The management of suspended solids and associated contaminants in rivers requires knowledge of sediment sources. In-situ sampling can only describe the integrated impact of the upstream sources. Empirical models that use surface reflectance from satellite images to estimate total suspended solid (TSS) concentrations can be used to supplement measurements and provide spatially continuous maps. However, there are few examples, especially in narrow, shallow and hydrologically dynamic rivers found in mountainous areas. A case study of the Didipio catchment in Philippines was used to address these issues. Four 5-m resolution RapidEye images, from between the years 2014 and 2016, and near-simultaneous ground measurements of TSS concentrations were used to develop a power law model that approximates the relationship between TSS and reflectance for each of four spectral bands. A second dataset using two 2-m resolution Pleiades-1A and a third using a 6-m resolution SPOT-6 image along with ground-based measurements, were consistent with the model when using the red band data. Using that model, encompassing data from all three datasets, gave an R2 value of 65% and a root mean square error of 519mgL-1. A linear relationship between reflectance and TSS exists from 1mgL-1 to approximately 500mgL-1. In contrast, for TSS measurements between 500mgL-1 and 3580mgL-1 reflectance increases at a generally lower and more variable rate. The results were not sensitive to changing the pixel location within the vicinity of the ground sampling location. The model was used to generate a continuous map of TSS concentration within the catchment. Further ground-based measurements including TSS concentrations that are higher than 3580mgL-1 would allow the model to be developed and applied more confidently over the full relevant range of TSS.
The main purpose of this study is to determine shoreline change in Bengkalis Cape, Riau Province, Indonesia using sediment samples analysis, satellite images, and oceanographic parameters. The samples were collected at five stations by using sediment grab and oceanographic observation was also carried out at each station in November 2015. The southern part of Bengkalis Cape is characterised by fine-grain sediments (mud) and high rate of accretion that reaches 29.77 metre/year, and is influenced by weak tidal currents with a velocity of less than 0.06 m/s and low wave energy. In contrast, the northern part is occupied by coarse-grain sediments (sand) which is characterised by high rate of abrasion as shown in the image data for 20 years; 1995-2015 reaches 38.02 metre/year, and is under the influence of strong tidal current (0.16 m/s) and high wave energy. The major contributing factor for the shoreline change is the current system which flowing from Malacca strait to the shore area and sediments deposition in the area.
Coastal ecosystems are facing heightened risks due to human-induced climate change, including rising water levels and intensified storm events. Accurate bathymetry data is crucial for assessing the impacts of these threats. Traditional data collection methods can be cost-prohibitive. This study investigates the feasibility of using freely accessible Landsat and Sentinel satellite imagery to estimate bathymetry and its correlation with hydrographic chart soundings in Port Klang, Malaysia. Through analysis of the blue and green spectral bands from the Landsat 8 and Sentinel 2 datasets, a bathymetry map of Port Klang's seabed is generated. The precision of this derived bathymetry is evaluated using statistical metrics like Root Mean Square Error (RMSE) and the coefficient of determination. The results reveal a strong statistical connection (R2 = 0.9411) and correlation (R2 = 0.7958) between bathymetry data derived from hydrographic chart soundings and satellite imagery. This research not only advances our understanding of employing Landsat imagery for bathymetry assessment but also underscores the significance of such assessments in the context of climate change's impact on coastal ecosystems. The primary goal of this research is to contribute to the comprehension of Landsat imagery's utility in bathymetry evaluation, with the potential to enhance safety protocols in seaport terminals and provide valuable insights for decision-making concerning the management of coastal ecosystems amidst climate-related challenges. The findings of this research have practical implications for a wide range of stakeholders involved in coastal management, environmental protection, climate adaptation and disaster preparedness.
