排序方式： 共有20条查询结果，搜索用时 265 毫秒
1.
2.
Permeshwar S. Chauhan Mahesh C. Porwal Lalit Sharma Jay Devs.negi 《Journal of the Indian Society of Remote Sensing》2003,31(3):211-218
The review of study site have revealed the change in vegetation cover of Sal Dense to Sal Medium and Sal Open in 6 forest
Mosaics owing to biotic and abiotic conditions prevailing in the specific areas. Analysis carried out using thematic map derived
from aerial photograph of 1976 and satellite data of IRS 1C LISS III False Colour Composite (FCC) of March 1999 revealed the
cause for change in forest density classes. Deforestation, encroachment and agriculture have been identified as the underlying
causes, which have affected some specific locations to a marked extent. There has been a progressive and remarkable change
among vegetation classes from 1976 to 1999. It is evident from forest type and density map that Sal density has significantly
reduced from Sal Dense 65.61 % in 1976 to Sal Dense 11.12% in the year 1999 followed by Sal Open 11.18 % and Sal Medium 18.24
%. The overall change has been estimated to be 42.11% of the total forested area. 相似文献
3.
Satellite Remote Sensing data has been used for vegetation mapping, initial stratification, distribution of sample plots and for calculating the area under different vegetation types. Primary and secondary analyses of vegetation has been done using phytosociological ground data collected from sample piots to assess the ecological importance of different species. Interrelationships among different communities have been evaluated through various available indices. The spatial distribution and vegetation analysis indicate that commercial extraction of natural forests of Andaman has set in retrogression. The evergreen forests subjected to shorter rotation of commercial exploitation are being invaded with seral deciduous species. The study highlights the status of forests (spatial and community) and stresses the need to conserve germplasm present in the natural evergreen forests. 相似文献
4.
Knowledge-Driven and Data-Driven Fuzzy Models for Predictive Mineral Potential Mapping 总被引：5，自引：0，他引：5
In this paper, we describe new fuzzy models for predictive mineral potential mapping: (1) a knowledge-driven fuzzy model that uses a logistic membership function for deriving fuzzy membership values of input evidential maps and (2) a data-driven model, which uses a piecewise linear function based on quantified spatial associations between a set of evidential evidence features and a set of known mineral deposits for deriving fuzzy membership values of input evidential maps. We also describe a graphical defuzzification procedure for the interpretation of output fuzzy favorability maps. The models are demonstrated for mapping base metal deposit potential in an area in the south-central part of the Aravalli metallogenic province in the state of Rajasthan, western India. The data-driven and knowledge-driven models described in this paper predict potentially mineralized zones, which occupy less than 10% of the study area and contain at least 83% of the model and validation base metal deposits. A cross-validation of the favorability map derived from using one of the models with the favorability map derived from using the other model indicates a remarkable similarity in their results. Both models therefore are useful for predicting favorable zones to guide further exploration work. 相似文献
5.
Jian Bai Alok Porwal Craig Hart Arianne Ford Le Yu 《Journal of Geochemical Exploration》2010,104(1-2):1-11
Most conventional statistical methods aiming at defining geochemical concentration thresholds for separating anomalies from background have limited effectiveness in areas with complex geological settings. This paper uses multifractal analysis to combine the characteristics of geochemical frequency distribution and spatial dispersion in order to map geochemical singularities instead of using conventional statistically derived concentration thresholds. The model, termed radius–areal Productivity (r–P) model, employs a stable measure and a scale-increasing method to estimate geochemical singularities spatially on geochemical landscape for delineating potential anomalies. The model is applied to geochemical data of regional stream sediments from the Funin Sheet, Yunnan, China. 相似文献
6.
Jessica P Karia M C Porwal P S Roy G Sandhya 《Journal of the Indian Society of Remote Sensing》2001,29(3):129-135
Studies on spatial distribution of the different forests during 1970 to 1999 using integrated remote sensing and GIS techniques reveals that area under forests in the Kalarani Round, is progressively reducing with the time. In 1970 forest area was found to be 22.75 sq km. in 1989 it was 15.34 sq km and in 1999 it was only 12.93 sq km. Thus. there is considerable loss in the tree cover from 1989 to 1999. Jhanpa and Kalarani R.F. are the example of this. Ground surveys indicate that the majority of the loss is caused by heavy grazing pressure there by decreasing regeneration of vegetation. If suitable measures are not taken. whole area may be converted into wasteland in due course of time. Another reason for the forest loss, could be land encroachments by villagers for their overwhelming needs. Karali R.F. is not much disturbed apart from the plantation raised in the foothills. Outside the reserve forest boundary, the change was observed due to construction of Narmada Sagar canal. 相似文献
7.
