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1.
Although wetlands play a key role in controlling flooding and nonpoint source pollution, sequestering carbon and providing an abundance of ecological services, the inventory and characterization of wetland habitats are most often limited to small areas. This explains why the understanding of their ecological functioning is still insufficient for a reliable functional assessment on areas larger than a few hectares. While LiDAR data and multispectral Earth Observation (EO) images are often used separately to map wetland habitats, their combined use is currently being assessed for different habitat types. The aim of this study is to evaluate the combination of multispectral and multiseasonal imagery and LiDAR data to precisely map the distribution of wetland habitats. The image classification was performed combining an object-based approach and decision-tree modeling. Four multispectral images with high (SPOT-5) and very high spatial resolution (Quickbird, KOMPSAT-2, aerial photographs) were classified separately. Another classification was then applied integrating summer and winter multispectral image data and three layers derived from LiDAR data: vegetation height, microtopography and intensity return. The comparison of classification results shows that some habitats are better identified on the winter image and others on the summer image (overall accuracies = 58.5 and 57.6%). They also point out that classification accuracy is highly improved (overall accuracy = 86.5%) when combining LiDAR data and multispectral images. Moreover, this study highlights the advantage of integrating vegetation height, microtopography and intensity parameters in the classification process. This article demonstrates that information provided by the synergetic use of multispectral images and LiDAR data can help in wetland functional assessment 相似文献
2.
Invasive plants pose significant threats to biodiversity and ecosystem function globally, leading to costly monitoring and management effort. While remote sensing promises cost-effective, robust and repeatable monitoring tools to support intervention, it has been largely restricted to airborne platforms that have higher spatial and spectral resolutions, but which lack the coverage and versatility of satellite-based platforms. This study tests the ability of the WorldView-2 (WV2) eight-band satellite sensor for detecting the invasive shrub mesquite (Prosopis spp.) in the north-west Pilbara region of Australia. Detectability was challenged by the target taxa being largely defoliated by a leaf-tying biological control agent (Gelechiidae: Evippe sp. #1) and the presence of other shrubs and trees. Variable importance in the projection (VIP) scores identified bands offering greatest capacity for discrimination were those covering the near-infrared, red, and red-edge wavelengths. Wavelengths between 400 nm and 630 nm (coastal blue, blue, green, yellow) were not useful for species level discrimination in this case. Classification accuracy was tested on three band sets (simulated standard multispectral, all bands, and bands with VIP scores ≥1). Overall accuracies were comparable amongst all band-sets (Kappa = 0.71–0.77). However, mesquite omission rates were unacceptably high (21.3%) when using all eight bands relative to the simulated standard multispectral band-set (9.5%) and the band-set informed by VIP scores (11.9%). An incremental cover evaluation on the latter identified most omissions to be for objects <16 m2. Mesquite omissions reduced to 2.6% and overall accuracy significantly improved (Kappa = 0.88) when these objects were left out of the confusion matrix calculations. Very high mapping accuracy of objects >16 m2 allows application for mapping mesquite shrubs and coalesced stands, the former not previously possible, even with 3 m resolution hyperspectral imagery. WV2 imagery offers excellent portability potential for detecting other species where spectral/spatial resolution or coverage has been an impediment. New generation satellite sensors are removing barriers previously preventing widespread adoption of remote sensing technologies in natural resource management. 相似文献
3.
High resolution satellite systems enable efficient and detailed mapping of tree cover, with high potential to support both natural resource monitoring and ecological research. This study investigates the capability of multi-seasonal WorldView-2 imagery to map five dominant tree species at the individual tree crown level in a parkland landscape in central Burkina Faso. The Random Forest algorithm is used for object based tree species classification and for assessing the relative importance of WorldView-2 predictors. The classification accuracies from using wet season, dry season and multi-seasonal datasets are compared to gain insights about the optimal timing for image acquisition. The multi-seasonal dataset produced the most accurate classifications, with an overall accuracy (OA) of 83.4%. For classifications based on single date imagery, the dry season (OA = 78.4%) proved to be more suitable than the wet season (OA = 68.1%). The predictors that contributed most to the classification success were based on the red edge band and visible wavelengths, in particular green and yellow. It was therefore concluded that WorldView-2, with its unique band configuration, represents a suitable data source for tree species mapping in West African parklands. These results are particularly promising when considering the recently launched WorldView-3, which provides data both at higher spatial and spectral resolution, including shortwave infrared bands. 相似文献
4.
