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1.
The development of cost-effective, reliable and easy to implement crop condition monitoring methods is urgently required for perennial tree crops such as coffee (Coffea arabica), as they are grown over large areas and represent long term and higher levels of investment. These monitoring methods are useful in identifying farm areas that experience poor crop growth, pest infestation, diseases outbreaks and/or to monitor response to management interventions. This study compares field level coffee mean NDVI and LSWI anomalies and age-adjusted coffee mean NDVI and LSWI anomalies in identifying and mapping incongruous patches across perennial coffee plantations. To achieve this objective, we first derived deviation of coffee pixels from the global coffee mean NDVI and LSWI values of nine sequential Landsat 8 OLI image scenes. We then evaluated the influence of coffee age class (young, mature and old) on Landsat-scale NDVI and LSWI values using a one-way ANOVA and since results showed significant differences, we adjusted NDVI and LSWI anomalies for age-class. We then used the cumulative inverse distribution function (α ≤ 0.05) to identify fields and within field areas with excessive deviation of NDVI and LSWI from the global and the age-expected mean for each of the Landsat 8 OLI scene dates spanning three seasons. Results from accuracy assessment indicated that it was possible to separate incongruous and healthy patches using these anomalies and that using NDVI performed better than using LSWI for both global and age-adjusted mean anomalies. Using the age-adjusted anomalies performed better in separating incongruous and healthy patches than using the global mean for both NDVI (Overall accuracy = 80.9% and 68.1% respectively) and for LSWI (Overall accuracy = 68.1% and 48.9% respectively). When applied to other Landsat 8 OLI scenes, the results showed that the proportions of coffee fields that were modelled incongruent decreased with time for the young age category and while it increased for the mature and old age classes with time. We concluded that the method could be useful for the identification of anomalous patches using Landsat scale time series data to monitor large coffee plantations and provide an indication of areas requiring particular field attention. 相似文献
2.
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
3.
In recent decades, the Kou watershed in south-western Burkina Faso has suffered from poor water management. Despite the abundance of water, most water users regularly face water shortages because of the increase in the amount of land under irrigation. To help them achieve a more equitable allocation of irrigated land, local stakeholders need an easily managed low-cost tool for monitoring and mapping these irrigated zones. The aim of this study was to develop a fast and low-cost procedure for mosaicing and geo referencing amateur small-scale aerial photographs for land-use surveys. Sets of tens (2009) and hundreds (2007) of low-altitude aerial photographs, with a resolution of 0.4 m and 0.8 m, respectively, were used to create a detailed land-cover map of typical African small-scale irrigated agriculture. A commercially available stitching tool and GIS allowed geo referenced 'mono-images’ to be constructed; both mosaics were warped on a high-resolution SPOT image with a horizontal root mean square error (RMSE) of about 11 m. The RMSE between the two image datasets was 2 m. This approach is less sensitive to atmospheric conditions that are non-predictable in programming satellite imagery. 相似文献
4.
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
5.
Detailed land-cover mapping is essential for a range of research issues addressed by the sustainability and land system sciences and planning. This study uses an object-based approach to create a 1 m land-cover classification map of the expansive Phoenix metropolitan area through the use of high spatial resolution aerial photography from National Agricultural Imagery Program. It employs an expert knowledge decision rule set and incorporates the cadastral GIS vector layer as auxiliary data. The classification rule was established on a hierarchical image object network, and the properties of parcels in the vector layer were used to establish land cover types. Image segmentations were initially utilized to separate the aerial photos into parcel sized objects, and were further used for detailed land type identification within the parcels. Characteristics of image objects from contextual and geometrical aspects were used in the decision rule set to reduce the spectral limitation of the four-band aerial photography. Classification results include 12 land-cover classes and subclasses that may be assessed from the sub-parcel to the landscape scales, facilitating examination of scale dynamics. The proposed object-based classification method provides robust results, uses minimal and readily available ancillary data, and reduces computational time. 相似文献
6.
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
7.