Studies relating to trends of vegetation, snowfall and temperature in the north-western Himalayan region of India are generally focused on specific areas. Therefore, a proper understanding of regional changes in climate parameters over large time periods is generally absent, which increases the complexity of making appropriate conclusions related to climate change-induced effects in the Himalayan region. This study provides a broad overview of changes in patterns of vegetation, snow covers and temperature in Uttarakhand state of India through bulk processing of remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) data, meteorological records and simulated global climate data. Additionally, regression using machine learning algorithms such as Support Vectors and Long Short-term Memory (LSTM) network is carried out to check the possibility of predicting these environmental variables. Results from 17 years of data show an increasing trend of snow-covered areas during pre-monsoon and decreasing vegetation covers during monsoon since 2001. Solar radiation and cloud cover largely control the lapse rate variations. Mean MODIS-derived land surface temperature (LST) observations are in close agreement with global climate data. Future studies focused on climate trends and environmental parameters in Uttarakhand could fairly rely upon the remotely sensed measurements and simulated climate data for the region.
Dartford, a town in England, heavily relied on industrial production, particularly mining, which caused significant environmental pollution and geological damage. However, in recent years, several companies have collaborated under the guidance of the local authorities to reclaim the abandoned mine land in Dartford and develop it into homes, known as the Ebbsfleet Garden City project. This project is highly innovative as it not only focuses on environmental management but also provides potential economic benefits, employment opportunities, builds a sustainable and interconnected community, fosters urban development and brings people closer together. This paper presents a fascinating case that employs satellite imagery, statistical data, and Fractional Vegetation Cover (FVC) calculations to analyse the re-vegetation progress of Dartford and the development of the Ebbsfleet Garden City project. The findings indicate that Dartford has successfully reclaimed and re-vegetated the mine land, maintaining a high vegetation cover level while the Ebbsfleet Garden City project has advanced. This suggests that Dartford is committed to environmental management and sustainable development while pursuing construction projects.
Satellite data and aerial photos have proved to be useful in efficient conservation and management of mangrove ecosystems. However, there have been only very few attempts to demonstrate the ability of drone images, and none so far to observe vegetation (species-level) mapping. The present study compares the utility of drone images (DJI-Phantom-2 with SJ4000 RGB and IR cameras, spatial resolution: 5cm) and satellite images (Pleiades-1B, spatial resolution: 50cm) for mangrove mapping-specifically in terms of image quality, efficiency and classification accuracy, at the Setiu Wetland in Malaysia. Both object- and pixel-based classification approaches were tested (QGIS v.2.12.3 with Orfeo Toolbox). The object-based classification (using a manual rule-set algorithm) of drone imagery with dominant land-cover features (i.e. water, land, Avicennia alba, Nypa fruticans, Rhizophora apiculata and Casuarina equisetifolia) provided the highest accuracy (overall accuracy (OA): 94.0±0.5% and specific producer accuracy (SPA): 97.0±9.3%) as compared to the Pleiades imagery (OA: 72.2±2.7% and SPA: 51.9±22.7%). In addition, the pixel-based classification (using a maximum likelihood algorithm) of drone imagery provided better accuracy (OA: 90.0±1.9% and SPA: 87.2±5.1%) compared to the Pleiades (OA: 82.8±3.5% and SPA: 80.4±14.3%). Nevertheless, the drone provided higher temporal resolution images, even on cloudy days, an exceptional benefit when working in a humid tropical climate. In terms of the user-costs, drone costs are much higher, but this becomes advantageous over satellite data for long-term monitoring of a small area. Due to the large data size of the drone imagery, its processing time was about ten times greater than that of the satellite image, and varied according to the various image processing techniques employed (in pixel-based classification, drone >50 hours, Pleiades <5 hours), constituting the main disadvantage of UAV remote sensing. However, the mangrove mapping based on the drone aerial photos provided unprecedented results for Setiu, and was proven to be a viable alternative to satellite-based monitoring/management of these ecosystems. The improvements of drone technology will help to make drone use even more competitive in the future.
