Mapping seagrass from satellite remote sensing data

This paper reviews some early results on a method adopted in mapping seagrass using Landsat-5 Thematic Mapper data. Seagrass information was extracted from satellite remotely sensed data using depth invariant index (DII) where the sea bottom features were expressed as index (i.e. each bottom type was represented by one index). DII was determined from radiance values recorded in band 1, 2 and 3 which taking into account the effect of water attenuation. Sea truth samples collected during the satellites overpass were used in calibrating DII and an independent accuracy assessment of information extracted

Integration of Remote Sensing-GIS Techniques for Mapping and Monitoring Seagrass and Ocean Colour off Malaysian Coasts

This paper describes seagrass and ocean colour mapping off Peninsular Malaysia. The seagrass were extracted from visible bands of Landsat TM using the depth invariant index of the scabottom type. The ocean colour which much referred to plankton concentration is derived by regressing samples from known site collected at time of satellite overpass. Out these information were then input into GIS database which were also being established to assist the Marine Fisheries Management and Development Centre in managing and monitoring coastal areas This paper also addresses the experience gained in building spatial database for coastal areas various dala collected from various mapping environments were carried out

Carbon sequestration model of tropical rainforest ecosystem using satellite remote sensing data

Various measurements methods have been used to determine the validity of the information produced for carbon sequestration especially in tropical rainforests. Generally, these methods can be divided into two major categories which are the micrometeorological and biometric approaches. The former uses remote sensing and tower flux and the latter refers to field direct measurement of biomass. Presently, use of a single measurement approach has sometimes caused uncertainty in the accuracy of carbon sequestration in terms of the source or sink of carbon in these forests. Thus, this study proposed and developed a new model for carbon sequestration generated from the integration of remote sensing and biometric approach. This study was carried out in Pasoh Forest Reserve and the model was used for up-scaling to estimate the carbon concentration of the entire forest. Data for remote sensing were obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data and the biometric approach was based on tree census and litterfall observations. The results for the years 2000 until 2009 based on the new model showed that the carbon sequestration was a carbon source with increments ranging between -1.421 t ha-1yr-1 to -16.573 t ha-1yr-1, a mean value of -8.526 t ha-1yr-1 and Root Mean Square Error (RMSE) 2.916. The use of the new model revealed that there is a 6% accuracy improvement in the results as compared to a single-based remote sensing model. As a conclusion, the integration of approaches for a new model for carbon sequestration is more efficient than the use of a single approach. Furthermore, the new model is suitable for validating and calibrating global automatic climate products

Terrain mapping for the southwestern desert of Iraq using interferometry method from sentinel-1A images

Synthetic aperture radar Interferometry is a popular three-dimensional imaging technique for creating a Digital Elevation Model. Using traditional methods for creating DEMs and terrain mapping is one of the methods that require high cost and time-consuming, which has affected the creation and updating of terrain maps in Iraq, so this study aims to use the InSAR technology to generate DEM, which contributes to the creation of terrain maps. In this work, the synthetic aperture radar interferometry approach was used on the interference stack generated from a pair of Sentinel-1A images within the SNAP program to generate a DEM and a terrain map of the desert region in south-western Iraq. The elevations of the digital elevation model were compared with those of the RTK-GCPs points in the region of interest. The results obtained from this study are a terrain map with the contour lines generated from the digital elevation model created by the InSAR technique with an accuracy of 18 m, with the root mean square error of the DEM being 8.17. The outputs prove the effectiveness of InSAR technology in generating accurate DEM that contributes to creating terrain maps in less time and cost than traditional methods

Comparative analysis of support vector machine, maximum likelihood and neural network classification on multispectral remote sensing data

Land cover classification is an essential process in many remote sensing applications. Classification based on supervised methods have been preferred by many due to its practicality, accuracy and objectivity compared to unsupervised methods. Nevertheless, the performance of different supervised methods particularly for classifying land covers in Tropical regions such as Malaysia has not been evaluated thoroughly. The study reported in this paper aims to detect land cover changes using multispectral remote sensing data. The data come from Landsat satellite covering part of Klang District, located in Selangor, Malaysia. Landsat bands 1, 2, 3, 4, 5 and 7 are used as the input for three supervised classification methods namely support vector machines (SVM), maximum likelihood (ML) and neural network (NN). The accuracy of the generated classifications is then assessed by means of classification accuracy. Land cover change analysis is also carried out to identify the most reliable method to detect land changes in which showing SVM gives a more stable and realistic outcomes compared to ML and NN

