Land characteristics and land suitability assessment for Styrax sp. in Humbang Hasundutan Regency, North Sumatra, Indonesia.

Styrax sp. is a valuable latex-producing plant in North Sumatra Province, Indonesia, which requires sustainable land use practices to maintain its production and ecological value. This study aimed to assess the land characteristics and suitability of Styrax sp. in Humbang Hasundutan Regency, North Sumatra Province, to support the development of sustainable land use practices for the cultivation of this plant. The study employed a survey method to collect soil samples and evaluated the land suitability using a matching method and Geographic Information System (GIS) techniques. The results showed that Styrax sp. had moderately suitable land suitability in three villages in Humbang Hasundutan Regency. The limiting factors for land suitability were identified as rooting media, nutrient retention, water availability, and erosion hazard. These findings have important implications for the development of sustainable land use practices for Styrax sp. cultivation in the region, which can contribute to the conservation of this valuable plant species and the maintenance of ecological balance. Overall, this study provides valuable insights into the land characteristics and suitability of Styrax sp. in North Sumatra Province and highlights the importance of sustainable land use practices for the conservation of valuable plant species.

Optimizing timber transportation planning for timber harvesting using bees algorithm in Malaysia.

Suitable extraction technique and the least cost while reducing the environmental impact is the primary concern in timber transportation planning in undulate topography. Two types of extraction machines with unique characteristics to be applied in timber harvest area in Malaysia is combined for timber harvesting with the aim each machine will extract timber suitable to their ability. A Bees Algorithm (BA) was proposed to find an optimum TTP for timber extraction, forest road, and landing locations with grid cell-sized 10 m × 10 m and attributed with fixed and variable costs. The result shows the log fisher (1351 timbers) as a preferable extraction technique with total cost of RM 86,551.73 than the crawler tractor (206 timbers); the timber extraction route is 2630 m for the log fisher and 9860 m for the crawler tractor with total cost of RM 10,453.03. The model finds a suitable timber extraction technique and estimates the extraction costs. Further studies are required to compare the BA with other optimization methods for better results.

Desertification prediction with an integrated 3D convolutional neural network and cellular automata in Al-Muthanna, Iraq.

Desertification is a major environmental issue all over the world, and Al-Khidhir district, Al-Muthanna, in the south of Iraq is no exception. In mapping, assessing, and predicting desertification, remote sensing and geospatial solutions (spatial analysis, machine learning) are crucial. During 1998-2018, this study employed satellite images from Landsat TM, ETM + , and OLI to map and predict desertification in the Al-Khidhir district. The year 2028 was chosen as the target date. Prediction models were constructed using a 3D convolutional neural network (3D CNN) and cellular automata (CA) techniques. In addition to the historical land cover maps, the model incorporated desertification indicators identified as important in the study, including geology, soil type, distance from waterways, elevation, population density, and Normalized Difference Vegetation Index (NDVI). Several accuracy metrics were used to evaluate the models, including overall accuracy (OA), average accuracy (AA), and the Kappa index (K). The simulated and actual land cover maps from 1998 and 2008 were used to evaluate the desertification prediction models. The 3D CNN model outperforms the typical 2D CNN for both the 2008 and 2018 images, according to the results. For the 2008 image, the 3D CNN model achieved 89.675 OA, 69.946 AA, and 0.781 K, while the 2018 image achieved 91.494 OA, 75.138 AA, and 0.770 K. The 2D CNN model performed a little worse than the 3D CNN model. The results of the change assessment showed that between 1998 and 2008, agricultural land was the dominant class (39%, 47.4%, respectively). The bare land, however, was the most dominant class in 2018, accounting for 46.6% of the total, compared to 26.2% for agricultural land. The spatial distribution characteristics of desertification in the Al-Khidhir, in the year 1998, were prevalent in the area's south (25.9%). In the following 10 years, desertification has spread to the surrounding territories. In the year 2008, desertification increased in the north of the study area (50.8%). Unless the local administration of Al-Khidhir district establishes desertification control strategies, this study suggests that the extent of bare land could expand in 2028 (54.1%).

Evaluating the impacts of land use/land cover changes across topography against land surface temperature in Cameron Highlands.

The Cameron Highlands has experienced multiple land encroachment activities and repeated deforestation, leading to extensive land-use and land-cover change (LULCC) during the past six decades. This study aims to determine the LULCC against topography in Cameron Highlands between 2009 and 2019 by using geospatial techniques to analyze Landsat 7 (ETM+) and 8 (OLI/TIRS), ASTER GDEM and MODIS imaging sensors. The results showed a decline of 35.98 km2 in primary forests over ten years across the Cameron Highlands, while agricultural lands and urban areas flourished by a rise of 51.61 km2 and 11.00 km2 respectively. It can be noted that the elevation most affected is between 1000 and 1500 m, across all classes. Further results showed the expansion of both agriculture and urban development onto slopes above 35°, leading to an instability of soil structure. In a comparison of the base years of 2009 with 2019, mean LST results have shown temperatures rising by 7.5°C, while an average between 3 and 4°C across the region is recorded. The results obtained provide new information for government bodies and land planners to coordinate their actions without further jeopardizing the environment of the Cameron Highlands.

Development of an indoor air quality checklist for risk assessment of indoor air pollutants by semiquantitative score in nonindustrial workplaces.

BACKGROUND: To meet the current diversified health needs in workplaces, especially in nonindustrial workplaces in developing countries, an indoor air quality (IAQ) component of a participatory occupational safety and health survey should be included. OBJECTIVES: The purpose of this study was to evaluate and suggest a multidisciplinary, integrated IAQ checklist for evaluating the health risk of building occupants. This IAQ checklist proposed to support employers, workers, and assessors in understanding a wide range of important elements in the indoor air environment to promote awareness in nonindustrial workplaces. METHODS: The general structure of and specific items in the IAQ checklist were discussed in a focus group meeting with IAQ assessors based upon the result of a literature review, previous industrial code of practice, and previous interviews with company employers and workers. RESULTS: For practicality and validity, several sessions were held to elicit the opinions of company members, and, as a result, modifications were made. The newly developed IAQ checklist was finally formulated, consisting of seven core areas, nine technical areas, and 71 essential items. Each item was linked to a suitable section in the Industry Code of Practice on Indoor Air Quality published by the Department of Occupational Safety and Health. CONCLUSION: Combined usage of an IAQ checklist with the information from the Industry Code of Practice on Indoor Air Quality would provide easily comprehensible information and practical support. Intervention and evaluation studies using this newly developed IAQ checklist will clarify the effectiveness of a new approach in evaluating the risk of indoor air pollutants in the workplace.