Water Quality Studies in Malacca River Basin Using Geographic Information Systems (GIS)

Malacca Straits is recognized worldwide due to its importance as the busiest waterway in the world. The environmental profile of the Malacca Straits points to certain stresses in the ecosystem due to pollution load. Pollutants from land-based sources are probably the most pressing environmental problem. Wastes from agricultural, industrial, and domestic source discharge directly into the Straits or indirectly via rivers, which flow into the Straits. As far as land based pollution is concerned, study on river basin is important. The Malacca River basin is of the rivers flowing into the Straits of Malacca. Geographic information System (GIS) was used to map the water pollution scenario due to its ability to analyze spatial and temporal data. Beside that, correlation coefficient method was used to determine the relationship and variation between rainfall and water quality parameters. Six parameters selected for this study. namely. Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Ammoniacal Nitrogen. pH, Suspended Solid (SS), and Dissolved Oxygen (DO). The output of this study consists of water quality database. maps, and graphs on the water quality variations. The important of this study is the application of GIS technology in monitoring water quality. Technology plays an important role for effective environmental management

Sap flow study on two different diameter sizes of Tectona grandis

Sap flow pattern of Tectona grandis planted at lowland forest assessed. This study aimed to determine the sap movement of two different diameter sizes T. grandis. Two sizes selected were 16 and 38 cm in diameter at breast height (dbh). Sap flow meter (SFM) used to assess the sap velocity rates at the interval of 30 min within 24 h for 15 days. Diurnal sap flow of T. grandis shows that mean velocity is high during day time compared night time. Small diameter has high sap flow compared to that of bigger diameter. A flow rates was high at the inner layer and less at outer layer for smaller tree. The variation was vice versa when the tree was getting bigger. Variations in sap flow of T. grandis characterized by several environmental factors. It was found that size contribute in the differed sap flow of T. grandis

Combining Moderate and High Resolution of Satellite Images for Characterizing Suitable Habitat for Vegetation and Wildlife

Combining different resolution of remote sensing satellites becomes a unique approach for vegetation and wildlife habitat assessment study. Remote sensing technology can reach land and water on the Earth's surface, and it can interpret signals from spectral responses. When these techniques are combined with Geographical Information Systems (GIS), land can be monitored in a variety of ways. Meanwhile, changes in land use led to changes in vegetation on the ground, with natural vegetation being removed from natural forests, leaving a degraded forest. This issue was not investigated for assessing habitat suitability for important plantations such as Eucalyptus plantation. Therefore, the study employed remote sensing and Geographical Information System (GIS) to model suitability of habitat to live and to survive in the Eucalyptus plantation. Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) derived from a mathematical equation can demonstrate intensity of greenness of green vegetation in particular area and time, and availability of soil moisture, respectively, is very suitable to model the greenness of the area. WorldView-2 satellite image was pre-proceed, proceed, and classified to produce land use indicator in Sabah Softwoods Berhad plantation majoring Eucalyptus spp. tree planted in Tawau, Sabah. Sentinel and Landsat 8 image were used for vegetation and water stress indicator were downloaded from Land Viewer application. Net Primary Productivity (NPP) at monthly scale was also calculated and ranked the productivity for the suitability mapping. Climatic condition based on monthly precipitation and seasonality derived from ASEAN Specialized Meteorological Centre (ASMC) was employed for ranking its suitability value. In this study, natural forest and oil palm plantation is tested to developed suitability map for vegetation and wildlife habitat to live with. All indicators were ranked 10 to 40 presenting benefit and usefulness of the indicator to vegetation and wildlife in the study area. Then, final classification was made from accumulation of those indicators into 0 to 200 (Not suitable to Highly suitable). The results showed 59.9% of the area classified as moderately suitable, 36.9% highly suitable, 3.2% least suitable and no area was classified as not suitable. This type of study assisted forest managers and policymakers for better managing of their forests for better life of trees and wildlife under their management. The methodology adapted in the study is ecologically sounded and economically viable to be modified and complied in Sustainable Forest Management (SFM) in Malaysia and other tropical forest regions

The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.Peer reviewe

土壌水分の時空間分布が半島マレーシアパソ森林保護区低地フタバガキ林に与える諸影響

京都大学0048新制・論文博士博士(農学)乙第13181号論農博第2860号新制||農||1061(附属図書館)学位論文||H30||N5103(農学部図書室)(主査)教授 小杉 緑子, 教授 北山 兼弘, 教授 舟川 晋也学位規則第4条第2項該当Doctor of Agricultural ScienceKyoto UniversityDFA

Mangrove Vegetation Health Assessment Based on Remote Sensing Indices for Tanjung Piai, Malay Peninsular

