ID: 55797
Title: Computation of physical characteristics of a lake system using IRS P6 (LISS - III) imagery
Author: A M Sheela J Letha, Sabu Joseph, K K Ramachandran, Manoj Chacko
Editor: Alfred Stein
Year: 2012
Publisher: Elsevier, Vol 14, Issue 1, February 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: IRS P6, LISS-III Imagery, Water temperature, Depth, Turbidity, Akkulam - Veli lake, Kerala
Abstract: Lakes are versatile ecosystems and they are under the threat of eutrophication and siltation. The physical characteristics of a lake provide some insight into the status of the lake. Satellite imagery analysis now plays a prominent role in the quick assessment of characteristics of a lake system in a vast area. This study is an attempt to assess the water temperature, depth, and turbidity level of a lake system (Akkulam-Veli lake, Kerala, India) using IRS P6 - LISS - III imagery. Field data were collected on the date of the overpass of the satellite. For the assessment of water temperature from satellite imagery, regression equation using spectral ratio (green/red bands) is found to yield superior results than the simple regression equation and multiple regression equation. For predicting the water depth, radiance in green and red bands can be used whereas that for turbidity, radiance in green and SWIR can be used, IRS P6-LISS-III imagery can be effectively used for the assessment of the physical characteristics of a lake system at a low cost.
Location: 241
Literature cited 1: None
Literature cited 2: None
ID: 55796
Title: Multinomial logistic regression-based feature selection for hyperspectral data
Author: Mahesh Pal
Editor: Alfred Stein
Year: 2012
Publisher: Elsevier, Vol 14, Issue 1, February 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Feature selection, Multinomial logistic regression, support vector machines, classification accuracy
Abstract: This paper evaluates the performance of three feature selection methods based on multinomial logistic regression, and compares the performance of the best multinomial logistic regression-based feature selection approach with the support vector machine based recurring feature eliminatin approach. Two hyperspectral datasets, one consisting of 65 features (DAIS data) and other with 185 features (AVIRIS data) were used. Result suggests that a total of between 15 and 10 features selected by using the multinomial logistic regression-based feature selection approach as proposed by Cawley and Talbot achieve a significant improvement in classification accuracy in comparison to the use of all the features of the DAIS and AVIRIS datasets. In addition to the improved performance, the Cawley and Talbot approach does not require any user-defined parameter, thus avoding the requirement of a model selection stage. In comparison, the other two multinomial logistic regression-based feature selection approaches require one user-defined parameter and do not perform as well as the Cawley and Talbot approach in terms of (i)the number of features required to achieve classification accuracy comparable to that achieved using the full dataset, and (ii) the classification accuracy achieved by the selected features. The Cawley and Talbot approach was also found to be computationally more efficient than the SVM-RFE technique, though both use the same number of selected features to achieve an equal or even higher level of accuracy than that achieved with full hyperspectral datasets.
Location: 241
Literature cited 1: None
Literature cited 2: None
ID: 55795
Title: A synthesis of remote sensing and local knowledge approaches in land degradation assessment in the Bawku East District, Ghana
Author: G A B Yiran, J M Kusimi, S K Kufogbe
Editor: Alfred Stein
Year: 2012
Publisher: Elsevier, Vol 14, Issue 1, February 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Bawku East District, Deforestation, Desertification, Image change detection, Land degradation, Remote sensing, Satellite image classification
Abstract: A greater percentage of Northern Ghana is under threate of land degradation and is negatively impacting on the well-being of the people owing to deforestation, increasing incidence of drought, indiscriminate bush burning and desertification. The problem is becoming severe with serious implications on the livelihoods of the people as the land is the major resource from which they eke their living. Reversing land degradation requires sustainable land use planning which should be based on detailed up-to-date information on landscape attributes. This information can be generated though remote sensing analytical studies. Therefore, an attempt has been made in this study to collect data for planning by employing remote sensing techniques and ground truthing.
