ID: 56587
Title: Malaysia : Towards spatially enabled society
Author: Deepali Roy
Editor: Prof Arup Dasgupta
Year: 2011
Publisher: Geospatial Media and Communications Pvt. Ltd,Vol 02, Issue 03, October 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: Geospatial World
Keywords: None
Abstract: None
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56586
Title: Thailand: Getting back on track
Author: Deepali Roy
Editor: Prof Arup Dasgupta
Year: 2011
Publisher: Geospatial Media and Communications Pvt. Ltd,Vol 02, Issue 03, October 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: Geospatial World
Keywords: Geospatial Technology
Abstract: One of the early adopters of geospatial technology in South East Asia, Thailand has been steadily incorporating the technology in its various development activities. Here ' s a look at what ' s going on in the geospatial industry in Thailand.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56585
Title: Singapore: Geo-enabled ' government -with-you '
Author: Deepali Roy
Editor: Prof Arup Dasgupta
Year: 2011
Publisher: Geospatial Media and Communications Pvt. Ltd,Vol 02, Issue 03, October 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: Geospatial World
Keywords: Geospatial technology
Abstract: Geospatial technology has played a significant role in Singapore ' s march into the league of developed nations within a short span of time. Not content to rest on the achievements so far, Singapore is actively implementing the technology to secure its growth.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56584
Title: Philippines: Poised for growth
Author: Sarah Hisham and Deepali Roy
Editor: Prof Arup Dasgupta
Year: 2011
Publisher: Geospatial Media and Communications Pvt. Ltd,Vol 02, Issue 03, October 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: Geospatial World
Keywords: Geospatial technology
Abstract: With its aggressive economic growth and development plans and steadily growing awareness about he benfits of geospatial technology, especially its role in safeguarding the nation against the vagaries of nature, Philippines offers a lot of potential to the geospatial industry.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56583
Title: Indonesia : G-readiness for future
Author: Deepali Roy,
Editor: Prof Arup Dasgupta
Year: 2011
Publisher: Geospatial Media and Communications Pvt. Ltd,Vol 02, Issue 03, October 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: Geospatial World
Keywords: Geospatial Information Act
Abstract: Shedding legacies and moving beyond ' business as usual ' mindset, Indonesia is accelerating its economic transformation and evolving a new way of working. It is strengthening traditional driver with new technologies and evolving infrastructure to carry the nation forward. Here ' s an insight into the geospatial scenario of a country which has recently approved the landmark Geospatial Information Act.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56582
Title: Information to the people
Author: Ron Bisio
Editor: Prof Arup Dasgupta
Year: 2012
Publisher: Geospatial Media and Communications Pvt. Ltd,Vol 02, Issue 10, May 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: Geospatial World
Keywords: Public transport
Abstract: Tiny Singapore is strengthening public transport with GIS to meet the travel demands of increasing population.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56581
Title: Tech for the skies
Author: Deepali Roy
Editor: Prof Arup Dasgupta
Year: 2012
Publisher: Geospatial Media and Communications Pvt. Ltd,Vol 02, Issue 10, May 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: Geospatial World
Keywords: Air traffic management
Abstract: With growing air traffic volumes, there is a lot of demand on air traffic management to manage the congestion on the runways and in the skies. Geospatial technology can help address this demand.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56580
Title: Classification of savanna tree species, in the Greater Kruger National Park region, by integrating hyperspectral and LiDAR data in a Ramdom Forest data mining environment
Author: L. Naidoo, M A Cho, R Mathieu, G Asner
Editor: George Vosselman
Year: 2012
Publisher: Elsevier, Vol 69, April 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Savanna tree species, spectral variability, tree height, Random forest, predictor datasets
Abstract: The accurate classification and mapping of individual trees at species level in the savanna ecosystem can provide numerous benefits for the managerial authorities. Such benefits include the mapping of economically useful tree species, which are a key source of food production and fuel wood for the local communities, and of problematic alien invasive and bush encroaching species, which can threaten the integrity of the environment and livelihoods of the local communities. Species level mapping is particularly challenging in African savannas which are complex, heterogeneous, and open environments with high intra-species spectral variability due to differences in geology, topography, rainfall, herbivory and human impacts within relatively short distances. Savanna vegetation are also highly irregular in canopy and crown shape, height and other structural dimensions with a combination of open grassland patches and dense woody thicket - a stark contrast to the more homogeneous forest vegetation. This study classified eight common savanna tree species in the Greater Kruger National Park region, South Africa, using a combination of hyperspectral and Light Detection and Ranging (LiDAR)-derived structural parameters, in the form of seven predictor datasets, in an automated Random Forest modelling approach. The most important predictors, which were found to play an important role in the different classification models and contributed to the sucess of the hybrid dataset model when combined, were species tree height; NDVI; the chlorophyll b wavelength (466 nm) and a selection of raw, continuum removed and Spectral Angle Mapper (SAM) bands. It was also concluded that the hybrid predictor dataset Random Forest model yielded the highest classification accuracy and prediction success for the eight savanna tree species with an overall calssfication accuracy of 87.68% and KHAT value of 0.843.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56579