Mangroves critically require conservation activity due to human encroachment and environmental unsustainability. The forests must be conserving through monitoring activities with an application of remote sensing satellites. Recent high-resolution multispectral satellite was used to produce Normalized Difference Vegetation Index (NDVI) and Tasselled Cap transformation (TC) indices mapping for the area. Satellite Pour l’Observation de la Terre (SPOT) SPOT-6 was employed for ground truthing. The area was only a part of mangrove forest area of Tanjung Piai which estimated about 106 ha. Although, the relationship between the spectral indices and dendrometry parameters was weak, we found a very significant between NDVI (mean) and stem density (y=10.529x + 12.773) with R2=0.1579. The sites with NDVI calculated varied from 0.10 to 0.26 (P1 and P2), under the environmental stress due to sand deposition found was regard as unhealthy vegetation areas. Whereas, site P5 with NDVI (mean) 0.67 is due to far distance from risk wave’s zone, therefore having young/growing trees with large lush green cover was regard as healthy vegetation area. High greenness indicated in TC means, the bands respond to a combination of high absorption of chlorophyll in the visible bands and the high reflectance of leaf structures in the near-infrared band, which is characteristic of healthy green vegetation. Overall, our study showed our tested WV-2 image combined with ground data provided valuable information of mangrove health assessment for Tanjung Piai, Johor, Malay Peninsula

Monitoring green biomass utilizing remote sensing techniques for agriculture and forest areas in East Malaysia

Forests and agricultural plantations are vegetated areas that play an important role in catering to human needs. The characteristics of the two lands can be recognized by utilizing remote sensing techniques of WorldView-2 satellite images as green biomass monitoring tools. The research was undertaken in Sabah Softwoods Berhad (SSB) forest plantation, East Malaysia, to analyze land use in the high functionality areas. Aside from the social benefits, there has been a lack of research conducted to estimate the areas' Net Primary Productivity (NPP) using remote sensing of biophysical characteristics. The NPP quantifies the biomass generated by the green plants, which provides the chemical energy that drives most of the biotic processes on the Earth. The NPP calculated from the study for all land features in the study areas ranged from 39.33 gCm−2 month−1 to 1498.00 gCm-2 month-1. This work has established a new NPP assessment for all land features in the Brumas forest plantation in East Malaysia. Because NPP is an estimate of the earth's living biomass, this type of approach should be used to develop biomass maps to meet human requirements on the planet

Seasonal variations of soil moisture regime at dry region of lowland dipterocarp forest in Pasoh forest reserve, Peninsular Malaysia

Soil moisture is an essential component in the terrestrial hydrological process and greatly influences nutrient cycle and energy flow. Tropical rainforest sometimes experiences a severe dry period for several months. Soil moisture is responsible for regulating transpiration during this time. This study focuses on the soil moisture in the tropical rainforest by determining soil water content at 6 ha Pasoh Reserve Forest, Negeri Sembilan, Peninsular Malaysia. The study area is located within the drier area in Peninsular Malaysia and therefore is suitable for assessing soil moisture fluctuation during the dry and wet seasons. We measure soil moisture from 39 grid points using Amplitude Domain Reflectometry (ADR-type) soil moisture profile probe from a different soil depth at 0.1, 0.2, 0.3, 0.4, 0.6 and 1.0 meter monthly. This study aims to determine the seasonal soil moisture fluctuation in the Pasoh Forest Reserve as the effect of monsoon season. During the Northeast monsoon season between October 2019 to March 2020, soil water content was higher than the other months of the year. October shows the most rainfall, amounting 364.77 mm month-1. Expectedly, at all soil depth, the moisture revealed the higher as the rain is at most. The soil moisture also increased significantly with a deeper soil depth at 1m, compared to shallower soil depth. This study could be used as a model for developing forests associated with soil moisture and the ecological character of the tropical forest

Assessment of values and trends in coarse spatial resolution NDVI datasets in Southeast Asia landscapes

Normalized difference vegetation index (NDVI) has been widely applied for monitoring vegetation dynamics. However, NDVI values are known to be profoundly affected by various external factors. In this study, the variation of NDVI values and trends among the several long-term NDVI datasets with resolution of 1, 4 and 8 km were assessed to understand the differences between the available datasets. The assessment items were 1) Pearson’s correlation coefficient, 2) trend map and breakpoint spatial similarities and 3) comparison of NDVI from Landsat and Flux tower in 2007–2015. The comparison revealed a maximum correlation coefficient of 0.67 among NDVI datasets and average spatial similarity of 37.2% among the trend maps estimated from NDVI datasets. Furthermore, there was a possibility of having significantly opposite trends between two trend maps from different NDVI products. Comparisons with NDVI from vegetation pixel in Landsat 5 TM and 8 OLI resulted in the R2 between 0.06 and 0.68 and RMSE of 0.07–0.2, while comparison with NDVI from flux tower data yielded the RMSE of 0.04–0.41, although the R2 was relatively weak at 0–0.18. Our study highlights the possibility of differences among NDVI datasets, and suggests that these differences should be reconciled especially in time-series analysis