The analysis included satellite image classification and change detection between Landsat images captured in 1989, 1999 and 2006. The images were classified into the following classes: water bodies, close savannah woodland, grassland/unharvested farmland, exposed soil, burst scars, and settlement. Change detection performed between the 1989 and 1999 and 1989 and 2006 showed that the environment is deteriorating. Land covers such as close savannah woodland, open savannah woodland and exposed soil diminished over the period whereas settlement and water bodies increased. The grassland/unharvested farmland showed high increases because the images were captured at the time that some farms were still crops or crop residue.
Urbanization, land clearing for farming, over grazing, firewood fetching and bush burning were identified as some of the underlying forces of vegetal cover degradation. The socio-cultural beliefs and practices of the people also influenced land cover change as sacred groves as well as medicinal plants are preserved. Local knowledge is recognized and used in the area but it is not porperly integrated with scientific knowledge for effective planning for sustainable land management. This is due to lack of expertise in remote sensing and geographic information systems (GIS) in the area.
Location: 241
Literature cited 1: None
Literature cited 2: None
ID: 55794
Title: Surface roughness analysis of a conifer forest canopy with airborne and terrestrial laser scanning techniques
Author: K Weligepolage, A S M Gieske, Z Su
Editor: Alfred Stein
Year: 2012
Publisher: Elsevier, Vol 14, Issue 1, February 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Momentum roughness, Airborne laser scanning, Terrestrial laser scanning, conifer forest
Abstract: Two digital Canopy Height Models (CHMs) were generated using the novel Terrestrial Laser Scanning (TLS) technique combined with Airborne Laser Scanning (ALS) data, acquired over a conifer forest. The CHMs were used to extract cross-sections in order to derive surface geometric parameters. Different morphometric models were applied to estimate aerodynamic roughness parameters: the roughness length (Z0) and the displacement height (d0). The CHMs were also used to derive the area-height relationship of the canopy surface. In order to estimate roughness parameters the observed canopy area-height relationship was modelled by uniform roughness elements of paraboloid or conical shape. The estimated average obstacle density varies between 0.14 and 0.24 for both CHMs. The canopy height distribution is approximately Gaussian, with average heights of about 26 m and 21 m for CHMs generated with data from TLS and ALS respectively. The estimated values of Z0 and d0 depend very much on the selected model. It was observed that the Raupach models with parameters tuned to resemble the forest structure of the study area can be applied to a wide range of roughness densities. The cumulative area-height modelling approach also yielded results which are compatible with other models. The results confirm that, to model the upper canopy surface of the conifer forest, both the cone and the paraboloid shapes are fairly appropriate.
Location: 241
Literature cited 1: None
Literature cited 2: None
ID: 55793
Title: Potential reasons for ionospheric anomalies immediately prior to China ' s Wenchuan earthquake on 12 May 2008 detected by nonlinear principal component analysis
Author: Jyh-Woei Lin
Editor: Alfred Stein
Year: 2012
Publisher: Elsevier, Vol 14, Issue 1, February 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Nonlinear principal component analysis (NLPCA), Principal component analysis (PCA), Total electron content (TEC), Wenchuan earthquake, P-type semiconductor effect
Abstract: Nonlinear principal component anaysis (NLPCA0 is used to detect total electron content (TEC) anomalies for China ' s Wenchuan earthquake on 12 May 2008 (UT) (Mw = 7.9). NLPCA is applied to global ionospheric maps (GIMs) at height ranging from 150 to 200 km with transforms conducted for the time period 00:00-06:00 UT on 12 May 2008. The GIMs are analyzed using NLPCA whereby the GIMs are seperated into 100 smaller maps of 360 in longitude and 180in latitude. These smaller maps are constructed at 71 x 71 pixels forming the transform matrix of the NLPCA. The transform allows for a principal eigenvalue to be assigned for each of the smaller maps. The results of the transforms provide 100 principal eigenvalues covering the region and the epicenter of the Wenchuan earthquake. The possibility of TEC anomalies being caused by X-ray flux and geomagnetic activity is eliminated by reviewing X-ray flux data and the Kp index. The eigenvalues of NLPCA are compared with the eigenvalues of principal component analysis (PCA), and TEC anomalies are clearly detected using NLPCA. Large principal eigenvalues representative of earthquake-related TEC anomalies were found nearby the epicenter for the time period 00:00-0600 UT using NLPCA. The earthquake occurred at 06:28 UT. A potential cause of the clear TEC anomaly almost directly over the epicenter in the time period 0200-0400 would be very stark p-type semiconductor effect caused by rocks under extreme stress. The stress may have abated during other time periods reducing p-type semiconductor effects and associated TEC anomalies.
