Ahmad Ainuddin Nuruddin

GIS‐grid‐based and multi‐criteria analysis for identifying and mapping peat swamp forest fire hazard in Pahang, Malaysia

Peat swamp forest fire hazard areas were identified and mapped by integrating GIS‐grid‐based and multi‐criteria analysis to provide valuable information about the areas most likely to be affected by fire in the Pekan District, south of Pahang, Malaysia. A spatially weighted index model was implemented to develop the fire hazard assessment model used in this study. Fire‐causing factors such as land use, road network, slope, aspect and elevation data were used in this application. A two‐mosaic Landsat TM scene was used to extract land use parameters of the study area. A triangle irregular network was generated from the digitized topographic map to produce a slope risk map, an aspect risk map and an elevation risk map. Spatial analysis was applied to reclassify and overlay all grid hazard maps to produce a final peat swamp forest fire hazard map. To validate the model, the actual fire occurrence map was compared with the fire hazard zone area derived from the model. The model can be used only for specific areas, and other criteria should be considered if the model is used for other areas. The results show that most of the actual fire spots are located in very high and high fire risk zones identified by the model.

An extended ID3 decision tree algorithm for spatial data

Utilizing data mining tasks such as classification on spatial data is more complex than those on non-spatial data. It is because spatial data mining algorithms have to consider not only objects of interest itself but also neighbours of the objects in order to extract useful and interesting patterns. One of classification algorithms namely the ID3 algorithm which originally designed for a non-spatial dataset has been improved by other researchers in the previous work to construct a spatial decision tree from a spatial dataset containing polygon features only. The objective of this paper is to propose a new spatial decision tree algorithm based on the ID3 algorithm for discrete features represented in points, lines and polygons. As in the ID3 algorithm that use information gain in the attribute selection, the proposed algorithm uses the spatial information gain to choose the best splitting layer from a set of explanatory layers. The new formula for spatial information gain is proposed using spatial measures for point, line and polygon features. Empirical result demonstrates that the proposed algorithm can be used to join two spatial objects in constructing spatial decision trees on small spatial dataset. The proposed algorithm has been applied to the real spatial dataset consisting of point and polygon features. The result is a spatial decision tree with 138 leaves and the accuracy is 74.72%.

Responses of herbaceous mimosa (Mimosa strigillosa), a new reclamation species, to cyclic moisture stress

A greenhouse study was conducted to evaluate the response of herbaceous mimosa (Mimosa strigillosa) to six levels of cyclic soil moisture stresses in a 17-week period. The results showed that the cultivar continued to grow and the biomass continued to increase even when the soil moisture stress was as high as at the wilting point (1500 Kpa). Also, transpiration recovery rate was quick and values of root/shoot ratio were high when the plant was subject to the cyclic moisture stress condition. All these characteristics, along with strong rooting and spreading ability, suggest this legume as a promising drought hardiness species for reclamation purposes.

Capability of integrated MODIS imagery and ALOS for oil palm, rubber and forest areas mapping in tropical forest regions.

Various classification methods have been applied for low resolution of the entire Earths surface from recorded satellite images, but insufficient study has determined which method, for which satellite data, is economically viable for tropical forest land use mapping. This study employed Iterative Self Organizing Data Analysis Techniques (ISODATA) and K-Means classification techniques to classified Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance satellite image into forests, oil palm groves, rubber plantations, mixed horticulture, mixed oil palm and rubber and mixed forest and rubber. Even though frequent cloud cover has been a challenge for mapping tropical forests, our MODIS land use classification map found that 2008 ISODATA-1 performed well with overall accuracy of 94%, with the highest Producers Accuracy of Forest with 86%, and were consistent with MODIS Land Cover 2008 (MOD12Q1), respectively. The MODIS land use classification was able to distinguish young oil palm groves from open areas, rubber and mature oil palm plantations, on the Advanced Land Observing Satellite (ALOS) map, whereas rubber was more easily distinguished from an open area than from mixed rubber and forest. This study provides insight on the potential for integrating regional databases and temporal MODIS data, in order to map land use in tropical forest regions.

Responses of herbaceous mimosa (Mimosa strigillosa), a new reclamation species, to soil pH

Abstract Growth responses of herbaceous mimosa ( Mimosa strigillosa Torr. and Gray), a potential new reclamation species in the SE USA and Mexico, to nine soil pH scales were studied under a controlled environment at Nacogdoches, TX, USA. Twenty seeds were planted in each of 40 (nine scales plus one control in four replicates) 20.3-cm pots filled with Tonkawa sandy soil. These pots were treated with H 2 SO 4 or Ca(OH) 2 to adjust each pot to its designated pH level. After 15 days of seeding, the emergence rate was at best about 50–70% for pH 4.7–6.6. The plant can survive and grow at soil pH as low as 4.7, but the optimum growth seems to be on soils with pH ranging from 6.2 to 7.1. At this pH range, the plant exhibits higher values of green and dry biomass, longer shoot growth and lower root/shoot weight and length ratios. The survival rate was greater than 90% for all treatments, except for pH 4.1. There were no nutrient deficiencies in plant tissues on soil pH 4.7 or higher. The plant allocated more growth to the shoot under optimum conditions, but more growth to the roots under environmental stress. It is not suitable for herbaceous mimosa to grow on soils with pH 4.1 or less.