A short review on causes of sea level rise for climate monitoring

Sea level rise has currently become a major issue for climate change. It has globally drawn attention because as time passes, global sea levels will continue to rise at an accelerating rate in the 21st century. It will cause a serious impact on environmental problems such as coastal inundation, salt intrusion, coastal erosion, and other phenomena. These scenarios lead to earth problems in which land and oceans continue shifting due to climate change, posing a threat to the very existence of all living beings in the coming years. As a result, climate monitoring is critical for tracking the change. Therefore, this paper reviews the physical factors that contribute to sea level rise. The main contributors for sea level rises, such as ice melting from land into the ocean, thermal expansion, a slowing of the Gulf Stream, and land sinkage, are being discussed. This paper also emphasises the studies of regional sea level, and sea level rate changes. Finally, this review will be discussed in order to clarify the causes of sea level rise issues for human society

Evaluation of spatiotemporal dynamics of land cover and land surface temperature using spectral indices and supervised classification: a case study of Jobai Beel Area, Bangladesh

This study aims to evaluate the spatiotemporal change of land cover (LC) and surface temperature of the Jobai Beel area, an exclusive agriculture zone, situated in the far-flung area of northwest Bangladesh using satellite data. Multi-temporal Landsat series of data from 1989 to 2020 and geospatial techniques have been employed to evaluate the LC change and land surface temperature (LST) variation. Different spectral indices such as NDVI, MNDWI, NDBal have been used to retrieve individual LC. Corresponding LST has also been extracted using the thermal bands. Supervised Classification and the post-classification change detection technique were employed to determine the temporal changes and validate the individual LC. The results were employed to assess the LST variation associated with LC changes. The results reveal that the area had undergone a drastic and inconsistent heterogeneous LC transformation during the study period. Water and vegetation areas have expanded at a rate of 0.24km2/year and 0.45km2/year respectively, while bare lands have shrunk at a rate of 0.70km2/year. In general, Bare land exhibits a significant positive correlation, when Vegetation areas show a significant negative correlation with LST. However, the correlation between water areas and LST was found statistically insignificant. Agriculture in the form of vegetation has been found the most dominating land cover character throughout the study period, which has been regulating the LST variation across the area

Naïve Bayes Classification Of High-Resolution Aerial Imagery

In this study, the performance of Naïve Bayes classification on a high-resolution aerial image captured from a UAV-based remote sensing platform is investigated. K-means clustering of the study area is initially performed to assist in selecting the training pixels for the Naïve Bayes classification. The Naïve Bayes classification is performed using linear and quadratic discriminant analyses and by making use of training set sizes that are varied from 10 through 100 pixels. The results show that the 20 training set size gives the highest overall classification accuracy and Kappa coefficient for both discriminant analysis types. The linear discriminant analysis with 94.44% overall classification accuracy and 0.9395 Kappa coefficient is found higher than the quadratic discriminant analysis with 88.89% overall classification accuracy and 0.875 Kappa coefficient. Further investigations carried out on the producer accuracy and area size of individual classes show that the linear discriminant analysis produces a more realistic classification compared to the quadratic discriminant analysis particularly due to limited homogenous training pixels of certain objects

Individual tree measurement in tropical environment using terrestrial laser scanning

Detailed forest inventory and mensuration of individual trees have drawn attention of research society mainly to support sustainable forest management. This study aims at estimating individual tree attributes from high density point cloud obtained by terrestrial laser scanner (TLS). The point clouds were obtained over single reference tree and group of trees in forest area. The reference tree is treated as benchmark since detailed measurements of branch diameter were made on selected branches with different sizes and locations. Diameter at breast height (DBH) was measured for trees in forest. Furthermore tree height, height to crown base, crown volume and tree branch volume were also estimated for each tree. Branch diameter is estimated directly from the point clouds based on semi-automatic approach of model fitting i.e. sphere, ellipse and cylinder. Tree branch volume is estimated based on the volume of the fitted models. Tree height and height to crown base are computed using histogram analysis of the point clouds elevation. Tree crown volume is estimated by fitting a convex-hull on the tree crown. The results show that the Root Mean Squared Error (RMSE) of the estimated tree branch diameter does not have a specific trend with branch sizes and number of points used for fitting process. This explains complicated distribution of point clouds over the branches. Overall cylinder model produces good results with most branch sizes and number of point clouds for fitting. The cylinder fitting approach shows significantly better estimation results compared to sphere and ellipse fitting models