With the high deforestation rates of global forest covers during the past decades, there is an ever-increasing need to monitor forest covers at both fine spatial and temporal resolutions. Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat series images have been used commonly for satellite-derived forest cover mapping. However, the spatial resolution of MODIS images and the temporal resolution of Landsat images are too coarse to observe forest cover at both fine spatial and temporal resolutions. In this paper, a novel multiscale spectral-spatial-temporal superresolution mapping (MSSTSRM) approach is proposed to update Landsat-based forest maps by integrating current MODIS images with the previous forest maps generated from Landsat image. Both the 240 m MODIS bands and 480 m MODIS bands were used as inputs of the spectral energy function of the MSSTSRM model. The principle of maximal spatial dependence was used as the spatial energy function to make the updated forest map spatially smooth. The temporal energy function was based on a multiscale spatial-temporal dependence model, and considers the land cover changes between the previous and current time. The novel MSSTSRM model was able to update Landsat-based forest maps more accurately, in terms of both visual and quantitative evaluation, than traditional pixel-based classification and the latest sub-pixel based super-resolution mapping methods The results demonstrate the great efficiency and potential of MSSTSRM for updating fine temporal resolution Landsat-based forest maps using MODIS images. 相似文献
8.
Flagrant soil erosion in Morocco is an alarming sign of soil degradation. Due to the considerable costs of detailed ground surveys of this phenomenon, remote sensing is an appropriate alternative for analyzing and evaluating the risks of the expansion of soil degradation. In this paper, we characterize the state of land degradation in a small Mediterranean watershed using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data and ground-based spectroradiometric measurements. The two visible, the near-infrared and six shortwave infrared bands of the above sensor were calibrated using ground measurements of the spectral reflectance. Field measurements were carried out in the Saboun experimental basin located in the marl soil region of the Moroccan western Rif. The study leads to the development and evaluation of a new spectral approach to express land degradation. This index called Land degradation index (LDI) is based on the concept of the soil line derived from spectroradiometric ground measurements. In this study, we compare LDI and the spectral angle mapping (SAM) approaches to assess and map land degradation. Results show that LDI provides more accurate results for mapping land degradation (Kappa = 0.79) when compared to the SAM method (Kappa = 0.61). Validation and evaluation of the results are based on the thematic maps derived from the ground data (organic matter, clay, silt and sand) by kriging, DEM, slope gradient and photointerpretation. 相似文献
9.
Large area tree maps, important for environmental monitoring and natural resource management, are often based on medium resolution satellite imagery. These data have difficulty in detecting trees in fragmented woodlands, and have significant omission errors in modified agricultural areas. High resolution imagery can better detect these trees, however, as most high resolution imagery is not normalised it is difficult to automate a tree classification method over large areas. The method developed here used an existing medium resolution map derived from either Landsat or SPOT5 satellite imagery to guide the classification of the high resolution imagery. It selected a spatially-variable threshold on the green band, calculated based on the spatially-variable percentage of trees in the existing map of tree cover. The green band proved more consistent at classifying trees across different images than several common band combinations. The method was tested on 0.5 m resolution imagery from airborne digital sensor (ADS) imagery across New South Wales (NSW), Australia using both Landsat and SPOT5 derived tree maps to guide the threshold selection. Accuracy was assessed across 6 large image mosaics revealing a more accurate result when the more accurate tree map from SPOT5 imagery was used. The resulting maps achieved an overall accuracy with 95% confidence intervals of 93% (90–95%), while the overall accuracy of the previous SPOT5 tree map was 87% (86–89%). The method reduced omission errors by mapping more scattered trees, although it did increase commission errors caused by dark pixels from water, building shadows, topographic shadows, and some soils and crops. The method allows trees to be automatically mapped at 5 m resolution from high resolution imagery, provided a medium resolution tree map already exists. 相似文献
10.
The estimation of above ground biomass in forests is critical for carbon cycle modeling and climate change mitigation programs. Small footprint lidar provides accurate biomass estimates, but its application in tropical forests has been limited, particularly in Africa. Hyperspectral data record canopy spectral information that is potentially related to forest biomass. To assess lidar ability to retrieve biomass in an African forest and the usefulness of including hyperspectral information, we modeled biomass using small footprint lidar metrics as well as airborne hyperspectral bands and derived vegetation indexes. Partial Least Square Regression (PLSR) was adopted to cope with multiple inputs and multicollinearity issues; the Variable of Importance in the Projection was calculated to evaluate importance of individual predictors for biomass. Our findings showed that the integration of hyperspectral bands (R2 = 0.70) improved the model based on lidar alone (R2 = 0.64), this encouraging result call for additional research to clarify the possible role of hyperspectral data in tropical regions. Replacing the hyperspectral bands with vegetation indexes resulted in a smaller improvement (R2 = 0.67). Hyperspectral bands had limited predictive power (R2 = 0.36) when used alone. This analysis proves the efficiency of using PLSR with small-footprint lidar and high resolution hyperspectral data in tropical forests for biomass estimation. Results also suggest that high quality ground truth data is crucial for lidar-based AGB estimates in tropical African forests, especially if airborne lidar is used as an intermediate step of upscaling field-measured AGB to a larger area. 相似文献
11.