Title: Potential of texture measurements of two-date dual polarization PALSAR data for the improvement of forest biomass estimation
Author: Md. Latifur Rahman Sarker, Janet Nichol, Baharin Ahmad, Ibrahim Busu, Alias Abdul Rahman
Editor: George Vosselman
Year: 2012
Publisher: Elsevier, Vol 69, April 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: PALSAR, dual polarization, SAR image texture, saturation level, forest biomass, leave-one-out cross-validation
Abstract: The recently available space-borne SAR sensor, PALSAR, is more promising than its predecessor JERS-1 for biomass estimation because of its long wavelength (L-band), and its ability to provide data with different polarizations, varying incidence angles and higher spatial resolutions. This research investigates the potential of two-date dual polarization (HH and HV) SAR imagery for biomass estimation using different kinds of texture processing and different combinations of single and dual polarization ratios. The investigation is conducted in a mountainous, sub-tropical study area where biomass levels are far beyond the previously recognized saturation levels for L-band SAR images, and forest is a mixture of native and non-native species and plantations.
We analyzed two-date SAR data with four steps of image processing, including raw data processing in various combinations, texture measurement parameters of HH and HV polarizations, texture measurement parameters of HH and HV together (both jointly and as a ratio), and a ratio of two-date texture parameters along with a single and two-date ratio. When the processed images were compared with ground data from 50 plots, the performance from raw data processing was low, with adjusted r2=0.22, but after all four processing steps, promising model accuracy (adjusted r2= 0.90 and RMSE = 28.58 t/ha) and validation accuracy (using the Leave-One-Out - Cross-Validation) with adjusted r2 = 0.88 and RMSE = 35.69 t/ha, were achieved from the combination of single- and two-date polarization ratios of texture parameters.
The strong performance achieved indicates that L-band dual-polarization (HH and HV) SAR data from PALSAR has great potential for biomass estimation, far beyond the previously reported L-band dual-polarization (HH and HV) SAR data from PALSAR has great potential for biomass estimation, far beyond the previously reported L-band saturation point for biomass. This result is attributed to the synergy among texture processing and dual polarization on the othe hand, which were able to average out random speckle noise, and the use of ratio instead of absolute quantities, due to its well known ability to reduce forest structural and terrain effects. The additional use of two-date SAR data with these processing techniques was able to add complementary information derived from biomass response in both wet and dry seasons. Thus overall, undesirable image noise and terrain effects were reduced.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56578
Title: Transaction rules for updating surfaces in 3D GIS
Author: Gerhard Groger, Lutz Plumer
Editor: George Vosselman
Year: 2012
Publisher: Elsevier, Vol 69, April 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Updating, GIS, surface, three-dimensional, quality
Abstract: Three-dimensional surface models representing the terrain and the outer hull of objects such as buildings and bridges support important 3D GIS appliations, for example telecommunication planning and noise emission simulation. Updates of surface models often introduce errors which violate basic assumptions of users and their applications. The notion of geometric-topological consistency covers many of these assumptions. It guarantees that objects do not penetrate mutually or that objects completely cover othe objects. Assuring that updates do not violate geometric-topological consistency constitutes a major challenge for 3D GIS which has not been satisfactorily met so far. This article presents a solution which is based on efficient transaction rules for updating 3D surface models. We show that these rules are safe (consistency is preserved by any rule application) and complete (any consistent surface model can be generated by successive rule applications). For both properties rigorous mathematic proofs are given.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56577
Title: Using multi-frequency radar and discrete - return LiDAR measurements to estimate above-ground biomass and biomass components in a coastal temperate forest
Author: Olivier W Tsui, Nicholas C Coops, Michael A Wulder, Peter L Marshall, Adrian McCardle
Editor: George Vosselman
Year: 2012
Publisher: Elsevier, Vol 69, April 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Above-ground biomass, Radar, LiDAR, Coherence, Polarimetry, Temperate forests