The growing effects of climate change on Malaysia's coastal ecology heighten worries about air pollution, specifically caused by urbanization and industrial activity in the maritime sector. Trucks and vessels are particularly noteworthy for their substantial contribution to gas emissions, including nitrogen dioxide (NO2), which is the primary gas released in port areas. The application of advanced analysis techniques was spurred by the air pollution resulting from the combustion of fossil fuels such as fuel oil, natural gas and gasoline in vessels. The study utilized satellite photos captured by the Tropospheric Monitoring Instrument (TROPOMI) on the Sentinel-5P satellite to evaluate the levels of NO2 gas pollution in Malaysia's port areas and exclusive economic zone. Before the COVID-19 pandemic, unrestricted gas emissions led to persistently high levels of NO2 in the analyzed areas. The temporary cessation of marine industry operations caused by the pandemic, along with the halting of vessels to prevent the spread of COVID-19, resulted in a noticeable decrease in NO2 gas pollution. In light of these favourable advancements, it is imperative to emphasize the need for continuous investigation and collaborative endeavours to further alleviate air contamination in Malaysian port regions, while simultaneously acknowledging the wider consequences of climate change on the coastal ecology. The study underscores the interdependence of air pollution, maritime activities and climate change. It emphasizes the need for comprehensive strategies that tackle both immediate environmental issues and the long-term sustainability and resilience of coastal ecosystems in the context of global climate challenges.
Remote Sensing (RS) and Geographical Information Systems (GIS) are widely used for change detection in rivers caused
by erosion and accretion. Digital image processing techniques and GIS analysis capabilities are used for detecting
temporal variations of erosion and accretion characteristics between the years 1999 and 2011 in a 40 km long Marala
Alexandria reach of River Chenab. Landsat satellite images for the years 1999, 2007 and 2011 were processed to analyze
the river channel migration, changes in the river width and the rate of erosion and accretion. Analyses showed that the
right bank was under erosion in both time spans, however high rate of deposition is exhibited in middle reaches. The
maximum erosion was 1569843 m2
and 1486160 m2
along the right bank at a distance of 24-28 km downstream of the
Marala barrage in the time span of 1999-2007 and 2007-2011, respectively. Along right bank mainly there is trend of
accretion but erosion is much greater between 20 and 28 km reach. Maximum accretion was 5144584 m2
from 1999-2007
and 2950110 m2
from 2007-2011 on the right bank downstream of the Marala Barrage. The derived results of channel
migration were validated by comparing with SRTM data to assess the accuracy of image classification. Integration of remote
sensing data with GIS is efficient and economical technique to assess land losses and channel changes in large rivers.
Geographical Information Systems (GIS) and three dimensional (3D) World Wide Web (WWW) applications usage are on the rise. The demand for online 3D terrain visualization for GIS data has increased. Current users demand for more complex data which have higher accuracy and realism. This is aided by the emergence of geo-browsers in the market which provide free service and also cater for the commercialized market. Other new technology driving the market is the use of software such as CityGML, Virtual Reality Markup Language (VRML)/ Entensive 3D (X3D), geoVRML, and Keyhole Markup Language (KML). These technologies also play an important role for this new era of online 3D terrain visualization. The aim of this paper is to implement the online 3D terrain visualization for GIS data by using VRML technology and launching the system into three different web servers. The data used for this system are contour data and high resolution satellite image (QUICKBIRD) for Universiti Putra Malaysia (UPM) area. Testing was done only for satellite image overlaid to 3D terrain data. The web servers used in this experiment were the Spatial Research Group Server in UPM, Universiti Utara Malaysia (UUM) web server, and ruzinoor.my web server. The comparison was based on the performance of web servers in terms of accessibility, uploading time, CPU usage, frame rate per second (fps), and number of users. The results from this experiment will be of help and guidance to the developers in finding the right web servers for the best performance on implementing online 3D terrain visualization for GIS data.