Location: 241
Literature cited 1: None
Literature cited 2: None
ID: 55792
Title: Effects of crop residue cover resulting from tillage practices on LAI estimation of wheat canopies using remote sensing
Author: Dehua Zhao, Tangwu Yang, Shuging An
Editor: Alfred Stein
Year: 2012
Publisher: Elsevier, Vol 14, Issue 1, February 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Remote sensing, wheat, residue, leaf area index, vegetation index
Abstract: Much research has been conducted seeking to reduce the noise interference from soil in remote sensing of vegetation, and thus more accurately predict plant biophysical parameters by developing differetn kinds of vegetation indices. However, few studies have examined the effect of residue cover on the estimation of leaf area index (LAI) using remote sensing techniques. Using the WinSail model along with ground measurements, this study sought to quantify the differences in spectral reflectance and commonly used ratio-based (NDVI and RVI), soil-adjusted (TSAVI and SAV12) and hyperspectral (dRE and REIP) vegetation indices between wheat canopies with bare soil and rice residue as backgrounds and to identify the vegetation indices that represented the best combination of low sensitivity to residue cover and high sensitivity to variation in LAI. We found large variations in residue cover in wheat fields, with average cover of 6.70% and 45.9% for intensively tilled and untilled fields, respectively. Changing the background from soil to residue resulted in substantial changes in both reflectance and vegetation indices of canopies when LAI varied between 0.1 and 1.0 with average absolute values of relatively percent differences (IRPDI) ranging from 4.21% to 19.03% for the six vegetation indices used in this study. We found that changes in the background from bare soil to rice residue would result in underestimation of LAI by NDVI, RVI, TSAVI, and SAVI2, and overestimation of LAI by dRE and REIP. More green leaves in the wheat canopy were covered by residue in untilled fields than in intensively tilled fields, with an average 8.57% obscuring rate when green cover was between 1.0% and 85%; this would lead to the underestimation of LAI by remote sensing techniques in untilled fields. Ultimately, we determined that dRE was the best choice of the six indices for predicting LAI because of its significant relationship with LAI and because it was least sensitive to residue effects yet remained sensitive to variation in LAI, underestimating LAI in untilled fields by only 3.70%. The better performance of dRE was attributed to the opposite additive influences of the residue obscuring effect and brightness differences between soil and residue.
Location: 241
Literature cited 1: None
Literature cited 2: None
ID: 55791
Title: Estimation of forest above-ground biomass using multi-parameter remote sensing data over a cold and arid area
Author: Xin Tian, Zhongbo Su, Erxue Chen, Zengyuan Li, Christiaan van der Tol, Jianping Guo, Qisheng He
Editor: Alfred Stein
Year: 2012
Publisher: Elsevier, Vol 14, Issue 1, February 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: k-NN method, Regression method, Above-ground biomass, configuration
Abstract: Remote sensing is a valuable tool for estimating forest biomass in remote areas. This study explores retrieval of forest above-ground biomass (AGB) over a cold and arid region in Northwest China, using two different methods (non-parametric and parametric), field data, and three different remote sensing data: a SPOT-5 HRG image, multi-temporal dual - polarization ALOS PALSAR and airborne LiDAR data. The non-parameteric method was applied in 300 different configurations, varying both the mathematical formualtion and the data input (SPOT-5 and ALOS PALSAR), and the quality of the performance of each configuration was evaluated by Leave One Out (LOO) cross-validation agianst ground measurements. For the parametric method (The multivariate linear regression), the same remote sensing data were used, but is one additional configuration the airborne LiDAR data were used for stepwise multiple regression.
