991 to 2015, future trends of land-use change were assessed using EXCEL forecast function, and its impact on the surrounding environment were conducted by reviewing already published articles on changing environment of the study area. This study revealed an annual deforestation rate of 1.4% in Penang Island since 1991. Trend analysis forecasted a forest area smaller than the current forest reserves by the year 2039. Impact analysis revealed a rapid biodiversity loss with increasing landslides, mudflows, water pollution, flash flood, and health hazard. An immediate ban over hill-land development is crucial for overall environmental safety.

Application of a GIS-/remote sensing-based approach for predicting groundwater potential zones using a multi-criteria data mining methodology.

This study integrates the application of Dempster-Shafer-driven evidential belief function (DS-EBF) methodology with remote sensing and geographic information system techniques to analyze surface and subsurface data sets for the spatial prediction of groundwater potential in Perak Province, Malaysia. The study used additional data obtained from the records of the groundwater yield rate of approximately 28 bore well locations. The processed surface and subsurface data produced sets of groundwater potential conditioning factors (GPCFs) from which multiple surface hydrologic and subsurface hydrogeologic parameter thematic maps were generated. The bore well location inventories were partitioned randomly into a ratio of 70% (19 wells) for model training to 30% (9 wells) for model testing. Application results of the DS-EBF relationship model algorithms of the surface- and subsurface-based GPCF thematic maps and the bore well locations produced two groundwater potential prediction (GPP) maps based on surface hydrologic and subsurface hydrogeologic characteristics which established that more than 60% of the study area falling within the moderate-high groundwater potential zones and less than 35% falling within the low potential zones. The estimated uncertainty values within the range of 0 to 17% for the predicted potential zones were quantified using the uncertainty algorithm of the model. The validation results of the GPP maps using relative operating characteristic curve method yielded 80 and 68% success rates and 89 and 53% prediction rates for the subsurface hydrogeologic factor (SUHF)- and surface hydrologic factor (SHF)-based GPP maps, respectively. The study results revealed that the SUHF-based GPP map accurately delineated groundwater potential zones better than the SHF-based GPP map. However, significant information on the low degree of uncertainty of the predicted potential zones established the suitability of the two GPP maps for future development of groundwater resources in the area. The overall results proved the efficacy of the data mining model and the geospatial technology in groundwater potential mapping.

Spatiotemporal dynamics of forest fires in the context of climate change: a review.

Forest fires are sudden, destructive, hazardous, and challenging to manage and rescue, earning them a place on UNESCO's list of the world's eight major natural disasters. Currently, amid global warming, all countries worldwide have entered a period of high forest fire incidence. Due to global warming, the frequency of forest fires has accelerated, the likelihood of large fires has increased, and the spatial and temporal dynamics of forest fires have shown different trends. Therefore, the impact of climate change on the spatiotemporal dynamics of forest fires has become a hot issue in the field of forest fire research in recent years. Therefore, it is of great significance and necessity to conduct a review of the research in this area. This review delves into the interactions and impacts between climate change and the spatiotemporal dynamics of forest fires. To address this issue, scholars have mainly adopted the following research methods: first, statistical analysis methods, second, the establishment of spatiotemporal prediction models for meteorology and forest fires, and third, the coupling of climate models with forest fire risk forecasting models. The statistical analysis method relies on the analysis of historical meteorological and fire-related data to study the effects of climate change and meteorological factors on fire occurrence. Meanwhile, forest fire prediction models utilize technical tools such as remote sensing. These models synthesize historical meteorological and fire-related data, incorporating key meteorological factors such as temperature, rainfall, relative humidity, and wind. The models revealed the spatial and temporal distribution patterns of fires, identified key drivers, and explored the interactions between climate change and forest fire dynamics, culminating in the construction of predictive models. With the deepening of the study, the coupling of climate models and fire risk ranking systems became a trend in the prediction of forest fire risk trends. Moreover, as the climate warms, the increased frequency of extreme weather events like heatwaves, droughts, snow and ice storms, and El Niño-Southern Oscillation (ENSO) has accelerated forest fire occurrences and raised the risk of major fires. This review offers valuable technical insights by comprehensively analyzing the spatial and temporal characteristics of forest fires, elucidating key meteorological drivers, and exploring potential mechanisms. These insights serve as a scientific foundation for preventive measures and effective forest fire management. In the face of a changing climate, this synthesis contributes to the development of informed strategies to mitigate the escalating threat of forest fires.

Radiogenic heat production estimation towards sustainable energy drive in northeastern Nigeria.

