ID: 55803
Title: Seedling evaluation of Diploknema butyracea (Roxb.) H J Lam
Author: Nawa Bahar, V R R Singh and Preeti Sharma
Editor: Dr V R R Singh
Year: 2010
Publisher: The Indian Forester, Vol 136, No 11, November 2010
Source: Centre for Ecological Sciences
Reference: None
Subject: The Indian Forester
Keywords: Diploknema butyracea, Evaluation, seedlings, germination, investigation
Abstract: The present investigation was carried out on seedlings evaluation in Diploknema butyracea (Cheura) under laboratory condition. During the course of test, the nature of germination was epigeous and the normal and abnormal seedlings were observed 91 and 9 percent respectively. Ten categories of abnormalities were also found in this species.
Location: Kumta Field Station
Literature cited 1: None
Literature cited 2: None
ID: 55802
Title: Vegetation types and their influence on some secondary nutrients and micronutrients in soils of Meghalaya forests
Author: M Bala Krishna Reddy
Editor: Dr V R R Singh
Year: 2010
Publisher: The Indian Forester, Vol 136, No 11, November 2010
Source: Centre for Ecological Sciences
Reference: None
Subject: The Indian Forester
Keywords: Secondary nutrients, micronutrients, vegetation types, lime rich forest soils
Abstract: The relationship between soil and vegetation is both dynamic and temporal. The variation in soil properties across the forests in Meghalaya is a result of iterplay of several factors, chief among which are climate, topography, tree species composition and state of health of teh sylvan ecosystem. Soil pH had a direct bearing on micronutrient content of the soils of the forests. Apart from the observation that certain important soil characteristics are well correlated with the vegetation types in the virgin forest tracts of Meghalaya it was also found that some secondary nutrients and micronutrients serve as useful indicators of state of health of the forest ecosystems of the State.
Location: Kumta Field Station
Literature cited 1: None
Literature cited 2: None
ID: 55801
Title: Response of important tropical tree species to elevated CO2
Author: S Varadharajan, C Buvaneswaran, Rekha R Warrier and R S C Jayaraj
Editor: Dr V R R Singh
Year: 2010
Publisher: The Indian Forester, Vol 136, No 11, November 2010
Source: Centre for Ecological Sciences
Reference: None
Subject: The Indian Forester
Keywords: Teak, Ailanthus, Casuarina, elevated CO2
Abstract: Global warming plays a major role in climate change which is caused mainly due to increase in CO2 level in the atmosphere. Present study attempted to evaluate four important tropical tree species, Tectona grandis, Ailanthus excelsa, Casuarina equisetifolia and Casuarina junghuhniana for adaptation to elevated levels of CO2 at nursery stage. The study was conducted inside the poly tunnels and CO2 enrichment was done to double the concentration. Seedlings in poly tunnel without CO2 enrichment served as control. The short term experiments revealed that of the four species studied, casuarinas performed well under elevated CO2 conditions in terms of all growth parameters studied. The performance of teak alone was poor indicating adverse effect of CO2 enrichment on morphological traits of this species as seen by reduced leaf area, lesser dry matter accumulation both in shoot and root system and poor Seedling Quality Index at elevated CO2 levels. Thus, in the present study, all the species except teak showed a positive response to CO2 enrichment in terms of morphological traits while Casuarina equisetifolia and C. junghuhniana showed better growth and seedling quality indicating their better adaptability to elevated CO2 level.
Location: Kumta Field Station
Literature cited 1: None
Literature cited 2: None
ID: 55800
Title: Resin quality evaluation of Indian Chir pine obtained by bore hole and rill methods
Author: G S Rawat
Editor: Dr V R R Singh
Year: 2010
Publisher: The Indian Forester, Vol 136, No 11, November 2010
Source: Centre for Ecological Sciences
Reference: None
Subject: The Indian Forester
Keywords: Quality Evaluation, Oleoresin, Chir pine, Borehole method
Abstract: The quality and yield of oleogum resin obtained from chir pine, varies by edaphic, climatic and genetic factors besides the tapping methods. In this context, borehole method of tapping has been found to be more feasible and superior over conventional methods in terms of minimum damage to the tree, and other inputs like time, labour, etc. Physicochemical analysis, based on standard methods of rosin and turpentine separated from chirpine resin samples, was attempted for quality evaluation. The analysis of oleoresin, rosin and turpentine revealed that the borehole tapped oleoresin has less contaminants and better quality with hgih turpentine as compared to rill tapped oleoresin. THis information may be helpful for tapping of high quality resin to the forest managers.