The result of the best performing non-parameteric configuration was satisfactory (R = 0.69 and RMSE = 20.7 tons/ha). The results for the parametric method were notoriously inaccurate, except for the case where airborne LiDAR data were included. The regression method with airborne low density LiDAR point cloud data was the best of all tested methods (R = 0.84 and RMSE = 15.2 tons/ha). A cross comparison of the two best results showed that the non-parametric method performs nearly as well as the parametric method with LiDAR data, except for some areas where forests have a very heterogeneous structure. It is concluded that the non-parametric method with SPOT data is able to map forest AGB operatively over the cold and arid region as an alterntive to the more expensive airborne LiDAR data.
Location: 241
Literature cited 1: None
Literature cited 2: None
ID: 55790
Title: Respond of Upper ocean during passage of MALA cyclone utilizing ARGO data
Author: Naresh Krishna Vissa, A N V Satyanarayana, B Prasad Kumar
Editor: Alfred Stein
Year: 2012
Publisher: Elsevier, Vol 14, Issue 1, February 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Upper ocean, Mixed layer, MALA cyclone, ARGO data, Sea surface cooling
Abstract: In the present study an attempt has been made to study the response of the upper ocean atmospheric interactions during the passage of a very severe cyclonic storm (VSCS) ' MALA ' formed over the Bay of Bengal (BOB) on 24 April 2006. Deepening of mixed layer depth (MLD), weakening of barrier layer thickness (BLT) associated with a deeper 260C isotherm level (D26) is observed after the MALA passage. Tropical cyclone heat potential (TCHP) and depth averaged temperature (T?100) exhibit a good degree of correlation for higher values. The passage of MALA cyclone also resulted in cooling the sea surface temperature (SST) by 4-50C. The findings suggest that turbulent and diapycnal mixing are responsible for cooler SSTs. Turbulent air-sea fluxes are analyzed using Objectively Analyzed air-sea Fluxes (OAFlux) daily products. During the mature stage of MALA higher latent heat flux (LHF), sensible heat flux (SHF), and enthalpy (LHF + SHF) are observed in the right side of this extreme event.
Location: 241
Literature cited 1: None
Literature cited 2: None
ID: 55789
Title: A hierarchial naive Bayesian network classifier embedded GMM for textural image
Author: Jianbin Tao, Qingquan Li, Changqing Zhu, Jili Li
Editor: Alfred Stein
Year: 2012
Publisher: Elsevier, Vol 14, Issue 1, February 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Bayesian network, High-dimensional remote sensing data, Textural image, classification
Abstract: To address several problems in high-dimensional textures feature space and the deficiencies of the single Gaussian distribution for remote sensing data, this paper proposes a hierarchical naive Bayesian network classifier embedded in a Gaussian mixture model for high-dimensional textural image classification. High-dimensional features are grouped by the model on the basis of the correlations between them. In this way, the high-dimensional problem is decomposed into multiple problems of lower dimension. At the same time, for each group of features, a Gaussian mixture model is applied to simulate the data distribution in feature space for land covers, which fits the "original" data distribution better than a single Gaussian model. The Gaussian mixture model is embedded as a child node into a naive Bayesian network, and then the final classification result is obtained within the naive Bayesian network classifier framework. Experimental results for the classification of Landsat ETM+ and QuickBird image textures demonstrated that the classification accuracy of this method is better than that of a traditional Bayesian network classifier and some other classival classifiers. Comparing with the method dealing with original high-dimensional features, it is also more efficiency and effectiveness with fewer demand of sample size and lower time complexity.