An estimate of the radiogenic heat production (RHP) across the different petrologic units of northeastern, Nigeria was previously not performed. Hence, their geothermal potentials are not widely known. However, an airborne radiometric data of equivalent uranium, (eU), equivalent thorium (eTh,) and percentage potassium (% K) acquired by Nigerian geological survey agency (NGSA) in the year 2009 was deployed in the evaluation of the RHP across the major petrologic outcrops of northeastern, Nigeria. The objective of this study is to estimate the quantity of RHP across the 13 petrologic units of the northeastern Nigerian terrain via the use of an empirical equation (RHP=ρ(0.0952Cu+0.0256CTh+0.0348Ck)). The petrologic units studied are; medium-coarse grained biotite-hornblende granites (OGe), porphyritic biotite-hornblende granites (OGp), banded gneiss (bG), charnokytes (Ch), ignimbrites (JYG), migmatites-gneiss (MG), basalts (bb), Gombe sandstones (GS), Pindiga Formation (PS), Yolde Formation (YL), Bima sandstones (BS), Keri-Keri Formation (KK), and alluvium (AL). Basic/preliminary processing such as; signal integration, signal validation, and examination of spurious data were applied prior to the RHP computation. The results of the heat production analysis performed show the range of RHP to be from 1.11µW/m3 to 3.35µW/m3 Hence, the maximum heat production value of 3.35µW/m3 was recorded along porphyritic biotite-hornblende granites (OGp) rock block, while the least value of 1.11µW/m3 was recorded over alluvium (AL) rock outcrops. Furthermore, the spatial distribution of the RHP values over the study location shows a gradual increase from the middle, low heat production (sedimentary zones) to the high heat producing areas (granitic and metamorphic zones) around eastern and western parts. The petrologic units arranged in order of decreasing magnitude of radiogenic heat generation are; OGp > MG > OGe/bG > bb > GS > Ch > JYG > BS > PS/YL > KK > AL. On a general note, the petrologic units studied were classified as low in terms of geothermal character based on comparison with other previous global RHP studies.

A comparison of radiometric correction techniques in the evaluation of the relationship between LST and NDVI in Landsat imagery.

Atmospheric corrections for multi-temporal optical satellite images are necessary, especially in change detection analyses, such as normalized difference vegetation index (NDVI) rationing. Abrupt change detection analysis using remote-sensing techniques requires radiometric congruity and atmospheric correction to monitor terrestrial surfaces over time. Two atmospheric correction methods were used for this study: relative radiometric normalization and the simplified method for atmospheric correction (SMAC) in the solar spectrum. A multi-temporal data set consisting of two sets of Landsat images from the period between 1991 and 2002 of Penang Island, Malaysia, was used to compare NDVI maps, which were generated using the proposed atmospheric correction methods. Land surface temperature (LST) was retrieved using ATCOR3_T in PCI Geomatica 10.1 image processing software. Linear regression analysis was utilized to analyze the relationship between NDVI and LST. This study reveals that both of the proposed atmospheric correction methods yielded high accuracy through examination of the linear correlation coefficients. To check for the accuracy of the equation obtained through linear regression analysis for every single satellite image, 20 points were randomly chosen. The results showed that the SMAC method yielded a constant value (in terms of error) to predict the NDVI value from linear regression analysis-derived equation. The errors (average) from both proposed atmospheric correction methods were less than 10%.

A statistical model to predict and analyze air surface temperature based on remotely sensed observations.

Air surface temperature (AST) is a crucial importance element for many applications such as hydrology, agriculture, and climate change studies. The aim of this study is to develop regression equation for calculating AST and to analyze and investigate the effects of atmospheric parameters (O3, CH4, CO, H2Ovapor, and outgoing longwave radiation (OLR)) on the AST value in Iraq. Dataset retrieved from the Atmospheric Infrared Sounder (AIRS) at EOS Aqua Satellite, spanning the years of 2003 to 2016, and multiple linear regression were used to achieve the objectives of the study. For the study period, the five atmospheric parameters were highly correlated (R, 0.855-0.958) with predicted AST. Statistical analyses in terms of ß showed that OLR (0.310 to 1.053) contributes significantly in enhancing AST values. Comparisons among selected five stations (Mosul, Kanaqin, Rutba, Baghdad, and Basra) for the year 2010 showed a close agreement between the predicted and observed AST from AIRS, with values ranging from 0.9 to 1.5 K and for ground stations data, within 0.9 to 2.6 K. To make more complete analysis, also, comparison between predicted and observed AST from AIRS for four selected month in 2016 (January, April, July, and October) has been carried out. The result showed a high correlation coefficient (R, 0.87 and 0.95) with less variability (RMSE ≤ 1.9) for all months studied, indicating model's capability and accuracy. In general, the results indicate the advantage of using the AIRS data and the regression analysis to investigate the impact of the atmospheric parameters on AST over the study area.

