首页 | 本学科首页   官方微博 | 高级检索  
相似文献
 共查询到20条相似文献，搜索用时 31 毫秒
1.
We studied changes in area and species composition of six indigenous forest fragments in the Taita Hills, Kenya using 1955 and 1995 aerial photography with 2004 airborne digital camera mosaics. The study area is part of Eastern Arc Mountains, a global biodiversity hot spot that boasts an outstanding diversity of flora and fauna and a high level of endemism. While a total of 260 ha (50%) of indigenous tropical cloud forest was lost to agriculture and bushland between 1955 and 2004, large-scale planting of exotic pines, eucalyptus, grevillea, black wattle and cypress on barren land during the same period resulted in a balanced total forest area. In the Taita Hills, like in other Afrotropical forests, indigenous forest loss may adversely affect ecosystem services.  相似文献   

2.
Abstract

In this study, we introduced novel hybrid of evidence believe function (EBF) with logistic regression (EBF-LR) and logistic model tree (EBF-LMT) for landslide susceptibility modelling. Fourteen conditioning factors were selected, including slope aspect, elevation, slope angle, profile curvature, plan curvature, topographic wetness index (TWI), stream sediment transport index (STI), stream power index (SPI), distance to rivers, distance to faults, distance to roads, lithology, normalized difference vegetation index (NDVI), and land use. The importance of factors was assessed using correlation attribute evaluation method. Finally, the performance of three models was evaluated using the area under the curve (AUC). The validation process indicated that the EBF-LMT model acquired the highest AUC for the training (84.7%) and validation (76.5%) datasets, followed by EBF-LR and EBF models. Our result also confirmed that combination of a decision tree-logistic regression-based algorithm with a bivariate statistical model lead to enhance the prediction power of individual landslide models.  相似文献   

3.
The main aim of present study is to compare three GIS-based models, namely Dempster–Shafer (DS), logistic regression (LR) and artificial neural network (ANN) models for landslide susceptibility mapping in the Shangzhou District of Shangluo City, Shaanxi Province, China. At First, landslide locations were identified by aerial photographs and supported by field surveys, and a total of 145 landslide locations were mapped in the study area. Subsequently, the landslide inventory was randomly divided into two parts (70/30) using Hawths Tools in ArcGIS 10.0 for training and validation purposes, respectively. In the present study, 14 landslide conditioning factors such as altitude, slope angle, slope aspect, topographic wetness index, sediment transport index, stream power index, plan curvature, profile curvature, lithology, rainfall, distance to rivers, distance to roads, distance to faults and normalized different vegetation index were used to detect the most susceptible areas. In the next step, landslide susceptible areas were mapped using the DS, LR and ANN models based on landslide conditioning factors. Finally, the accuracies of the landslide susceptibility maps produced from the three models were verified using the area under the curve (AUC). The validation results showed that the landslide susceptibility map generated by the ANN model has the highest training accuracy (73.19%), followed by the LR model (71.37%), and the DS model (66.42%). Similarly, the AUC plot for prediction accuracy presents that ANN model has the highest accuracy (69.62%), followed by the LR model (68.94%), and the DS model (61.39%). According to the validation results of the AUC curves, the map produced by these models exhibits the satisfactory properties.  相似文献   

4.
This study evaluates and compares landslide susceptibility maps of the Baxie River basin, Gansu Province, China, using three models: evidential belief function (EBF), certainty factor (CF) and frequency ratio (FR). First, a landslide inventory map is constructed from satellite image interpretation and extensive field data. Second, the study area is partitioned into 17,142 slope units, and modelled using nine landslide influence parameters: elevation, slope angle, slope aspect, relief amplitude, cutting depth, gully density, lithology, normalized difference vegetation index and distance to roads. Finally, landslide susceptibility maps are presented based on EBF, CF and FR models and validated using area under curve (AUC) analysis. The success rates of the EBF, CF and FR models are 0.8038, 0.7924 and 0.8088, respectively, while the prediction rates of the three models are 0.8056, 0.7922 and 0.7989, respectively. The result of this study can be reliably used in land use management and planning.  相似文献   

