Impact of land cover change on urban surface temperature in Iskandar Malaysia

Iskandar Malaysia is one of the most ambitious and impressive development projects ever undertaken in Malaysia that has been experiencing rapid rate of land use change since 2006. Land use change is due to the urban expansion and reduction in natural green areas resulted from enhanced economic growth. The objectives of this study were to investigate the effect of land use and land cover changes (LULCC) on land surface temperature changes in Iskandar Malaysia and to predict the land surface temperature (LST) based on the LULCC by 2025. Remote sensing data such as Landsat (Landsat 5, 7 and 8) were used to calculate the LST and to determine the contribution of urban greenery as a possible remedy to Urban Heat Island (UHI). Weighted Average statistical technique was further used to calculate the effect of changes (increase and decrease) in each land use/cover (LULC) types on LST and predict the LST of entire Iskandar Malaysia by 2025. It was found that build up areas are the warmest land use during the days while forest and mangrove areas have the lowest day time LST. An increase in LST of 3.28 °C was found for urban surface from 1989 to 2013. A similar pattern was also seen in LST for other land cover classes and the increase was 1.96 °C for forest, 2.05 °C (Rubber), 2.47 °C (mangrove), 2.6 °C (oil palm) and 2.86 °C for water. Mean LST for entire Iskandar Malaysia rose from 21.88 °C to 24.78 °C (2.85 °C) by the year of 1989 and 2013. It is predicted that it will increase to 25.3 C by the year of 2025

Tree canopy cover and its potential to reduce CO2 in South of Peninsular Malaysia

Urban trees provide a wide range of ecosystem services that can address climate-change mitigation and adaptation. In this study, the tree cover and their potential to store carbon in two cities (Johor Bahru and Pasir Gudang) that are developing rapidly in the south of Peninsular Malaysia have been estimated. Tree coverage was mapped using Landsat 8 Thematic Mapper satellite data for year 2016. Various digital image processing techniques namely Maximum Likelihood and a sub-pixel classification were applied to obtain tree coverage of urban trees/forest, mangrove and oil palm. Results of the study show that natural tree coverage (forest and mangrove) in the cities range between 19 % and 47 % and generally Pasir Gudang has more tree coverage compared to Johor Bahru. Johor Bahru is the centre for various business and cultural activities, thus more built up areas are found in the city. On average, trees in the cities store approximately 796,136 t carbon or 2,919,164 t CO2-eq which is about 18 % of the total CO2-equivalent emissions projected for 2025 under the Business as Usual (BaU) scenario. The mapping of tree canopy cover and estimating their potential to store carbon is important for assessing climate change mitigation

Green corridors for liveable and walkable city: A case of Kuala Lumpur

The concept of sustainability embraces the conservation of the environment, cultural preservation, economic stability and overcoming of social problems. To ensure urban sustainability, one of the crucial factors is the environmental health, in which the environment should be kept in the best condition in developing countries thereby leading to reduction of environmental pollution. Green corridors in cities are one such way to ensure that the green areas are being used optimally. Such studies do exist in Malaysia but there is no established and published implementation. There is a need to analyse and study the current problems of the existing green corridors plans. This paper helps to visualise all the six suggested routes of the green corridors that had been made in Kuala Lumpur city. It discusses the opportunities and limitations of the plans as well as ways to improve for a successful implementation of green corridors in Kuala Lumpur

Identification of suitable trees for urban parks and roadsides in Iskandar Malaysia