Within the last few decades mangrove forests worldwide have been experiencing high annual rates of loss and many of those that remain have undergone considerable degradation. To understand the condition of these forests, various optical remote sensing platforms have been used to map and monitor these wetlands, including the use of these data for biophysical parameter mapping. For many mangrove forests a reliable source of optical imagery is not possible given their location in quasi-permanent cloud cover or smoke covered regions. In such cases it is recommended that Synthetic Aperture Radar (SAR) be considered. The purpose of this investigation was to examine the relationships between various ALOS-PALSAR modes, acquired from eight images, and mangrove biophysical parameter data collected from a black mangrove (Avicennia germinans) dominated forest that has experienced considerable degradation. In total, structural data were collected from 61 plots representing the four common stand types found in this degraded forest of the Mexican Pacific: tall healthy mangrove (n = 17), dwarf healthy mangrove (n = 15), poor condition mangrove (n = 13), and predominantly dead mangrove (n = 16).Based on backscatter coefficients, significant negative correlation coefficients were observed between filtered single polarization ALOS PALSAR (6.25 m) HH backscatter and Leaf Area Index (LAI). When the dead stands were excluded (n = 45) the strength of these relationships increased. Moreover, significant negative correlation coefficients were observed with stand height, Basal Area (BA) and to a lesser degree with stem density and mean DBH. With the coarser spatial resolution dual-polarization and quad polarization data (12.5 m) only a few, and weaker, correlation coefficients were calculated between the mangrove parameters and the filtered HH backscatter. However, significant negative values were once again calculated for the HH when the 16 dead mangrove stands were removed from the sample. Conversely, strong positive significant correlation coefficients were calculated between the cross-polarization HV backscatter and LAI when the dead mangrove stands were considered. Although fewer in comparison to the HH correlations, a number of VV backscatter based relationships with mangrove parameters were observed from the quad polarization mode and, to a lesser extent, with the one single VV polarization data.In addition to backscatter coefficients, stepwise multiple regression models of the mangrove biophysical parameter data were developed based on texture parameters derived from the grey level co-occurrence matrix (GLCM) of the ALOS data. A similar pattern to the backscatter relationships was observed for models based on the single polarization unfiltered data, with fairly strong coefficients of determination calculated for LAI and stem height when the dead stands were excluded. In contrast, similar coefficients of determination with biophysical parameters were observed for the dual and quad polarization multiple regression models when the dead stands were both included and excluded from the analyses. An estimated mangrove LAI map of the study area, derived from a multiple regression model of the quad polarization texture parameters, showed comparable spatial patterns of degradation to a map derived from higher spatial resolution optical satellite data. 相似文献
5.
Object-based class modelling allows for mapping complex, hierarchical habitat systems. The riparian zone, including forests, represents such a complex ecosystem. Forests within riparian zones are biologically high productive and characterized by a rich biodiversity; thus considered of high community interest with an imperative to be protected and regularly monitored. Satellite earth observation (EO) provides tools for capturing the current state of forest habitats such as forest composition including intermixture of non-native tree species. Here we present a semi-automated object based image analysis (OBIA) approach for the mapping of riparian forests by applying class modelling of habitats based on the European Nature Information System (EUNIS) habitat classifications and the European Habitats Directive (HabDir) Annex 1. A very high resolution (VHR) WorldView-2 satellite image provided the required spatial and spectral details for a multi-scale image segmentation and rule-base composition to generate a six-level hierarchical representation of riparian forest habitats. Thereby habitats were hierarchically represented within an image object hierarchy as forest stands, stands of homogenous tree species and single trees represented by sunlit tree crowns. 522 EUNIS level 3 (EUNIS-3) habitat patches with a mean patch size (MPS) of 12,349.64 m2 were modelled from 938 forest stand patches (MPS = 6868.20 m2) and 43,742 tree stand patches (MPS = 140.79 m2). The delineation quality of the modelled EUNIS-3 habitats (focal level) was quantitatively assessed to an expert-based visual interpretation showing a mean deviation of 11.71%. 相似文献
6.