5.
Temporal changes in the normalized difference vegetation index (NDVI) have been widely used in vegetation mapping due to the usefulness of NDVI data in distinguishing characteristic seasonal differences in the phenology of greenness of vegetation cover. Research has also shown that NDVI provides potential to derive meaningful metrics that describe ecosystem functions. In this paper, we have applied both unsupervised “k-means” classification and supervised minimum distance classification as derived from temporal changes in NDVI measured in 1997 along the North Eastern China Transect (NECT), and we have also utilized the same two classification methods together with NDVI-derived metrics, namely maximum NDVI, mean NDVI, NDVI amplitude, NDVI threshold, total length of growing season, fraction of growing season during greenup, rate of greenup, rate of senescence, integrated NDVI during the growing season, and integrated NDVI during greenup/integrated NDVI during senescence to map vegetation. The main objectives of this study are: (1) to test the relative performance of NDVI temporal profile metrics and NDVI-derived metrics for vegetation cover discrimination in NECT; (2) to test the relative performance of unsupervised (k-means) and supervised (minimum distance) methods for vegetation mapping; (3) to test the accuracy of the IGBP-DIS released land cover map for NECT; (4) to provide an up-to-date vegetation map for NECT. The results suggest that the classifications based on NDVI temporal profile metrics have higher accuracies than those based on any other metrics, such as NDVI-derived metrics, or all (NDVI temporal profile metrics + NDVI-derived metrics), or 15 metrics (NDVI temporal profile + Rate of greenup, Rate of senescence, and Integrated NDVI in greenup/integrated NDVI in senescence) for both methods. And among them, unsupervised k-means classification had the highest overall accuracy of 52% and Kappa coefficient of 0.2057. Both unsupervised (k-means) and supervised (minimum distance) methods achieved similar accuracies for the same metrics. The accuracy of IGBP-DIS released land cover map had an overall accuracy of 37% and a Kappa coefficient is 0.1441, and can improve to 46% by decomposing the crop/natural vegetation mosaic to cropland and other natural vegetation types. The results support using unsupervised k-means classification based on NDVI temporal profile metrics to provide an up-to-date vegetation cover classification. However, new effort is necessary in the future in order to improve the overall performance on this issue.  相似文献   

6.
The landslide hazard occurred in Taibai County has the characteristics of the typical landslides in mountain hinterland. The slopes mainly consist of residual sediments and locate along the highway. Most of them are in the less stable state and in high risk during rainfall in flood season especially. The main purpose of this paper is to produce landslide susceptibility maps for Taibai County (China). In the first stage, a landslide inventory map and the input layers of the landslide conditioning factors were prepared in the geographic information system supported by field investigations and remote sensing data. The landslides conditioning factors considered for the study area were slope angle, altitude, slope aspect, plan curvature, profile curvature, distance to faults, distance to rivers, distance to roads, normalized difference vegetation index, lithological unit, rainfall and land use. Subsequently, the thematic data layers of conditioning factors were integrated by frequency ratio (FR), weights of evidence (WOE) and evidential belief function (EBF) models. As a result, landslide susceptibility maps were obtained. In order to compare the predictive ability of these three models, a validation procedure was conducted. The curves of cumulative area percentage of ordered index values vs. the cumulative percentage of landslide numbers were plotted and the values of area under the curve (AUC) were calculated. The predictive ability was characterized by the AUC values and it indicates that all these models considered have relatively similar and high accuracies. The success rate of FR, WOE and EBF models was 0.9161, 0.9132 and 0.9129, while the prediction rate of the three models was 0.9061, 0.9052 and 0.9007, respectively. Considering the accuracy and simplicity comprehensively, the FR model is the optimum method. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose.  相似文献   