In this research wok, three different techniques of change detection were used to detect changes in forest areas. One of the techniques used a local similarity measure approach to detect changes. This new approach of change detection technique, which used mutual information to measure the similarity between two multi-temporal images, was developed based on correspondence of the pixel values, rather than the difference in their intensity. Pixels suffering any changes will be maximally dissimilar. The study was conducted using multi-temporal SPOT 5 satellite images, with the resolution of 10 m x10 m on 5th August 2005 and 13th June 2007. The experimental results show that local mutual information provides more reliable results in detecting changes of the multitemporal images containing different lighting condition compared to the image differencing and NDVI technique, specifically in areas with less plant growth. In addition, it can also overcome the problem on selecting the threshold value. Besides, the findings of this study have also shown that band 3, which is sensitive to vegetation biomass, gave the best result in detecting area of changes compared to the others.
Selingan Island off Sandakan, Sabah is a famous turtle nesting ground and a part of the Turtle Islands Park (TIP) within the Coral Triangle region of Malaysia. This small island faces the serious problem of beach erosion that is reducing the turtle nesting area. Sabah Parks deployed stone revetments in 2005, followed by placement of reef balls at the southern part of the Selingan Island in 2007 for protecting the shoreline. The objective of this study was to determine the effectiveness of these measures for shoreline protection. Shoreline changes were determined from satellite images, beach profiling and field observations. Satellite images from 2010 to 2016 were obtained from Google Earth Pro analyzed to examine the changes in the shape and size of the island with QGIS software. Beach profiling was performed in December 2017 at three sites and compared with the condition in 2011. The findings indicated that the shape of the island was squeezed towards the east where the reef balls were located. The size of the island has not changed much in 9 years after the deployment of the reef balls, but a high volume of sediments accumulated at the south due to the presence of shoreline protection. Generally, the man-made structures in Selingan Island are effective in trapping the sediment and providing more nesting area for turtles. It is recommended that the shoreline dynamics of the island should be regularly monitored for better understanding of the changes and taking appropriate actions.
General flowering (GF) is a unique phenomenon wherein, at irregular intervals, taxonomically diverse trees in Southeast Asian dipterocarp forests synchronize their reproduction at the community level. Triggers of GF, including drought and low minimum temperatures a few months previously has been limitedly observed across large regional scales due to lack of meteorological stations. Here, we aim to identify the climatic conditions that trigger large-scale GF in Peninsular Malaysia using satellite sensors, Tropical Rainfall Measuring Mission (TRMM) and Moderate Resolution Imaging Spectroradiometer (MODIS), to evaluate the climatic conditions of focal forests. We observed antecedent drought, low temperature and high photosynthetic radiation conditions before large-scale GF events, suggesting that large-scale GF events could be triggered by these factors. In contrast, we found higher-magnitude GF in forests where lower precipitation preceded large-scale GF events. GF magnitude was also negatively influenced by land surface temperature (LST) for a large-scale GF event. Therefore, we suggest that spatial extent of drought may be related to that of GF forests, and that the spatial pattern of LST may be related to that of GF occurrence. With significant new findings and other results that were consistent with previous research we clarified complicated environmental correlates with the GF phenomenon.
Microwave Remote sensing data have been widely used in land cover and land use classification. The objective of this research paper is to investigate the feasibility of the multi-polarized ALOS-PALSAR data for land cover mapping. This paper presents the methodology and preliminary result including data acquisitions, data processing and data analysis. Standard supervised classification techniques such as the maximum likelihood, minimum distance-to-mean, and parallelepiped were applied to the ALOS-PALSAR images in the land cover mapping analysis. The PALSAR data training areas were chosen based on the information obtained from
optical satellite imagery. The best supervise classifier was selected based on the highest overall accuracy and
kappa coefficient. This study indicated that the land cover of Butterworth, Malaysia can be mapped accurately
using ALOS PALSAR data.