In order to monitor natural and anthropogenic disturbance effects to wetland ecosystems, it is necessary to employ both accurate and rapid mapping of wet graminoid/sedge communities. Thus, it is desirable to utilize automated classification algorithms so that the monitoring can be done regularly and in an efficient manner. This study developed a classification and accuracy assessment method for wetland mapping of at-risk plant communities in marl prairie and marsh areas of the Everglades National Park. Maximum likelihood (ML) and Support Vector Machine (SVM) classifiers were tested using 30.5 cm aerial imagery, the normalized difference vegetation index (NDVI), first and second order texture features and ancillary data. Additionally, appropriate window sizes for different texture features were estimated using semivariogram analysis. Findings show that the addition of NDVI and texture features increased classification accuracy from 66.2% using the ML classifier (spectral bands only) to 83.71% using the SVM classifier (spectral bands, NDVI and first order texture features). 相似文献
7.
This study evaluates the feasibility of hyperspectral and multispectral satellite imagery for categorical and quantitative mapping of salinity stress in sugarcane fields located in the southwest of Iran. For this purpose a Hyperion image acquired on September 2, 2010 and a Landsat7 ETM+ image acquired on September 7, 2010 were used as hyperspectral and multispectral satellite imagery. Field data including soil salinity in the sugarcane root zone was collected at 191 locations in 25 fields during September 2010. In the first section of the paper, based on the yield potential of sugarcane as influenced by different soil salinity levels provided by FAO, soil salinity was classified into three classes, low salinity (1.7–3.4 dS/m), moderate salinity (3.5–5.9 dS/m) and high salinity (6–9.5) by applying different classification methods including Support Vector Machine (SVM), Spectral Angle Mapper (SAM), Minimum Distance (MD) and Maximum Likelihood (ML) on Hyperion and Landsat images. In the second part of the paper the performance of nine vegetation indices (eight indices from literature and a new developed index in this study) extracted from Hyperion and Landsat data was evaluated for quantitative mapping of salinity stress. The experimental results indicated that for categorical classification of salinity stress, Landsat data resulted in a higher overall accuracy (OA) and Kappa coefficient (KC) than Hyperion, of which the MD classifier using all bands or PCA (1–5) as an input performed best with an overall accuracy and kappa coefficient of 84.84% and 0.77 respectively. Vice versa for the quantitative estimation of salinity stress, Hyperion outperformed Landsat. In this case, the salinity and water stress index (SWSI) has the best prediction of salinity stress with an R2 of 0.68 and RMSE of 1.15 dS/m for Hyperion followed by Landsat data with an R2 and RMSE of 0.56 and 1.75 dS/m respectively. It was concluded that categorical mapping of salinity stress is the best option for monitoring agricultural fields and for this purpose Landsat data are most suitable. 相似文献
8.
《ISPRS Journal of Photogrammetry and Remote Sensing》2007,61(5):325-340
In this paper, a methodology for individual tree-based species classification using high sampling density and small footprint lidar data is clarified, corrected and improved. For this purpose, a well-defined directed graph (digraph) is introduced and it plays a fundamental role in the approach. It is argued that there exists one and only one such unique digraph that describes all four pure events and resulting disjoint sets of laser points associated with a single tree in data from a two-return lidar system. However, the digraph is extendable so that it fits an n-return lidar system (n > 2) with higher logical resolution. Furthermore, a mathematical notation for different types of groupings of the laser points is defined, and a new terminology for various types of individual tree-based concepts defined by the digraph is proposed. A novel calibration technique for estimating individual tree heights is evaluated. The approach replaces the unreliable maximum single laser point height of each tree with a more reliable prediction based on shape characteristics of a marginal height distribution of the whole first-return point cloud of each tree. The result shows a reduction of the RMSE of the tree heights of about 20% (stddev = 1.1 m reduced to stddev = 0.92 m). The method improves the species classification accuracy markedly, but it could also be used for reducing the sampling density at the time of data acquisition. Using the calibrated tree heights, a scale-invariant rescaled space for the universal set of points for each tree is defined, in which all individual tree-based geometric measurements are conducted. With the corrected and improved classification methodology the total accuracy raises from 60% to 64% for classifying three leaf-off individual tree deciduous species (N = 200 each) in West Virginia, USA: oaks (Quercus spp.), red maple (Acer rubrum), and yellow poplar (Liriodendron tuliperifera). 相似文献
9.