7.
This study empirically compared noise reduction techniques for the normalized difference vegetation index (NDVI) time-series based on a new absolute measure using a time-series of 16-day composite NDVI images extracted from the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) products covering the Poyang Lake area in China. We proposed an approach to accurately extract representative NDVI temporal profiles for the 12 land cover cluster types by clustering profiles, selecting optimal number of clusters, merging and labeling clusters, and selecting the representative NDVI profiles. The geometric average of the mean average distance between the reconstructed profile and the raw profiles, and the mean average distance between the reconstructed profile and the upper envelope (Dg(nr, c)) was selected as the most appropriate measure substitutive to RMSE for the evaluation of the noise reduction effects, when the ‘true’ profiles were not available. The running median, mean value, maximum operation, end point processing, and Hanning smoothing (RMMEH) filter and iterative Savitzky–Golay filter were the two most appropriate noise reduction techniques for the NDVI temporal profiles of the study area in the evaluation of noise reduction effects by the seven techniques. The robust framework using the proposed approach for the accurate extraction of representative NDVI temporal profiles and (Dg(nr, c)) in this study, is applicable in the evaluation of noise reduction effects using different techniques and in other study areas.  相似文献   

8.
The common spectra wavebands and vegetation indices (VI) were identified for indicating leaf nitrogen accumulation (LNA), and the quantitative relationships of LNA to canopy reflectance spectra were determined in both wheat (Triticum aestivum L.) and rice (Oryza sativa L.). The 810 and 870 nm are two common spectral wavebands indicating LNA in both wheat and rice. Among all ratio vegetation indices (RVI), difference vegetation indices (DVI) and normalized difference vegetation indices (NDVI) of 16 wavebands from the MSR16 radiometer, RVI (870, 660) and RVI (810, 660) were most highly correlated to LNA in both wheat and rice. In addition, the relations between VIs and LNA gave better results than relations between single wavebands and LNA in both wheat and rice. Thus LNA in both wheat and rice could be indicated with common VIs, but separate regression equations are better for LNA monitoring.  相似文献   

9.
Land surface phenology has been widely retrieved although no consensus exists on the optimal satellite dataset and the method to extract phenology metrics. This study is the first comprehensive comparison of vegetation variables and methods to retrieve land surface phenology for 1999–2017 time series of Copernicus Global Land products derived from SPOT-VEGETATION and PROBA-V data. We investigated the sensitivity of phenology to (I) the input vegetation variable: normalized difference vegetation index (NDVI), leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and fraction of vegetation cover (FCOVER); (II) the smoothing and gap filling method for deriving seasonal trajectories; and (III) the method to extract phenological metrics: thresholds based on a percentile of the annual amplitude of the vegetation variable, autoregressive moving averages, logistic function fitting, and first derivative methods. We validated the derived satellite phenological metrics (start of the season (SoS) and end of the season (EoS)) using available ground observations of Betula pendula, B. alleghaniensis, Acer rubrum, Fagus grandifolia, and Quercus rubra in Europe (Pan-European PEP725 network) and the USA (National Phenology Network, USA-NPN). The threshold-based method applied to the smoothed and gap-filled LAI V2 time series agreed best with the ground phenology, with root mean square errors of ˜10 d and ˜25 d for the timing of SoS and EoS respectively. This research is expected to contribute for the operational retrieval of land surface phenology within the Copernicus Global Land Service.  相似文献   