Urban trees provide a number of benefits, mainly for environment, community, and economy, but can also be harmful to property and human lives. Urban trees planted at roadsides with low endurance rate and unhealthiness increase the risk of tree fracture and fall which is hazardous to motorists and pedestrians. Overhanging limbs, on the other hand, can obscure streetlights, signs and traffic signals and affect road users’ vision in vicinity. These situations contribute to the cumulative maintenance burden to the local authority. This makes the study of maintenance level and suitable location for urban tree planting important. An appropriate maintenance and location can be suggested for assuring a healthy, safe, resilient and long-term survival of urban trees. Urban tree field data from two local authorities in Iskandar Malaysia region (located in the southern part of Peninsular Malaysia), Johor Bahru City Council, and Pasir Gudang Municipal Council, were obtained to achieve the objective of this study. Survey based on questionnaire was conducted to gain detailed information about the maintenance level of existing trees and their suitable locations in the cities from the professionals including urban planners, landscape architects, and certified arborists. By applying a simple scoring method to the data obtained from professionals, suitable locations for existing trees in the two cities were determined. The scores range between 0 and 300 and the highest value means less maintenance is required by the trees. Results show that Mimosup elengi tree species (Sapotaceae family) has the highest score of 300 followed by Cinnamomum verum (297) and Hopea odorata (283). Khaya senegalensis, on the other hand, with 245 score value was found to require high levels of maintenance. The results also indicate that maintenance level and suitable location for planting vary and depending on the features of the tree species. Strongest trees or limbs tend to cause less problems thus require less maintenance. Trees found in the nature (forest) including Mimosup elengi and Cinnamomum verum are usually more resilient and can tolerate a wide range of conditions and locations. This study can help reducing the risk of tree fracture and fall, prolong the life of trees, and reduce the burden of maintenance for local authorities and decision makers by providing insights to the maintenance level and suitable locations for planting and to make better management plans for urban forestry in Malaysia in the future

Oil palm detection and delineation using local maxima, template matching and seeded region growing

Oil palm (Elaeis guineensis Jacq.) is recognized as a golden crop and it contributes significantly to the economic development of Malaysia. Oil palm detection and delineation are important stepping stones for the practice of precision agriculture in the oil palm industry and it could be done so with remote sensing applications. This research aims to develop a semi-automatic, streamlined approach of oil palm detection and delineation using a combination of template matching, local maxima and seeded region growing with Worldview-2 data. The performance of the proposed methods was assessed in various aspects while taking into consideration the different planting conditions, age, and height. The proposed methods of oil palm detection managed to achieve high accuracy with overall precision and recall rate of 83% and 90% respectively and planimetric accuracy of 0.84 m root mean square error. The overall accuracy index is recorded at 71.2%. It was found that different planting conditions affect the detection accuracy to a certain degree where oil palms in optimal planting conditions are the most accurately detected with an accuracy index of 89.5%. Meanwhile, the parameters of age and height were found to have no significant effect on the planimetric accuracy or its positional accuracy. Oil palm delineation scored a high segmentation accuracy with only a 25% error rate. The proposed methods are feasible for oil palm detection with their simple, streamlined and user-friendly features and the application of this approach can be extended to other regions of oil palms with similar conditions

Remote sensing to study mangrove fragmentation and its impacts on leaf area index and gross primary productivity in the south of Peninsular Malaysia

Mangrove is classified as an important ecosystem along the shorelines of tropical and subtropical landmasses, which are being degraded at an alarming rate despite numerous international treaties having been agreed. Iskandar Malaysia (IM) is a fast-growing economic region in southern Peninsular Malaysia, where three Ramsar Sites are located. Since the beginning of the 21st century (2000–2019), a total loss of 2907.29 ha of mangrove area has been estimated based on medium-high resolution remote sensing data. This corresponds to an annual loss rate of 1.12%, which is higher than the world mangrove depletion rate. The causes of mangrove loss were identified as land conversion to urban, plantations, and aquaculture activities, where large mangrove areas were shat-tered into many smaller patches. Fragmentation analysis over the mangrove area shows a reduction in the mean patch size (from 105 ha to 27 ha) and an increase in the number of mangrove patches (130 to 402), edge, and shape complexity, where smaller and isolated mangrove patches were found to be related to the rapid development of IM region. The Moderate Resolution Imaging Spectro-radiometer (MODIS) Leaf Area Index (LAI) and Gross Primary Productivity (GPP) products were used to inspect the impact of fragmentation on the mangrove ecosystem process. The mean LAI and GPP of mangrove areas that had not undergone any land cover changes over the years showed an increase from 3.03 to 3.55 (LAI) and 5.81 g C m-2 to 6.73 g C m-2 (GPP), highlighting the ability of the mangrove forest to assimilate CO2 when it is not disturbed. Similarly, GPP also increased over the gained areas (from 1.88 g C m-2 to 2.78 g C m-2). Meanwhile, areas that lost mangroves, but replaced them with oil palm, had decreased mean LAI from 2.99 to 2.62. In fragmented mangrove patches an increase in GPP was recorded, and this could be due to the smaller patches (< 9 ha) and their edge effects where abundance of solar radiation along the edges of the patches may increase productivity. The impact on GPP due to fragmentation is found to rely on the type of land transfor-mation and patch characteristics (size, edge, and shape complexity). The preservation of mangrove forests in a rapidly developing region such as IM is vital to ensure ecosystem, ecology, environment, and biodiversity conservation, in addition to providing economical revenue and supporting human activities