Location: Kumta Field Station
Literature cited 1: None
Literature cited 2: None
ID: 55799
Title: Flood detection and mapping of the Thailand Central Plain using RADARSAT and MODIS under a sensor web environment
Author: Kridsakron Auynirundronkool, Nengcheng Chen, Caihua Peng, ChaoYang, Jianya Gong
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: Sensor web, flood detection, web processing service, Sensor Observation Service, RADARSAT, MODIS
Abstract: Flooding in general is insignificant event worldwide and also in Thailand. The Central plain, the Northern plain and the northeast of Thailand are frequently flooded areas, caused by yearly monsoons. The Thai government has extra expenditure to provide disaster relief and for the restoration of flood affected structures, persons, livestock, etc. Current flood detection in real time or near time has become a challenge in the flood emergency response. In this paper, an automatic instant time flood detection approach consisting of a data retrieval service, flood sensor observation service (SOS), flood detection web processing service (WPS) under a sensor web environment, is presented to generate dynamically real-time flood maps. A scenario of a RADARSAT and MODIS sensor web data service for flood detection cover of the Thailand Central plain is used to test the feasibility of the proposed framework. MODIS data are used to overview the wide area, while RADARSAT data are used to classify the flood area. The proposed framework using the transactional web coverage service (WCS-T) for instant flood detection processes dynamic real-time remote sensing observations and generates instant flood maps. The results show that the proposed approach is feasible for automatic instant flood detection.
Location: 241
Literature cited 1: None
Literature cited 2: None
ID: 55798
Title: Multi-scale remote sensing sagebrush characterization with regression trees over Wyoming, USA: Laying a foundation for monitoring
Author: Collin G Homer, Cameron L Aldridge, Debra K Meyer, Spencer J Schell
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: Sagebrush, Wyoming, Regression tree classification, Rangeland remote sensing, Monitoring
Abstract: Sagebrush ecosystems in North America have experienced extensive degradation since European settlement. Further degradation continues from exotic invasive plants, altered fire frequency, intensive grazing practices, oil and gas development, and climate change- adding urgency to the need for ecosystem - wide understanding. Remote sensing is often identified as a key information source to facilitate ecosystem-wide characterization, monitoring, and analysis; however, approaches, that characterize sagebrush with sufficient and accurate local datail across large enough areaas to support this paradigm are unavilable.We describe the development of a new remote sensing sagebrush characterization approach for the state of Wyoming, USA. This approach integrates 2.4 m QuickBird, 30 m Landsat TM, and 56 m AWiFS imagery into the characterization of four primary continuous field components including percent bare ground, percent herbaceous cover, percent litter, and percent shrub, and four secondary components including percent sagebrush (Artemisia spp) percent big sagebrush (Artemisia tridentata), percent Wyoming sagebrush (Artemisia tridentata Wyomingensis), and shrub height using a regression tree. According to an independent accuracy assessment, primary component root mean square error (RMSE) values ranged from 4.90 to 10.16 for 2.4 m QuickBird, 6.01 to 15.54 for 30 m Landsate, and 6.97 to 16.14 for 56 m AWiFS. Shrub and herbaceous components outperformed the current data standard called LANDFIRE, with a shrub RMSE value of 6.04 versus 12.64 and a herbaceous component RMSE value of 12.89 versus 14.63. This approach offers new advancements in sagebrush characterization from remote sensing and provides a foundation to quantitatively monitor these components into the future.
Location: 241
Literature cited 1: None
Literature cited 2: None
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