10.
SPOT satellites have been imaging Earth's surface since SPOT 1 was launched in 1986. It is argued that absolute atmospheric correction is a prerequisite for quantitative remote sensing. Areas where land cover changes are occurring rapidly are also often areas most lacking in situ data which would allow full use of radiative transfer models for reflectance factor retrieval (RFR). Consequently, this study details the proposed historical empirical line method (HELM) for RFR from multi-temporal SPOT imagery. HELM is designed for use in landscape level studies in circumstances where no detailed overpass concurrent atmospheric or meteorological data are available, but where there is field access to the research site(s) and a goniometer or spectrometer is available. SPOT data are complicated by the ±27° off-nadir cross track viewing. Calibration to nadir only surface reflectance factor (ρs) is denoted as HELM-1, whilst calibration to ρs modelling imagery illumination and view geometries is termed HELM-2. Comparisons of field measured ρs with those derived from HELM corrected SPOT imagery, covering Helsinki, Finland, and Taita Hills, Kenya, indicated HELM-1 RFR absolute accuracy was ±0.02ρs in the visible and near infrared (VIS/NIR) bands and ±0.03ρs in the shortwave infrared (SWIR), whilst HELM-2 performance was ±0.03ρs in the VIS/NIR and ±0.04ρs in the SWIR. This represented band specific relative errors of 10–15%. HELM-1 and HELM-2 RFR were significantly better than at-satellite reflectance (ρSAT), indicating HELM was effective in reducing atmospheric effects. However, neither HELM approach reduced variability in mean ρs between multi-temporal images, compared to ρSAT. HELM-1 calibration error is dependent on surface characteristics and scene illumination and view geometry. Based on multiangular ρs measurements of vegetation-free ground targets, calibration error was negligible in the forward scattering direction, even at maximum off-nadir view. However, error exceeds 0.02ρs where off-nadir viewing was ≥20° in the backscattering direction within ±55° azimuth of the principal plane. Overall, HELM-1 results were commensurate with an identified VIS/NIR 0.02ρs accuracy benchmark. HELM thus increases applicability of SPOT data to quantitative remote sensing studies.  相似文献   

11.
Vegetation maps are essential tools for planning and evaluation in nature conservation. To a large extend, management objectives can be defined in terms of vegetation attributes. Vegetation maps are frequently used in conservation planning and evaluation. Nowadays, remote sensing is one of the most important sources of vegetation cover classifications at different scales. In this paper, the usefulness of moderate resolution images for invasive species Cirsium arvense and Stachys byzanthina mapping at local scale is evaluated. A fuzzy classification approach is tested for improved discrimination of invasive species. The study was carried out in Vazroud rangelands located in the Mazandaran province of Iran. In this study among the analysed Z-score for uncertainty map creation is found that 0.9, 1.0 for TM image and 1.1 for IRS image in normalized are the best based on known attributes of the study area.  相似文献   

12.
On the night of 27th Sept 2012, a hydrogen fluoride (HF) storage tank exploded in the industrial area of Gumi, Korea. In this study, an attempt was made to detect the damaged vegetation using a hyperspectral image. Assuming that HF was the most significant stressor of vegetation near the blast site, our results indicated that the difference in reflectance values between the 786 and 801 nm wavelengths became negative (?) and fluctuated from ?5 to ?160 in the affected vegetation. On the other hand, positive (+) values were found in the non-affected vegetation. The combination of normalized difference vegetation index (NDVI) and simple ratio (SR) was used [named combined vegetation index (CVI) in this study] to assess the accuracy of these measurements. Among 150,965 pixels that were defined as damaged vegetation, about 2.8 % were analyzed incorrectly in this study. As a result, factors such as wind direction, distance from the accident spot, elevation, and aspect were shown to affect damage distribution.  相似文献   

13.
Soil erosion rates in alpine regions are related to high spatial variability complicating assessment of risk and damages. A crucial parameter triggering soil erosion that can be derived from satellite imagery is fractional vegetation cover (FVC). The objective of this study is to assess the applicability of normalized differenced vegetation index (NDVI), linear spectral unmixing (LSU) and mixture tuned matched filtering (MTMF) in estimating abundance of vegetation cover in alpine terrain. To account for the small scale heterogeneity of the alpine landscape we used high resolved multispectral QuickBird imagery (pixel resolution = 2.4 m) of a site in the Urseren Valley, Central Swiss Alps (67 km2). A supervised land-cover classification was applied (total accuracy 93.3%) prior to the analysis in order to stratify the image. The regression between ground truth FVC assessment and NDVI as well as MTMF-derived vegetation abundance was significant (r2 = 0.64, r2 = 0.71, respectively). Best results were achieved for LSU (r2 = 0.85). For both spectral unmixing approaches failed to estimate bare soil abundance (r2 = 0.39 for LSU, r2 = 0.28 for MTMF) due to the high spectral variability of bare soil at the study site and the low spectral resolution of the QuickBird imagery. The LSU-derived FVC map successfully identified erosion features (e.g. landslides) and areas prone to soil erosion. FVC represents an important but often neglected parameter for soil erosion risk assessment in alpine grasslands.  相似文献   