Remote sensing has recently been used to map forage quality for rangeland management. However, the validity of the remotely sensed forage quality can best be assessed when it connects well with the animal unit. In this study we used the new WorldView-2 multispectral imagery to estimate and map forage quality (nitrogen concentration) as a step to explain GPS based cattle distribution in a rangeland of Southeastern Zimbabwe. Nitrogen concentration was successfully estimated and mapped (Rcv2 = 0.66, relative error = 0.13%) using partial least squares regression (PLSR). The integration of GPS based cattle distribution patterns with forage quality in a GIS showed that cattle locations significantly clustered in areas of high forage quality. The results of this study suggest that new multispectral data with unique band settings such as WorldView-2 improves the estimation and mapping of forage quality in rangelands at landscape level. In addition, our results indicate that remotely sensed forage quality can be used to explain herbivore distribution, particularly cattle grazing patterns in rangelands. 相似文献
10.
Inventories of mixed broad-leaved forests of Iran mainly rely on terrestrial measurements. Due to rapid changes and disturbances and great complexity of the silvicultural systems of these multilayer forests, frequent repetition of conventional ground-based plot surveys is often cost prohibitive. Airborne laser scanning (ALS) and multispectral data offer an alternative or supplement to conventional inventories in the Hyrcanian forests of Iran. In this study, the capability of a combination of ALS and UltraCam-D data to model stand volume, tree density, and basal area using random forest (RF) algorithm was evaluated. Systematic sampling was applied to collect field plot data on a 150 m × 200 m sampling grid within a 1100 ha study area located at 36°38′- 36°42′N and 54°24′–54°25′E. A total of 308 circular plots (0.1 ha) were measured for calculation of stand volume, tree density, and basal area per hectare. For each plot, a set of variables was extracted from both ALS and multispectral data. The RF algorithm was used for modeling of the biophysical properties using ALS and UltraCam-D data separately and combined. The results showed that combining the ALS data and UltraCam-D images provided a slight increase in prediction accuracy compared to separate modeling. The RMSE as percentage of the mean, the mean difference between observed and predicted values, and standard deviation of the differences using a combination of ALS data and UltraCam-D images in an independent validation at 0.1-ha plot level were 31.7%, 1.1%, and 84 m3 ha−1 for stand volume; 27.2%, 0.86%, and 6.5 m2 ha−1 for basal area, and 35.8%, −4.6%, and 77.9 n ha−1 for tree density, respectively. Based on the results, we conclude that fusion of ALS and UltraCam-D data may be useful for modeling of stand volume, basal area, and tree density and thus gain insights into structural characteristics in the complex Hyrcanian forests. 相似文献
11.
In this paper, we focused on the retrieval of the LAI in an alpine wetland located in western part of China in late August and early July 2011. A two-layer canopy reflectance model (ACRM) was used to establish the relationships between the LAI and the reflectance of near-infrared (NIR) and red (RED) wavebands. The reflectance data were derived from Landsat TM L1T product and the Terra and Aqua MODIS 16-day and 8-day composite reflectance products (MOD/MYD09) at 250 m resolution. Due to the lack of the information about some major input parameters for ACRM, which are sensitive to model outputs in the reflectance of NIR and RED wavebands, the inverse problem was ill-posed. To overcome this problem, a method of increasing the sensitivity of the LAI while reducing the influence of other model free parameters based on the study of free parameters’ sensitivity to the ACRM outputs and the region’s features was studied. The area of interest was divided into two parts using the approximately statistic normalized difference vegetation index (NDVI) value around 0.5. One part was sparse vegetation (0.1 < NDVI < 0.5), which is more sensitive to soil background effects and less sensitive to the canopy biophysical and biochemical variables. The other part was dense vegetation (0.5 ≤ NDVI < 1.0), which is less sensitive to soil background effects and more sensitive to plant canopies and leaf parameters. Then, the relationships of ρnir–LAI and ρred–LAI were established using a look-up table algorithm for the two parts. Furthermore, a regularization technique for fast pixel-wise retrieval was introduced to reduce the elements of LUT sets while maintaining a relatively high accuracy. The results were very promising compared to the field measured LAI values that the correlation (R2) of the measured LAI values and retrieved LAI values reached 0.95, and the root-mean-square deviation (RMSD) was 0.33 for late August, 2011, while the R2 reached 0.82 and RMSD was 0.25 for early July 2011. 相似文献
12.