Abstract: Height measurements from small-footprint discrete-return LiDAR and backscatter coefficients from C-and L-band radar were used independently and in combination to estimate above-ground component and total biomass for a coniferous temperate forest, located on Vancouver Island, British Columbia, Canada. Reference biomass data were obtained from plot-level data and used for comparison against the LiDAR and radar-based biomass models. For the LiDAR-only model, height metrics such as mean first return height and percentiles (e.g., 10th and 90th) of first returns correlated best to total above-ground and stem biomass. While percent of first returns above 2 m and percentiles (75th and 90th) of first returns height metrics correlated best to crown biomass. A comparison between above-ground components and total biomass indicate that stem biomass displayed the highest relationship with the LiDAR measurements while crown biomass showed the lowest relationship with relative root mean squared error ranging from 16% to 22%, respectively. Alternatively, the radar-only models indicated that for C-band radar, a combination of HH and VV backscatter demonstrated the most significant correlation with forest biomass compared to coherence based models with a relative mean squared error of 53%. For L-band radar, a combination of HH and HV backscatter showed the most significant correlation compared to coherence based models with a relatie root mean squared error of 44%. Exploring a mixture of C-and L-band backscatter and coherence based models revealed that a combination of C-HV and L-HV coherence magnitudes provided the best radar relationship with forest biomass with a relative root mean squared error of 35%. Also for all radar - based models, L- and C-band backscatte and coherence magnitudes were poorly correlated with individual biomass components when compared to total above-ground biomass. The adition of C- and L-band backscatter and coherence variables to the LiDAR -only biomass model was also investigated. The results showed that the integration of C-band HH backscatter to the LiDAR -only model significantly improved the relationship with forest biomass by explaining an additional 8.9% and 6.5% of the variability in total aboveground and stem biomass respectively, while C -band polarimetric entropy explained an additional 17.9% of the variability in crown biomass. Improvements in the relative root mean squared errors were also observed ranging from 7.1% to 11.7%. The study suggests that for a temperate forest dominanted by coniferous stands, the addition of C-band radar variables to a best LiDAR-only linear model provides improved estimates of above-ground component and total biomass.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56576
Title: Spatial content-based scene similarity assessment
Author: Caixia Wang, Anthony Stefanidis, Peggy Agouris
Editor: George Vosselman
Year: 2012
Publisher: Elsevier, Vol 69, April 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Scene matching, Similarity metrics, image matching
Abstract: Scene comparison and matching is a fundamental operation in geoinformatics. However, existing solutions are rather inadequate to support scene similarity assessment when comparing datasets collected from diverse sources especially ones that are available in diverse modalities (e.g. comparing image to vector datasets), or represent different time instances and thus differ partially in their content. In this paper we introduce a two-stage scene similarity assessment and matching framework that makes use of spatial scene content to compare and match two scenes as they may be captured in two different datasets (e.g. an aerial image and a map). At first stage our approach makes use of a matching algorithm based on the comparison of attributed graphs, where linear feature networks (e.g road networks) are transformed into graphs and network properties are expressed through graph-embedded invariant attributes. By matching these graphs we can assess the similarity between two scenes. At the second stage, we proceed with an invariant scene comparison metric that incorporates additional scene content in the form of object configurations present within individual road network loops (e.g building arrangements within city squares). By combining diverse but co-located pieces of information (e.g roads and buildings) in an integrated process, our algorithm supports scene comparison and matching even when comparing heterogeneous datasets. In this paper we present key theoretical concepts and provide experimental results to demonstrate the performance of the proposed approach.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56575
Title: A method for extracting burned areas from Landsat TM/ETM + images by soft aggregation of multiple spectral indices and a region growing algorithm
Author: D Stroppiana, G Bordogna, P Carrara, M Boschetti, L. Boschetti, P A Brivio
Editor: George Vosselman
Year: 2012
Publisher: Elsevier, Vol 69, April 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Fuzzy set theory, fire perimeters, multi-criteria approach, Mediterranean environment