14.
Quantitative estimations of the fractional cover of photosynthetic vegetation (fPV), non-photosynthetic vegetation (fNPV) and bare soil (fBS) are critical for soil wind erosion, desertification, grassland grazing, grassland fire, and grassland carbon storage studies. At present, regional and large-scale fPV, fNPV and fBS estimations have been carried out in many areas. However, few studies have used moderate resolution imaging spectroradiometer (MODIS) data to perform large-scale, long-term fPV, fNPV and fBS estimations in the Xilingol grassland of China. The objective of this study was to quantitatively estimate the time series of fPV, fNPV and fBS in the typical grassland region of Xilingol from MODIS image data. Field measurement spectral and coverage data from May and September 2017 were combined with the 8-day composite product (MOD09A1) acquired during 2017. We established an empirical linear model of different non-photosynthetic vegetation indices (NPVIs) and fNPV based on the sample scale. The linear correlation between the dead fuel index (DFI) and fNPV was best (R2 = 0.60, RMSE = 0.15). A normalized difference vegetation index (NDVI)-DFI model based on MODIS data was proposed to accurately estimate the fPV, fNPV and fBS (estimation accuracies of 44%, 71%, and 74%, respectively) in the typical grasslands of Xilingol in China. The fPV, fNPV and fBS values for the typical grassland time series estimated by the NDVI-DFI model were consistent with the phenological characteristics of the grassland vegetation. The results show that the application of the NDVI-DFI model to the Xilingol grassland is reasonable and appropriate, and it is of great significance to the monitoring of soil wind erosion and fires in grasslands.  相似文献   

15.
The aim of this study is to estimate leaf area index (LAI) in different type of plants using vegetation indices (VIs) and neural network algorithms retrieved from MODIS data. Four VI were calculated, and neural networks were built up based on MODIS surface reflectance products. Among the tested VIs, normalized difference vegetation index (NDVI) and chlorophyll index (CI) appeared to be the best candidate indices in estimating LAI across sites with different vegetation types. The models having the highest accuracy were CI for grassland and deciduous broad leaf forest with determination coefficients (R-square above 0.70, and NDVI for crop R-square?=?0.78). Neural network showed better results than VI methods except in grassland sites. The added VI information showed no significant improvement of model accuracy for the neural networks in most sites.  相似文献   

16.
国产高分卫星分辨率的不断提高,使其可以从几何形态、纹理结构及光谱信息等不同侧面实现对城市地表要素的精细描述。与面向对象分类技术相比,深度学习技术的快速发展,使得城市建筑物提取的精度不断提高。然而,由于道路两旁高大建筑物及树木的遮挡,城市道路的提取精度依然有限。本文在利用卷积神经网络提取建筑物的基础上,利用OSM面状道路数据及城市边界数据,结合植被指数和水体指数,借助空间图层叠加,使得城市建筑物、道路、植被和水体提取总体精度优于90%,为国产高分影像辅助城市精细化管理和应用提供了有效解决方案。  相似文献   

17.
The Arctic is experiencing disproportionate warming relative to the global average, and the Arctic ecosystems are as a result undergoing considerable changes. Continued monitoring of ecosystem productivity and phenology across temporal and spatial scales is a central part of assessing the magnitude of these changes. This study investigates the ability to use automatic digital camera images (DCIs) as proxy data for gross primary production (GPP) in a complex low Arctic wetland site. Vegetation greenness computed from DCIs was found to correlate significantly (R2 = 0.62, p < 0.001) with a normalized difference vegetation index (NDVI) product derived from the WorldView-2 satellite. An object-based classification based on a bi-temporal image composite was used to classify the study area into heath, copse, fen, and bedrock. Temporal evolution of vegetation greenness was evaluated and modeled with double sigmoid functions for each plant community. GPP at light saturation modeled from eddy covariance (EC) flux measurements were found to correlate significantly with vegetation greenness for all plant communities in the studied year (i.e., 2010), and the highest correlation was found between modeled fen greenness and GPP (R2 = 0.85, p < 0.001). Finally, greenness computed within modeled EC footprints were used to evaluate the influence of individual plant communities on the flux measurements. The study concludes that digital cameras may be used as a cost-effective proxy for potential GPP in remote Arctic regions.  相似文献   