LiDAR has been an effective technology for acquiring urban land cover data in recent decades. Previous studies indicate that geometric features have a strong impact on land cover classification. Here, we analyzed an urban LiDAR dataset to explore the optimal feature subset from 25 geometric features incorporating 25 scales under 6 definitions for urban land cover classification. We performed a feature selection strategy to remove irrelevant or redundant features based on the correlation coefficient between features and classification accuracy of each features. The neighborhood scales were divided into small (0.5–1.5 m), medium (1.5–6 m) and large (>6 m) scale. Combining features with lower correlation coefficient and better classification performance would improve classification accuracy. The feature depicting homogeneity or heterogeneity of points would be calculated at a small scale, and the features to smooth points at a medium scale and the features of height different at large scale. As to the neighborhood definition, cuboid and cylinder were recommended. This study can guide the selection of optimal geometric features with adaptive neighborhood scale for urban land cover classification. 相似文献
13.
Pine plantations in Australia are subject to a range of abiotic and biotic damaging agents that affect tree health and productivity. In order to optimise management decisions, plantation managers require regular intelligence relating to the status and trends in the health and condition of trees within individual compartments. Remote sensing technology offers an alternative to traditional ground-based assessment of these plantations. Automated estimation of foliar crown health, especially in degraded crowns, can be difficult due to mixed pixels when there is low or fragmented vegetation cover. In this study we apply a linear spectral unmixing approach to high spatial resolution (50 cm) multispectral imagery to quantify the fractional abundances of the key image endmembers: sunlit canopy, shadow, and soil. A number of Pinus radiata tree crown attributes were modelled using multiple linear regression and endmember fraction images. We found high levels of significance (r2 = 0.80) for the overall crown colour and colour of the crown leader (r2 = 0.79) in tree crowns affected by the fungal pathogen Sphaeropsis sapinea, which produces both needle necrosis and chlorosis. Results for stands associated with defoliation and chlorosis through infestation by the aphid Essigella californica were lower with an r2 = 0.33 for crown transparency and r2 = 0.31 for proportion of crown affected. Similar analysis of data from a nitrogen deficient site produced an outcome somewhat in between the other two damaging agents. Overall the sunlit canopy image fraction has been the most important variable used in the modelling of forest condition for all damaging agents. 相似文献
14.
Leaf area index (LAI) and biomass are important indicators of crop development and the availability of this information during the growing season can support farmer decision making processes. This study demonstrates the applicability of RapidEye multi-spectral data for estimation of LAI and biomass of two crop types (corn and soybean) with different canopy structure, leaf structure and photosynthetic pathways. The advantages of Rapid Eye in terms of increased temporal resolution (∼daily), high spatial resolution (∼5 m) and enhanced spectral information (includes red-edge band) are explored as an individual sensor and as part of a multi-sensor constellation. Seven vegetation indices based on combinations of reflectance in green, red, red-edge and near infrared bands were derived from RapidEye imagery between 2011 and 2013. LAI and biomass data were collected during the same period for calibration and validation of the relationships between vegetation indices and LAI and dry above-ground biomass. Most indices showed sensitivity to LAI from emergence to 8 m2/m2. The normalized difference vegetation index (NDVI), the red-edge NDVI and the green NDVI were insensitive to crop type and had coefficients of variations (CV) ranging between 19 and 27%; and coefficients of determination ranging between 86 and 88%. The NDVI performed best for the estimation of dry leaf biomass (CV = 27% and r2 = 090) and was also insensitive to crop type. The red-edge indices did not show any significant improvement in LAI and biomass estimation over traditional multispectral indices. Cumulative vegetation indices showed strong performance for estimation of total dry above-ground biomass, especially for corn (CV ≤ 20%). This study demonstrated that continuous crop LAI monitoring over time and space at the field level can be achieved using a combination of RapidEye, Landsat and SPOT data and sensor-dependant best-fit functions. This approach eliminates/reduces the need for reflectance resampling, VIs inter-calibration and spatial resampling. 相似文献
15.