In the present study, forest type classification using Landsat TM False Colour Composite (FCC) bands 2, 3, 4 has been evaluated for mapping highly heterogeneous forest environment of Western Ghats (Kerala). Visual interpretation of Landsat TM FCC has been carried out to identify bioclimatic vegetation types. For accuracy estimation maps prepared from 1∶15,000 scale black-and-white aerial photographs have been used as ground check data. For comparison aerial photomap classes have been aggregated to match with Landsat-TM-derived map. The classification accuracy of ten major bioclimatic and landcover types was estimated using systematic sampling procedure. The overall classification accuracy of the forest types for the study area was 88.33%. 相似文献
8.
This paper describes a hybrid fuzzy weights-of-evidence (WofE) model for mineral potential mapping that generates fuzzy predictor
patterns based on (a) knowledge-based fuzzy membership values and (b) data-based conditional probabilities. The fuzzy membership
values are calculated using a knowledge-driven logistic membership function, which provides a framework for treating systemic
uncertainty and also facilitates the use of multiclass predictor maps in the modeling procedure. The fuzzy predictor patterns
are combined using Bayes’ rule in a log-linear form (under an assumption of conditional independence) to update the prior
probability of target deposit-type occurrence in every unique combination of predictor patterns. The hybrid fuzzy WofE model
is applied to a regional-scale mapping of base-metal deposit potential in the south-central part of the Aravalli metallogenic
province (western India). The output map of fuzzy posterior probabilities of base-metal deposit occurrence is classified subsequently
to delineate zones with high-favorability, moderate favorability, and low-favorability for occurrence of base-metal deposits.
An analysis of the favorability map indicates (a) significant improvement of probability of base-metal deposit occurrence
in the high-favorability and moderate-favorability zones and (b) significant deterioration of probability of base-metal deposit
occurrence in the low-favorability zones. The results demonstrate usefulness of the hybrid fuzzy WofE model in representation
and in integration of evidential features to map relative potential for mineral deposit occurrence. 相似文献
9.
In the present study authors have attempted to prepare a forest composition cover type map using landsat Thematic Mapper (T.M.) False colour Composite (F.C.C.) on 1:3n250,000 scale synthesized by combining band 2, 3 & 4 pertaining to study area. Landsat T.M.F.C.C. have been visually interpreted for delineation of forest cover type identified on the basis of tone/colour, texture, pattern & phenology and correlated with geographical location for drawing the final inferences. Limited field checks were done and types identified were correlated at some conspicuous identifiable location on image and ground and the informations were extrapolated for the whole study area. The forested area have been stratified into broad forest composition types as per Champion & Seth’s classification scheme. Temperate forest consisting of Oak forest and Deodar/mixed conifers could easily be separated due to sharp tonal contrast, however sub-tropical Chirpine and Northern tropical miscellaneous forest could be identified after having much correlation with the geographical location. The broad composition types were further classified into two density classes and the area under each category have been worked out using dot grid. 相似文献
10.
Probabilistic Fuzzy Logic Modeling: Quantifying Uncertainty of Mineral Prospectivity Models Using Monte Carlo Simulations 总被引：2，自引：0，他引：2
Significant uncertainties are associated with the definition of both the exploration targeting criteria and computational algorithms used to generate mineral prospectivity maps. In prospectivity modeling, the input and computational uncertainties are generally made implicit, by making a series of best-guess or best-fit decisions, on the basis of incomplete and imprecise information. The individual uncertainties are then compounded and propagated into the final prospectivity map as an implicit combined uncertainty which is impossible to directly analyze and use for decision making. This paper proposes a new approach to explicitly define uncertainties of individual targeting criteria and propagate them through a computational algorithm to evaluate the combined uncertainty of a prospectivity map. Applied to fuzzy logic prospectivity models, this approach involves replacing point estimates of fuzzy membership values by statistical distributions deemed representative of likely variability of the corresponding fuzzy membership values. Uncertainty is then propagated through a fuzzy logic inference system by applying Monte Carlo simulations. A final prospectivity map is represented by a grid of statistical distributions of fuzzy prospectivity. Such modeling of uncertainty in prospectivity analyses allows better definition of exploration target quality, as understanding of uncertainty is consistently captured, propagated and visualized in a transparent manner. The explicit uncertainty information of prospectivity maps can support further risk analysis and decision making. The proposed probabilistic fuzzy logic approach can be used in any area of geosciences to model uncertainty of complex fuzzy systems. 相似文献