18.
Karbi Anglong and North Cachar Hills districts of Assam are endowed with rich and diverse vegetation resources. Increased human pressure due to shifting cultivation and raw material extraction for industrial purposes are heavily altering the forested landscape. The present study deals with mapping of forest types in the two districts using LANDSAT-MSS digital data. The maps thus generated provide spatial distribution of bioclimatic vegetation types. Supervised maximum likelihood classification has been performed using training sets collected during field work. The spectral behaviour of vegetation types have been studied for optimising classification scheme. The classification accuracy of classes mapped has been calculated.  相似文献   

19.
In this study, digital images collected at a study site in the Canadian High Arctic were processed and classified to examine the spatial-temporal patterns of percent vegetation cover (PVC). To obtain the PVC of different plant functional groups (i.e., forbs, graminoids/sedges and mosses), field near infrared-green-blue (NGB) digital images were classified using an object-based image analysis (OBIA) approach. The PVC analyses comparing different vegetation types confirmed: (i) the polar semi-desert exhibited the lowest PVC with a large proportion of bare soil/rock cover; (ii) the mesic tundra cover consisted of approximately 60% mosses; and (iii) the wet sedge consisted almost exclusively of graminoids and sedges. As expected, the PVC and green normalized difference vegetation index (GNDVI; (RNIR − RGreen)/(RNIR + RGreen)), derived from field NGB digital images, increased during the summer growing season for each vegetation type: i.e., ∼5% (0.01) for polar semi-desert; ∼10% (0.04) for mesic tundra; and ∼12% (0.03) for wet sedge respectively. PVC derived from field images was found to be strongly correlated with WorldView-2 derived normalized difference spectral indices (NDSI; (Rx − Ry)/(Rx + Ry)), where Rx is the reflectance of the red edge (724.1 nm) or near infrared (832.9 nm and 949.3 nm) bands; Ry is the reflectance of the yellow (607.7 nm) or red (658.8 nm) bands with R2’s ranging from 0.74 to 0.81. NDSIs that incorporated the yellow band (607.7 nm) performed slightly better than the NDSIs without, indicating that this band may be more useful for investigating Arctic vegetation that often includes large proportions of senescent vegetation throughout the growing season.  相似文献   

20.
An assessment of gully erosion along road drainage-release sites is critical for understanding the contribution of roads to soil loss and for informed land management practices. Considering that road-related gully erosion has traditionally been measured using field methods that are expensive, tedious and limited spatially as well as temporally, it is important to identify affordable, timely and robust methods that can be used to effectively map and estimate the volume of gullies along the road networks. In this study, gullies along major roads were identified from remotely sensed data sets and their volumes were estimated in a Geographic Information Systems environment. Also, the biophysical and climatic factors such as vegetation cover, the road contributing surface area, the gradient of the discharge hillslope and rainfall were derived from remotely sensed data sets using Geographic Information Systems techniques to find out whether they could explain the morphology of gullies that existed in this area. The results of this study indicate that hillslope gradient (R2?=?0.69, α = 0.00) and road contributing surface area (R2?=?0.63, α = 0.00) have a strong influence on the volume of gullies along the major roads in the south-eastern region of South Africa, as might have been expected. However, other factors such as vegetation cover (R2 = 0.52, α = 0.00) and rainfall (R2 = 0.41 and α = 0.58) have a moderately weaker influence on the overall volume of gullies. Overall, the findings of this study highlight the importance of using remote sensing and Geographic Information Systems technologies in investigating gully erosion occurrence along major roads where detailed field work remains a challenge.  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号