Precision agriculture requires high-resolution information to enable greater precision in the management of inputs to production. Actionable information about crop and field status must be acquired at high spatial resolution and at a temporal frequency appropriate for timely responses. In this study, high spatial resolution imagery was obtained through the use of a small, unmanned aerial system called AggieAirTM. Simultaneously with the AggieAir flights, intensive ground sampling for plant chlorophyll was conducted at precisely determined locations. This study reports the application of a relevance vector machine coupled with cross validation and backward elimination to a dataset composed of reflectance from high-resolution multi-spectral imagery (VIS–NIR), thermal infrared imagery, and vegetative indices, in conjunction with in situ SPAD measurements from which chlorophyll concentrations were derived, to estimate chlorophyll concentration from remotely sensed data at 15-cm resolution. The results indicate that a relevance vector machine with a thin plate spline kernel type and kernel width of 5.4, having LAI, NDVI, thermal and red bands as the selected set of inputs, can be used to spatially estimate chlorophyll concentration with a root-mean-squared-error of 5.31 μg cm−2, efficiency of 0.76, and 9 relevance vectors. 相似文献
16.
Inputs to various applications and models, current global land cover (GLC) maps are based on different data sources and methods. Therefore, comparing GLC maps is challenging. Statistical comparison of GLC maps is further complicated by the lack of a reference dataset that is suitable for validating multiple maps. This study utilizes the existing Globcover-2005 reference dataset to compare thematic accuracies of three GLC maps for the year 2005 (Globcover, LC-CCI and MODIS). We translated and reinterpreted the LCCS (land cover classification system) classifier information of the reference dataset into the different map legends. The three maps were evaluated for a variety of applications, i.e., general circulation models, dynamic global vegetation models, agriculture assessments, carbon estimation and biodiversity assessments, using weighted accuracy assessment. Based on the impact of land cover confusions on the overall weighted accuracy of the GLC maps, we identified map improvement priorities. Overall accuracies were 70.8 ± 1.4%, 71.4 ± 1.3%, and 61.3 ± 1.5% for LC-CCI, MODIS, and Globcover, respectively. Weighted accuracy assessments produced increased overall accuracies (80–93%) since not all class confusion errors are important for specific applications. As a common denominator for all applications, the classes mixed trees, shrubs, grasses, and cropland were identified as improvement priorities. The results demonstrate the necessity of accounting for dissimilarities in the importance of map classification errors for different user application. To determine the fitness of use of GLC maps, accuracy of GLC maps should be assessed per application; there is no single-figure accuracy estimate expressing map fitness for all purposes. 相似文献
17.
Indigenous forest biome in South Africa is highly fragmented into patches of various sizes (most patches < 1 km2). The utilization of timber and non-timber resources by poor rural communities living around protected forest patches produce subtle changes in the forest canopy which can be hardly detected on a timely manner using traditional field surveys. The aims of this study were to assess: (i) the utility of very high resolution (VHR) remote sensing imagery (WorldView-2, 0.5–2 m spatial resolution) for mapping tree species and canopy gaps in one of the protected subtropical coastal forests in South Africa (the Dukuduku forest patch (ca.3200 ha) located in the province of KwaZulu-Natal) and (ii) the implications of the map products to forest conservation. Three dominant canopy tree species namely, Albizia adianthifolia, Strychnos spp. and Acacia spp., and canopy gap types including bushes (grass/shrubby), bare soil and burnt patches were accurately mapped (overall accuracy = 89.3 ± 2.1%) using WorldView-2 image and support vector machine classifier. The maps revealed subtle forest disturbances such as bush encroachment and edge effects resulting from forest fragmentation by roads and a power-line. In two stakeholders’ workshops organised to assess the implications of the map products to conservation, participants generally agreed amongst others implications that the VHR maps provide valuable information that could be used for implementing and monitoring the effects of rehabilitation measures. The use of VHR imagery is recommended for timely inventorying and monitoring of the small and fragile patches of subtropical forests in Southern Africa. 相似文献
18.