Location: 241
Literature cited 1: None
Literature cited 2: None
ID: 55788
Title: Image-based predictive ecosystem mapping in Canadian arctic parks
Author: Robert Fraser, Donald McLennan, Serguei Ponomarenko, Ian Olthof
Editor: Alfred Stein
Year: 2012
Publisher: Elsevier, Vol 14, Issue 1, February 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Remote sensing, decision trees, predictive modeling, ecosystem mapping, digital elevation model
Abstract: Ecological monitoring of Arctic national parks is challenging owing to their size and remote locations. Baselien ecosystem maps are a basic requriement for monitoring and are often derived from classification of remote sensing data. In many cases, however, the vegetation communities of interest overlap spectrally and cannot be separated using imagery alone. One solution is to use ancillay spatial data that are able to predict the distribution of Arctic ecosystems, which are often structured along environmental gradients. This paper presents a new image-based predictive ecosystem mapping (I-PEM) method that integrates remote sensing-based vegetation mapping with predictive terrain attributes from a digital elevation model. The approach is unique in its use of a conventional, air photo-based ecosystem map to train a decision tree classifier for mapping over a larger area of satellite coverage. I-PEM is demonstrated using SPOT HRVIR imagery over Ivvavik National Park in Yukon and Torngat Mountains National Park in Newfoundland. Results indicate that a 28-class ecosystem map derived from air-photo interpretation can be reproduced using the method with 85% or greater accuracy.
Location: 241
Literature cited 1: None
Literature cited 2: None
ID: 55787
Title: Multi-and hyperspectral geologic remote sensing: A review
Author: Freek D van der Meer, Harald M A van der Werff, Frank J A van Ruitenbeek, Chris A Hecker, Wim H Bakker, Marleen F Noomen, Mark van der Meijde, E John M Carranza, J Boudewijn de Smeth, Tsehaie Woldai
Editor: Alfred Stein
Year: 2012
Publisher: Elsevier, Vol 14, Issue 1, February 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Geologic remote sensing, Landsat, ASTER, Hyperspectral
Abstract: Geologists have used remote sensing data since the advent of the technology for regional mapping, structural interpretation and to aid in prospecting for ores and hydrocarbons. This paper provides a review of multispectral and hyperspectral remote sensing data, products and applications in geology. During the early days of Landsat Multispectral scanner and Thematic Mapper, geologists developed band ratio techniques and selective principal component analysis to produce iron oxide and hydroxyl images that could be related to hydrothermal alteration. The advent of the Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) with six channels in the shortwave infrared and five channels in the thermal region alloweed to produce qualitative surface mineral maps of clay minerals (kaolinite, illite), sulfate minerals (alunite), carbonate minerals (calcite, dolomite), iron oxides (hematite, goethite), and silica (quartz) which allowed to map alteration facies (propylitic, argillic etc). The step toward quantitative and validated (subpixel) surface mineralogic mappign was made with the advent of high spectral resolution hyperspectral remote sensing. This led to a wealth of techniques to match image pixel spectra to library and field spectral and to unravel mixed pixel spectra to pure endmember spectra to derive sub-pixel surface compositional information. These products have found their way to the mining industry and are to a lesser extent taken up by the oil and gas sector. The main threat for geologic remote sensing lies in the lack of (satellite) dat continuity. There is however a unique opportunity to develop standardized protocols leading to validated and reproducible products from satellite remote sensing for the geology community. By focusing on geologic mapping products such as mineral and lithologic maps, geochemistry, P-T paths, fluid pathways etc. the geologic remote sensing community can bridge the gap with the geosciences community. Increasingly workflows should be multidisciplinary and remote sensing data should be integrated with field observations and subsurface geophysical data to monitor and understand geologic processes.