Soil carbon dioxide efflux and atmospheric impact in a 10-year-old Dipterocarpus recovering lowland tropical forest, Peninsular Malaysia

Recovering logged-over forest ecosystem increases CO2 efflux into the atmospheric carbon pool in response of environmental factors to change in soil temperature and moisture. These CO2 outbursts can have a marked influence on the ecosystem carbon balance and thereby affect the atmospheric carbon pool. The study was conducted in a 10 years logged-over forest of Sungai Menyala forest, Port Dickson, Negeri Sembilan, Malaysia. The measurements of soil CO2 effluxes were conducted using a continuous open flow chambers technique connected to a multi gas-handling unit and infrared CO2/H2O gas analyser. The aim of this study is to determine the percentage of CO2 contributed into the atmosphere from a recovering 10 year logged-over lowland forest. One-way analysis of variance (ANOVA) was used to test the significance correlation between soil CO2 efflux and environmental variables. Post-hoc comparisons were made using Tukey test (p < 0.05), and multiple linear regressions were used to determine the impact of environmental factors on soil CO2 efflux. Soil CO2 efflux range from 345.6 to 600.4 mg/m−2/h−1 with the highest efflux in the afternoon attributed to increase in soil temperature and moisture. Higher soil temperature and moisture recorded signify the influential factor. Furthermore, the predictor environmental variables; Soil Organic Carbon (SOC), Total Organic Carbon (TOC), Soil Moisture Content (SMC), Bulk Density, Below Ground Carbon Stock, Total Aboveground Carbon Biomass (TAGB), soil pH, Nitrogen to Carbon ratio account for the spatial and temporal variation in soil CO2 efflux. These factors attributed to increase in CO2 efflux into the atmosphere.

Monitoring vegetation drought using MODIS remote sensing indices for natural forest and plantation areas

Natural forest, oil palm and rubber plantations are economically and environmentally important for Peninsular Malaysia. The present study analysed four years of moderate-resolution imaging spectroradiometer (MODIS) surface reflectance data to develop spectral indices of vegetation, water availability and moisture stress for the study area. The indices – the Normalised Difference Vegetation Index, the Normalised Difference Water Index and the Moisture Stress Index – were applied to the three different habitats to monitor drought and develop a Malaysia Southwest Monsoon (M-SWM) classification. By integrating indicators of the Southwest Monsoon, the Standard Precipitation Index, mean precipitation and temperature and spectral indices correlation analysis, M-SWM classification showed greater sensitivity to drought conditions than any of the individual indicators alone. The results also found that July is the driest month; it was the only period classified as ‘Very Dry’ based on the M-SWM.

Forest Logging and It Impact on Soil Carbon Dioxide Effluxin the tropical Forest, Peninsular Malaysia

Forest harvesting is expected to have an impact on soil CO2 efflux as it influence soil properties and changes in microclimatic conditions which can have implications on the regional carbon balance. Soil CO2 efflux was measured using a continuous open flow chambers technique connected to a multi-gas-handling unit and infrared CO2/H2O gas analyser. Soil temperature, soil moisture, water potential, Total Organic Carbon (TOC), Soil Organic Carbon (SOC), Soil Organic Carbon stock (SOCstock), Bulk density and pH were examinedto ascertain their contribution onsoil CO2 efflux and effect ofenvironmental factors in a canopy gap created through the logging of groups of trees in the Sungai Menyala forest, Peninsular Malaysia.

A method of mapping forest fuel types in peat swamp forest

In response to growing concerns over the burning of peat swamp forests, researchers have begun developing methods of mapping forest fire. Forest fire is one of the major causes of deforestation of tropical peat swamps in Malaysia. A way of identifying which peat swamp forest is vulnerable to forest fire is to develop a fuel type map to classify forest fire into different risk levels. In this study, remote sensing and geographical information system (GIS) techniques were integrated. Landsat Thematic Mapper (TM) image dated April 3rd, 1999, which corresponded to fire incident in this study area was used. The objective of this paper is to map fuel types in peat swamp forests. Results show that greenness and wetness components of Tasselled cap, used in classification, accurately captured greener and wetter area by combining supervised image and Tasselled cap image. The overall kappa statistics was 0.94 for combined supervised and Tasselled Cap classification. High values of kappa statistics for certain vegetation classes were due to the availability of representative pixels in the classes.

Modeling forest fires risk using spatial decision tree

Forest fires have long been annual events in many parts of Sumatra Indonesia during the dry season. Riau Province is one of the regions in Sumatra where forest fires seriously occur every year mostly because of human factors both on purposes and accidently. Forest fire models have been developed for certain area using the weightage and criterion of variables that involve the subjective and qualitative judging for variables. Determining the weights for each criterion is based on expert knowledge or the previous experienced of the developers that may result too subjective models. In addition, criteria evaluation and weighting method are most applied to evaluate the small problem containing few criteria. This paper presents our initial work in developing a spatial decision tree using the spatial ID3 algorithm and Spatial Join Index applied in the SCART (Spatial Classification and Regression Trees) algorithm. The algorithm is applied on historic forest fires data for a district in Riau namely Rokan Hilir to develop a model for forest fires risk. The modeling forest fire risk includes variables related to physical as well as social and economic. The result is a spatial decision tree containing 138 leaves with distance to nearest river as the first test attribute.