Bracken fern is an invasive plant that presents serious environmental, ecological and economic problems around the world. An understanding of the spatial distribution of bracken fern weeds is therefore essential for providing appropriate management strategies at both local and regional scales. The aim of this study was to assess the utility of the freely available medium resolution Landsat 8 OLI sensor in the detection and mapping of bracken fern at the Cathedral Peak, South Africa. To achieve this objective, the results obtained from Landsat 8 OLI were compared with those derived using the costly, high spatial resolution WorldView-2 imagery. Since previous studies have already successfully mapped bracken fern using high spatial resolution WorldView-2 image, the comparison was done to investigate the magnitude of difference in accuracy between the two sensors in relation to their acquisition costs. To evaluate the performance of Landsat 8 OLI in discriminating bracken fern compared to that of Worldview-2, we tested the utility of (i) spectral bands; (ii) derived vegetation indices as well as (iii) the combination of spectral bands and vegetation indices based on discriminant analysis classification algorithm. After resampling the training and testing data and reclassifying several times (n = 100) based on the combined data sets, the overall accuracies for both Landsat 8 and WorldView-2 were tested for significant differences based on Mann-Whitney U test. The results showed that the integration of the spectral bands and derived vegetation indices yielded the best overall classification accuracy (80.08% and 87.80% for Landsat 8 OLI and WorldView-2 respectively). Additionally, the use of derived vegetation indices as a standalone data set produced the weakest overall accuracy results of 62.14% and 82.11% for both the Landsat 8 OLI and WorldView-2 images. There were significant differences {U (100) = 569.5, z = −10.8242, p < 0.01} between the classification accuracies derived based on Landsat OLI 8 and those derived using WorldView-2 sensor. Although there were significant differences between Landsat and WorldView-2 accuracies, the magnitude of variation (9%) between the two sensors was within an acceptable range. Therefore, the findings of this study demonstrated that the recently launched Landsat 8 OLI multispectral sensor provides valuable information that could aid in the long term continuous monitoring and formulation of effective bracken fern management with acceptable accuracies that are comparable to those obtained from the high resolution WorldView-2 commercial sensor. 相似文献
19.
In this paper, a user-defined inter-band correlation filter function was used to resample hyperspectral data and thereby mitigate the problem of multicollinearity in classification analysis. The proposed resampling technique convolves the spectral dependence information between a chosen band-centre and its shorter and longer wavelength neighbours. Weighting threshold of inter-band correlation (WTC, Pearson's r) was calculated, whereby r = 1 at the band-centre. Various WTC (r = 0.99, r = 0.95 and r = 0.90) were assessed, and bands with coefficients beyond a chosen threshold were assigned r = 0. The resultant data were used in the random forest analysis to classify in situ C3 and C4 grass canopy reflectance. The respective WTC datasets yielded improved classification accuracies (kappa = 0.82, 0.79 and 0.76) with less correlated wavebands when compared to resampled Hyperion bands (kappa = 0.76). Overall, the results obtained from this study suggested that resampling of hyperspectral data should account for the spectral dependence information to improve overall classification accuracy as well as reducing the problem of multicollinearity. 相似文献
20.
Mangrove species compositions and distributions are essential for conservation and restoration efforts. In this study, hyperspectral data of EO-1 HYPERION sensor and high spatial resolution data of SPOT-5 sensor were used in Mai Po mangrove species mapping. Objected-oriented method was used in mangrove species classification processing. Firstly, mangrove objects were obtained via segmenting high spatial resolution data of SPOT-5. Then the objects were classified into different mangrove species based on the spectral differences of HYPERION image. The classification result showed that in the top canopy, Kandelia obovata and Avicennia marina dominated Mai Po Marshes Natural Reserve, with area of 196.8 ha and 110.8 ha, respectively, Acanthus ilicifolius and Aegiceras corniculatum were mixed together and living at the edge of channels with an area of 11.7 ha. Additionally, mangrove species shows clearly zonations and associations in the Mai Po Core Zone. The overall accuracy of our mangrove map was 88% and the Kappa confidence was 0.83, which indicated great potential of using hyperspectral and high-resolution data for distinguishing and mapping mangrove species. 相似文献