Abstract: Since fire is a major threat to forests and wooded areas in the Mediterranean environment of Southern Europe, systematic regional fire monitoring is a necessity. Satellite data constitue a unique cost-effective source of information on the occurrence of fire events and on the extent of the area burned. Our objective is to devleop a (semi-) automated alogrithm for mapping burned areas from medium spatial resolution (30 m) satellite data. In this article we present a multi-criteria approach based on Spectral Indices, soft computing techniques and a region growing algorithm; theoretically this approach relies on the convergence of partial evidence of burning provided by the indices. Our proposal features several innovative aspects: it is flexible in adapting to a variable number o indices and to missing data: it exploits positive and negative evidence (bipolar information) and it offers different criteria for aggregating partial evidence in order to derive the layers of candidate seeds and candidate region growing boundaries. The study was conducted on a set of Landsat TM images, acquired for the year 2003 ove Southern Europe and pre-processed with the LEDAPS (Landsat Ecosystem Disturbance Adaptive Processing System) processing chain for deriving surface spectral reflectance pi in the TM bands. The proposed method was applied toshow its flexibility and the sensitivity of the accuracy of the resulting burned area maps to different aggregation criteria and thresholds for seed selection. Validation performed over an entire independent Landsat TM image shows the commission and omission errors to be below 21% and 3% respectively.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56574
Title: A simple method for distinguishing global Case-1 and Case-2 waters using SeaWiFS measurements
Author: Bunkei Matsushita, Wei Yang, Peng Chang, Fan Yang, Takehiko Fukushima
Editor: George Vosselman
Year: 2012
Publisher: Elsevier, Vol 69, April 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Inherent optical properties, Remote-sensing reflectance at 412 and 443 nm, Case-1 and Case-2 waters, GSM01
Abstract: Since the combinations of water constituents are different between Case-1 and Case-2 waters, bio-optical models, retrieval algorithms for water constituent concentrations and other applications in water-color remote sensing are also very dissimilar between these waters. Use of the algorithms specifically developed for Case-1 waters returns inaccurate results in Case-2 waters, and vice versa. To select an appropriate algorithm for a given water pixel, it is important to first determine whether it is a Case-1 or Case-2 water and to clarify its temporal variations. This paper presents a simple method based on the inherent optical properties (IOPs) of water bodies for discriminating global Case-1 and Case-2 waters based on satellite data. Compared with the previous methods, the newly proposed method only requires two remote-sensing reflectances at 412 and 443 nm for relative comparisons, and thus it not only can easily be implemented using satellite data but also is robust even for satellite data with imperfect atmospheric correction, unpredictable noise pixels in the images, and so on. The new method was then applied to seasonal SeaWiFS 9 - km data to map the global distribution of Case-1 and Case-2 waters for each season in 2003. The results showed that more than 80% of global waters belong to the Case-1 category throughout the year, and the Case-2 waters are mainly concentrated in the Northern Hemisphere along the coasts. Both the area and distribution of Case-1 and Case-2 waters changed seasonally. By using a sub-dataset from NOMAD, it was found that when the ratio of [aph(443) +aw(443)]/a(443) was larger (smaller) than 50%, about 70% (75%) of the samples were identified as Case-1 (Case-2) waters by the new method. Moreover, the semi-analytical alogrithm GSM01 was more accurate for distinguishing Case-1 than Case-2 waters, which implies that use of the proposed method to select the appropriate remote-sensing algorithm would be important.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 56573
Title: Robust hyperspectral vision-based classification for multi-season weed mapping
Author: Yun Zhang, David C. Slaughter, Erik S Staab
Editor: George Vosselman
Year: 2012
Publisher: Elsevier, Vol 69, April 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Computer vision, plant recognition, machine learning, multiclassifier system, weed control, seasonal variability
Abstract: This study investigated the robustness of hyperspectral image-based plant recognition to seasonal variability in a natural farming environment in the context of automated in-row weed control. A machine vision system was developed and equipped with a CCD camera integrated with a line-imaging spectrograph for close-range weed sensing and mapping. Three canonical Bayesian classifiers were developed using canopy reflectance (400 -795 nm) collected over three seasons for tomato and weeds. The performance of the three season-specific classifiers was tested by changing environmental conditions, resulting in an increase in total error rate of up to 36%. Global calibration across the complete span of the three seasons produced overall classification accuracies of 85.0%, 90.0%, and 92.7%, respectively, for 2005, 2006 and 2008. To improve the stability of global classifier over multiple seasons, a multiclassifier system was constructed with three canonical Bayesian classifiers optimized for the three seasons individually. This system was tested on a data set simulating an upcoming season with field conditions similar to that in 2005. The system increased the total discrimination accuracy to 95.8% for the tested season under simulation. This method provided an innovative direction for achieving robust plant recognition over multiple seasons by integrating expert knowledge from historical data that most closely matched the new field environment.
Location: 231
Literature cited 1: None
Literature cited 2: None