Decreasing water pollution is a big problem in coastal waters. Coastal health of ecosystems can be affected by high concentrations of suspended sediment. In this work, a Modified Hopfield Neural Network Algorithm (MHNNA) was used with remote sensing imagery to classify the total suspended solids (TSS) concentrations in the waters of coastal Langkawi Island, Malaysia. The adopted remote sensing image is the Advanced Land Observation Satellite (ALOS) image acquired on 18 January 2010. Our modification allows the Hopfield neural network to convert and classify color satellite images. The samples were collected from the study area simultaneously with the acquiring of satellite imagery. The sample locations were determined using a handheld global positioning system (GPS). The TSS concentration measurements were conducted in a lab and used for validation (real data), classification, and accuracy assessments. Mapping was achieved by using the MHNNA to classify the concentrations according to their reflectance values in band 1, band 2, and band 3. The TSS map was color-coded for visual interpretation. The efficiency of the proposed algorithm was investigated by dividing the validation data into two groups. The first group was used as source samples for supervisor classification via the MHNNA. The second group was used to test the MHNNA efficiency. After mapping, the locations of the second group in the produced classes were detected. Next, the correlation coefficient (R) and root mean square error (RMSE) were calculated between the two groups, according to their corresponding locations in the classes. The MHNNA exhibited a higher R (0.977) and lower RMSE (2.887). In addition, we test the MHNNA with noise, where it proves its accuracy with noisy images over a range of noise levels. All results have been compared with a minimum distance classifier (Min-Dis). Therefore, TSS mapping of polluted water in the coastal Langkawi Island, Malaysia can be performed using the adopted MHNNA with remote sensing techniques (as based on ALOS images).
Many residential areas of Kuala Lumpur are susceptible to landslides; this is seen in the frequency of landslide occurences in these areas. The objective of this study is to delineate landslide risk areas in support of development planning, monitoring and control of unstable areas. In this study, five landslide causative factors were extracted from satellite imagery and maps provided by the Geological Survey Department of Malaysia. Factors included in the study including land use, river density and lineament derived from Landsat ETM image, precipitation amount from rain gauge stations and lithology, were extracted from the geological map of the study area. Layers were analyzed and divided into subclasses. An average weightage score was applied to calculate the subclasses into percentage weights of influence on landslide. Overlay, geo-processing and geo-statistic techniques in GIS were used to discriminate these weighted subclasses into landslide susceptibility at low, medium and high levels of risk areas. Results showed very high susceptible areas covering 0.21% of Kuala Lumpur of which 5.02% were found in the highly urbanized areas. Meanwhile, a landslide susceptibility map was generated to show low, medium and high susceptible areas in Kuala Lumpur. Results were verified using recorded cases of landslides in Kuala Lumpur which showed a 77% agreement with the study.
Geographic Information System (GIS) and remote sensing are geospatial technologies that have been used for many years in environmental studies, including gathering and analysing of information on the physical parameters of wildlife habitats and modelling of habitat assessments. The home range estimation provided in a GIS environment offers a viable method of quantifying habitat use and facilitating a better understanding of species and habitat relationships. This study used remote sensing, GIS and Analytic Hierarchy Process (AHP) application tools as methods to assess the habitat parameters preference of Asian elephant. Satellite images and topographical maps were used for the environmental and topographical habitat parameter generation encompassing land use-land cover (LULC), Normalized Digital Vegetation Index (NDVI), water sources, Digital Elevation Model (DEM), slope and aspect. The kernel home range was determined using elephant distribution data from satellite tracking, which were then analysed using habitat parameters to investigate any possible relationship. Subsequently, the frequency of the utilization distribution of elephants was further analysed using spatial and geostatistical analyses. This was followed by the use of AHP for identifying habitat preference, selection of significant habitat parameters and classification of criterion. The habitats occupied by the elephants showed that the conservation of these animals would require good management practices within and outside of protected areas so as to ensure the level of suitability of the habitat, particularly in translocation areas.