Location: 241
Literature cited 1: None
Literature cited 2: None
ID: 55786
Title: Identifying a suitable combination of classification technique and bandwidth (s) for burned area mapping in tallgrass prairie with MODIS imagery
Author: Rhett L Mohler, Douglas G Goodin
Editor: Alfred Stein
Year: 2012
Publisher: Elsevier, Vol 14, Issue 1, February 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Burned area mapping, MODIS, object-based classification, Tallgrass prairie, grasslands
Abstract: Prescribed fire is crucial to the ecology and maintenance of tallgrass prairie, and its application affects a variety of human and natural systems. Consequently, maps showing the location and extent of these fires are critical to managing tallgrass prairies in a manner that balances the need of all stakeholders. Satellite-based optical remote sensing can provide the necessary input for this mapping, but it requires the development mapping methods that are specific to tallgrass prairie. In this research, we devise and test a suitable mapping method by comparing the efficacy of seven combinations of bands and indices from the MODIS sensor using both pixel and object -based classification methods. Due to the relatively small size of many prescribed fires in tallgrass prairie, scenarios based on the 250 m spatial resolution red and NIR bands outperformed those based on the coarser 500m spatial resolution bands, and a combination of both red and NIR performed better than each 250 m band individually. Object-based classfication offered no improvement over pixel-based classification, and performed poorer insome cases. Our results suggest that mapping burned areas in tallgrass prairie should be doen at a minimum of 250m spatial resolution, should used as a pixel-based classification technique, and should use a combination of red and NIR.
Location: 241
Literature cited 1: None
Literature cited 2: None
ID: 55785
Title: Assessing ground cover at patch and hillslope scale in semi-arid woody vegetation and pasture using fused Quickbird data
Author: Carlos Munoz-Robles, Paul Frazier, Matthew Tighe, Nick Reid, sue V Briggs, Brian Wilson
Editor: Alfred Stein
Year: 2012
Publisher: Elsevier, Vol 14, Issue 1, February 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Ecohydrology, Image fusion, Inter-patches, Modified intensity-hue-saturation, transform, patches, Australia
Abstract: The amount and distribution of vegetation and ground cover are important factors that influence resource transfer (e.g. runoff, sediment) in patterned semi-arid landscapes. Identifying and describing these features in detail is an essential part of measuring and understanding ecohydrological processes at hillslope scales that can then be applied at broader scales. The aim of this study was to develop a comprehensive methodology to map ground cover using high resolution Quickbird imagery in woody and non-woody (pasture) vegetation. The specific goals were to: (1) investigate the use of several techniques of image fusion, namely principal components analysis (PCA), Brovey transform, modified intensity-hue-saturation (MIHS) and wavelet transform to increase the spatial detail of multispectral Quickbird data ; (2) evaluate the performance of the red and near-infra-red bands (NIR), the difference vegetation index (DVI), and the normalised difference vegetation index (NDVI) in estimating ground cover, and (3) map and assess spatial and temporal changes in ground cover at hillslope scale using the most appropriate method or combination of methods. Estimates of ground cover from the imagery were compared with a subset of observed ground cover estimates to determine map accuracy. The MIHS alogrithm produced images that best preserved spectral and spatial integrity, while the red band fused with the panchromatic band produced the most accurate ground cover maps. The patch size of the ground cover beneath canopies was similar to canopy size, and percent ground cover (mainly litter) increased with canopy size. Ground cover was mapped with relative accuracies of 84% in the woody vegetation and 86% in the pasture. From 2008 to 2009, ground cover increased from 55% to 65% in the woody vegetation and from 40% to 45% in the pasture. These ground cover maps can be used to explore the spatial ecohydrological interactions between areas of different ground cover at hillslope scale with application to management at broader scales.