Spectral variability analysis of in-situ hyperspectral remote sensing at leaf and branch scales for tree species at tropical urban forest

Spectral variability analysis has been carried out on in-situ hyperspectral remote sensing data for 20 tree species available in tropical forest in Malaysia. Five different spectral ranges have been tested to evaluate the influence of intra-species spectral variability at specific spectral range given by different spatial scales (i.e. leaf to branch scales). The degree of intra-species spectral variability was not constant among different spectral ranges where the influence of spatial scale towards intra-species spectral variability at these spectral ranges was found increasing from leaf to branch scale. The ratio of leaves to non-photosynthetic tissues has made branch scale significantly influent the intra-species spectral variability. Results have shown that a specific spectral range was species sensitive on the intra-species and inter-species spectral variability in this study. This study also suggested the use of species sensitive wavelengths extracted from specific spectral range in hyperspectral remote sensing data in order to achieve good accuracy in tree species classification

Textural measures for estimating oil palm age

In oil palm management, age is one of the yield determinant factors. The conventional field investigations are often exhaustive and costly methods when implemented on a large scale. Despite much attention to classify individual oil palm ages by using various remote sensing images, none of the studies depicted satisfying overall accuracies. The overall aim of this study was to optimize window size and number of texture measurements for oil palm ages classification. The study was conducted in a commercial oil palm plantation comprised of palms of multiple ages planted from 1991 to 2008. Three Satellite Pour l’Observation de la Terre (SPOT)-5 multispectral images, acquired on 12 April 2012, 4 April 2013, and 14 April 2014, were evaluated. The individual ages were successfully classified with accuracy ranging from 59% to 97%, with an average overall accuracy of 84%. The results illustrated that the largest window size related to the smallest oil palm planting block in the study area, 390 m × 390 m on-the-ground window size, and seven combination of texture measurements, resulted in the highest classification overall accuracy. The utilization of texture measurements produced synergistic effects able to discriminate the oil palm age, with mean, entropy, homogeneity, and angular second moment as among the significant textures

Multi-sensor satellite data for carbon storage mapping of green space in a fast growing development corridor in Malaysia

Disturbances such as deforestation and land use change on natural vegetation have caused carbon dioxide (CO2) emission to the atmosphere which contributes to global warming and climate change. Malaysia has started to take necessary steps to mitigate the potential impact of increased CO2 and integrating the green infrastructure into urban planning is a way to go. Green space in urban environment provides a variety of benefits to the community by sequestrating carbon, absorbing urban emissions and producing oxygen. In this study we quantified the carbon storage capacity of various green spaces, namely forests, mangroves and urban parks by biometric measurements and remote sensing techniques at a rapidly developing economy region in Iskandar Malaysia (IM) in the southern Peninsular Malaysia that covers an area of 2,217 km2. Satellite imageries such as RapidEye and Advanced Land Observing Satellite phased Array type L-band Synthetic Aperture Radar) were used for mapping the carbon content of different vegetation in IM. The spatial distribution of carbon storage shows that mangroves contribute the largest amount of carbon storage in IM with 0.437M t C and this is mainly due to their vast area (8382 ha). This is followed by tropical forest (0.185M t C). However, tropical forest has the highest carbon density with 161.7 tC ha-1 compared to mangroves (52.1 tC ha-1). In general, trees in urban parks have lower carbon storage (ranging between 32.63 tC ha-1 to 48.81 tC ha-1) compared to forests and mangrove. In total, these vegetation types in IM remove ~2.29 M tCO2eq. Green space in IM was found to remove about 3% of carbon emitted to the air in IM. These results suggest that the government must make firm policies to increase more green cover in the urban areas and to preserve the existing green space for improving environmental quality for people and supporting biodiversity conservation

Interaction between aerosols, clouds and solar radiation: impact on savanna productivity

Savannas have relatively high levels of net primary productivity compared with the actual biomass (dry mass of organic matter)