Three techniques to retrieve information on sea ice thickness from both active and passive radar backscatter data are presented. The first inversion model is a combination of the radiative transfer theory with dense medium phase and amplitude correction theory (DMPACT), and the Levenberg-Marquardt optimization algorithm. The radiative transfer theory was applied as the forward model to generate radar backscatter data, while the DMPACT was included to account for the close spacing effect among the scatterers within the medium. The Levenberg-Marquardt optimization algorithm was then applied to reduce the error between the model generated radar backscatter data and the measured radar backscatter data from satellite images so that the sea ice thickness could be estimated. The second method presented was the neural network inversion method which utilizes a chain of neurons with variable weights. Once the network was fully operational it would be possible to predict the sea ice thickness, provided sufficient training data are given. The last method was the genetic algorithm which is a search technique used in order to predict the approximate sea ice thickness from the measured data. Data from ground truth measurements carried out in Ross Island, Antarctica, together with radar backscatter data extracted from purchased satellite images were used as input to verify the models. All three models were tested and successfully predicted sea ice thickness from actual terrain using the ground truth measurement data, with several constraints and assumptions placed to avoid problems during the retrieval process. While the models still have their own limitations, the potential use of the models for actual sea ice thickness retrieval was confirmed.
Satellite imagery reveals flowstripes on Foundation Ice Stream parallel to ice flow, and meandering features on the ice-shelf that cross-cut ice flow and are thought to be formed by water exiting a well-organised subglacial system. Here, ice-penetrating radar data show flow-parallel hard-bed landforms beneath the grounded ice, and channels incised upwards into the ice shelf beneath meandering surface channels. As the ice transitions to flotation, the ice shelf incorporates a corrugation resulting from the landforms. Radar reveals the presence of subglacial water alongside the landforms, indicating a well-organised drainage system in which water exits the ice sheet as a point source, mixes with cavity water and incises upwards into a corrugation peak, accentuating the corrugation downstream. Hard-bedded landforms influence both subglacial hydrology and ice-shelf structure and, as they are known to be widespread on formerly glaciated terrain, their influence on the ice-sheet-shelf transition could be more widespread than thought previously.
Analysis of relationship between sea surface temperature (SST) and Chlorophyll-a (chl-a) improves our understanding on the variability and productivity of the marine environment, which is important for exploring fishery resources. Monthly level 3 and daily level 1 images of Moderate Resolution Imaging Spectroradiometer Satellite (MODIS) derived SST and chl-a from July 2002 to June 2011 around the archipelagic waters of Spermonde Indonesia were used to investigate the relationship between SST and chl-a and to forecast the potential fishing ground of Rastrelliger kanagurta. The results indicated that there was positive correlation between SST and chl-a (R=0.3, p<0.05). Positive correlation was also found between SST and chl-a with the catch of R. kanagurta (R=0.7, p<0.05). The potential fishing grounds of R. kanagurta were found located along the coast (at accuracy of 76.9%). This study indicated that, with the integration of remote sensing technology, statistical modeling and geographic information systems (GIS) technique were able to determine the relationship between SST and chl-a and also able to forecast aggregation of R. kanagurta. This may contribute in decision making and reducing search hunting time and cost in fishing activities.
Coral reefs are rich in biodiversity and ecosystem services. However increase in degradation are still occurring at an alarming rate. In management of this ecosystem, determination of its spatial distribution is of importance. Satellite imageries can be used to map distribution extent using spectral characteristics which is a fundamental parameter in mapping. The aims of this study were to determine the spectral characteristics of corals and associated habitats and to map its spatial distribution using 2009 ALOS advanced visible and near infrared radiometer type 2 (AVNIR-2) satellite imagery. Results indicated that coral and habitats surrounding the area display variation in the spectral characteristics magnitude but displays similar spectral curve. Spectral characteristics from the corals and surrounding habitats were determined by presence of benthic microalgae and calcium carbonate. Maximum likelihood classification on the image produced five main classes. Spatial distribution of coral and associated habitats indicated five main zones which are sandy shore zone, sandy intertidal zone, seagrass zone, coral/submerged sandy zone and rocky zone. Distribution of live corals indicated coverage of 0.54 km2, sea grass (0.94 km2), sandy bottom (1.31 km2) and rocky shores (0.19 km2). The results of this study indicated that ALOS satellite data was able to determine variation in spectral characteristics of coral reefs and other habitats thus is capable of mapping the ecosystems spatial distribution.