Location: 241
Literature cited 1: None
Literature cited 2: None
ID: 55784
Title: Crop area mapping in West Africa using landscape stratification of MODIS time series and comparison with existing global land products
Author: Elodie Vintrou, Annie Desbrosse, Agnes Begue, Sibiry Traore, Christian Baron, Danny Lo Seen
Editor: Alfred Stein
Year: 2012
Publisher: Elsevier, Vol 14, Issue 1, February 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Cultivated area, MODIS time series, stratification, Global land products, Mali
Abstract: In Africa, food security early warning systems use satellite-derived data concerning crop conditions and agricultural production. Such systems can be improved if they are provided with a more reliable estimation of the cultivated area at national scale. This paper evaluates the potential of using time series from the MODerate resolution Imaging Spectroradiometer MOD13Q1 (16-day composite of normalized difference vegetation index at 250m resolution) to extract cultivated areas in the fragmented rural landscapes of Mali. To this end, we first stratified Southern Mali inot 13 rural landscapes based on the spatio-temporal variability of NDVI and textural indices, using an object-oriented classfication scheme.
The accuracy of the resulting map (MODIScrop) and how it compares with existing coarse - resolution global land products (GLC2000 Africa, GLOBCOVER, MODIS V05 and ECOCLIMAP-II), was then assessed against six crop/non-crop maps derived from SPOT 2.5 m resolution images used as references. For crop areal coverage, the MODIScrop cultivated map was successful in assessing the overall cultivated area at five out of the six validation sites (less than 6% of the absolute difference), while in terms of crop spatial distribution, the producer accuracy was between 33.1% and 80.8%. This accuracy was linearly correlated with the mean patch size index calculated on the SPOT crop maps (r2 = 0.8). Using the Pareto boundary as an accuracy assessment method at the study sites, we showed that (i) 20-40% of the classification crop error was due to the spatial resolution of the MODIS sensor (250 m), and that (ii) compared to MODIS V05, which otherwise performed better than the other existing products, MODIScrop generally minimized omission-commission errors. A spatial validation of the different products was carried out using SPOT image classifications as reference. In the corresponding error matrices, the fraction of correctly classified pixels for our product was 70%, compared to 58% for MODIS V05, while it ranged between 40% and 51% for the GLC2000, the ECOCLIMAP-II and the GLOBCOVER.
Location: 241
Literature cited 1: None
Literature cited 2: None
ID: 55783
Title: Understanding the 2007-2008 eruption of Anak Krakatau Volcano by combining remote sensing technique and seismic data
Author: Agustan, Fumiaki Kimata, Yoga Era Pamitro, Hasanuddin Z Abidin
Editor: Alfred Stein
Year: 2012
Publisher: Elsevier, Vol 14, Issue 1, February 2012
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation and Geoinformation
Keywords: Krakatau, PALSAR, InSAR, ground deformation, volcano eruption
Abstract: One of the most violent volcanic eruption in recorded history is the Krakatau eruption on August 27, 1883. This caldera-forming eruption destroyed two thirds of the Krakatau volcanic island in the Sunda Strait resulting in the remaining three small islands later known as the Krakatau complex. From 1927 to 1929, eruptions in the center of Krakatau complex have produced a new volcano named Anak Krakatau, which continuously builds its body through eruptions until now. One eruption event took place between 2007 and 2008 with several eruptions that lasted in total from the end of October 2007 to August 2008. Eruptions were characterized by Strombolian activity with ash columns 1 km high, as well as pyroclastic and lava flows. We monitored the ground deformation of Anak Krakatau Volcano by interfering PALSAR data from June 2007 to February 2009. The result of InSAR technique shows a complex pattern of ground deformation. Inflation up to 4 cm, together with subsidence around the crater, was measured for almost three months before the eruption with a volume increase of approximately 1 x 106 m3. After the eruption, the southwest side of the volcanic cone subsided by 18 cm, whereas the northeast side of the cone uplifted 12 cm in almost two years. The observed ground deformation after the eruption can be explained by 4 m of tensile opening along a dipping rectangular tensile dislocation buried in an elastic half-space, approximately 400m below sea level.
Location: 241
Literature cited 1: None
Literature cited 2: None