Impacts of particulate matter (PM2.5) on the health status of outdoor workers: observational evidence from Malaysia.

Ambient air pollution is a significant contributor to disease burden, leading to an estimated 4.2 million premature deaths and 103.1 million disability-adjusted life years (DALYs) annually worldwide. As industrialization and urbanization surge in Asia, air pollution and its corresponding health issues follow suit. Findings on disease burden in developing countries are extremely scanty. This study aimed to determine the concentration of PM2.5 and its impact on respiratory health of outdoor workers in Malaysia. A 2-cycled 3-month cohort study involving 440 participants was conducted. Workers' health status was assessed via (1) Total Ocular Symptom Score (TOSS), (2) Total Nasal Symptom Score (TNSS), (3) St. George's Respiratory Questionnaire (SGPQ), and (4) Asthma Control Test (ACT). The maximum PM2.5 concentration was measured at 122.90 ± 2.07 µg/m3 during third week of August 2016. Meanwhile, the minimum concentration was measured at 57.47 ± 3.80 µg/m3 and 57.47 ± 1.64 µg/m3 during fourth week of July 2016 and first week of August 2017 respectively. Findings revealed that TOSS, TNSS, and SGPQ changes were significantly (p < 0.05) associated with the concentration of PM2.5. Outdoor workers were more significantly (p < 0.05) affected by changes in PM2.5 compared to indoor workers with a moderate correlation (r value ranged from 0.4 to 0.7). Ironically, no significant association was found between ACT assessment and PM2.5. Collectively, our findings suggested that changes in the concentration of PM2.5 threatened the respiratory health of outdoor workers. The existing policy should be strengthened and preventive measures to be enforced safeguarding health status of outdoor workers.

Effects of Ambient Particulate Matter (PM2.5) Exposure on Calorie Intake and Appetite of Outdoor Workers.

Malaysia has been experiencing smoke-haze episodes almost annually for the past few decades. PM2.5 is the main component in haze and causes harmful impacts on health due to its small aerodynamic size. This study aimed to explore the implications of PM2.5 exposure on the dietary intake of working individuals. Two phased 13-weeks follow-up study was conducted involving 440 participants, consisting of two cohorts of outdoor and indoor workers. Ambient PM2.5 concentrations were monitored using DustTrakTM DRX Aerosol Monitor. Data on Simplified Nutritional Appetite Questionnaire (SNAQ) and 24 h diet recall were collected weekly. The highest PM2.5 concentration of 122.90 ± 2.07 µg/m3 was recorded in August, and it vastly exceeded the standard value stipulated by US EPA and WHO. SNAQ scores and calorie intake were found to be significantly (p < 0.05) associated with changes in PM2.5 exposure of outdoor workers. Several moderate and positive correlations (R-value ranged from 0.4 to 0.6) were established between SNAQ scores, calorie intake and PM2.5 exposure. Overall findings suggested that long hours of PM2.5 exposure affect personal dietary intake, potentially increasing the risk of metabolic syndromes and other undesired health conditions. The current policy should be strengthened to safeguard the well-being of outdoor workers.

Estimation of aerosol optical depth at different wavelengths by multiple regression method.

This study aims to investigate and establish a suitable model that can help to estimate aerosol optical depth (AOD) in order to monitor aerosol variations especially during non-retrieval time. The relationship between actual ground measurements (such as air pollution index, visibility, relative humidity, temperature, and pressure) and AOD obtained with a CIMEL sun photometer was determined through a series of statistical procedures to produce an AOD prediction model with reasonable accuracy. The AOD prediction model calibrated for each wavelength has a set of coefficients. The model was validated using a set of statistical tests. The validated model was then employed to calculate AOD at different wavelengths. The results show that the proposed model successfully predicted AOD at each studied wavelength ranging from 340 nm to 1020 nm. To illustrate the application of the model, the aerosol size determined using measure AOD data for Penang was compared with that determined using the model. This was done by examining the curvature in the ln [AOD]-ln [wavelength] plot. Consistency was obtained when it was concluded that Penang was dominated by fine mode aerosol in 2012 and 2013 using both measured and predicted AOD data. These results indicate that the proposed AOD prediction model using routine measurements as input is a promising tool for the regular monitoring of aerosol variation during non-retrieval time.