Land cover change is increasingly affecting the biophysics, biogeochemistry, and biogeography of the Earth's surface and the atmosphere, with far-reaching consequences to human well-being. However, our scientific understanding of the distribution and dynamics of land cover and land cover change (LCLCC) is limited. Previous global land cover assessments performed using coarse spatial resolution (300 m–1 km) satellite data did not provide enough thematic detail or change information for global change studies and for resource management. High resolution (∼30 m) land cover characterization and monitoring is needed that permits detection of land change at the scale of most human activity and offers the increased flexibility of environmental model parameterization needed for global change studies. However, there are a number of challenges to overcome before producing such data sets including unavailability of consistent global coverage of satellite data, sheer volume of data, unavailability of timely and accurate training and validation data, difficulties in preparing image mosaics, and high performance computing requirements. Integration of remote sensing and information technology is needed for process automation and high-performance computing needs. Recent developments in these areas have created an opportunity for operational high resolution land cover mapping, and monitoring of the world. Here, we report and discuss these advancements and opportunities in producing the next generations of global land cover characterization, mapping, and monitoring at 30-m spatial resolution primarily in the context of United States, Group on Earth Observations Global 30 m land cover initiative (UGLC). 相似文献
12.
Unmanned Aerial Vehicle (UAV) remote sensing has opened the door to new sources of data to effectively characterize vegetation metrics at very high spatial resolution and at flexible revisit frequencies. Successful estimation of the leaf area index (LAI) in precision agriculture with a UAV image has been reported in several studies. However, in most forests, the challenges associated with the interference from a complex background and a variety of vegetation species have hindered research using UAV images. To the best of our knowledge, very few studies have mapped the forest LAI with a UAV image. In addition, the drawbacks and advantages of estimating the forest LAI with UAV and satellite images at high spatial resolution remain a knowledge gap in existing literature. Therefore, this paper aims to map LAI in a mangrove forest with a complex background and a variety of vegetation species using a UAV image and compare it with a WorldView-2 image (WV2).In this study, three representative NDVIs, average NDVI (AvNDVI), vegetated specific NDVI (VsNDVI), and scaled NDVI (ScNDVI), were acquired with UAV and WV2 to predict the plot level (10 × 10 m) LAI. The results showed that AvNDVI achieved the highest accuracy for WV2 (R2 = 0.778, RMSE = 0.424), whereas ScNDVI obtained the optimal accuracy for UAV (R2 = 0.817, RMSE = 0.423). In addition, an overall comparison results of the WV2 and UAV derived LAIs indicated that UAV obtained a better accuracy than WV2 in the plots that were covered with homogeneous mangrove species or in the low LAI plots, which was because UAV can effectively eliminate the influence from the background and the vegetation species owing to its high spatial resolution. However, WV2 obtained a slightly higher accuracy than UAV in the plots covered with a variety of mangrove species, which was because the UAV sensor provides a negative spectral response function(SRF) than WV2 in terms of the mangrove LAI estimation. 相似文献
13.
Remote sensing technologies are an ideal platform to examine the extent and impact of fire on the landscape. In this study we assess that capacity of the RapidEye constellation and Landsat (Thematic Mapper and Operational Land Imager to map fine-scale burn attributes for a small, low severity prescribed fire in a dry Western Canadian forest. Estimates of burn severity from field data were collated into a simple burn index and correlated with a selected suite of common spectral vegetation indices. Burn severity classes were then derived to map fire impacts and estimate consumed woody surface fuels (diameter ≥2.6 cm). All correlations between the simple burn index and vegetation indices produced significant results (p < 0.01), but varied substantially in their overall accuracy. Although the Landsat Soil Adjusted Vegetation Index provided the best regression fit (R2 = 0.56), results suggested that RapidEye provided much more spatially detailed estimates of tree damage (Soil Adjusted Vegetation Index, R2 = 0.51). Consumption estimates of woody surface fuels ranged from 3.38 ± 1.03 Mg ha−1 to 11.73 ± 1.84 Mg ha−1, across four derived severity classes with uncertainties likely a result of changing foliage moisture between the before and after fire images. While not containing spectral information in the short wave infrared, the spatial variability provided by the RapidEye imagery has potential for mapping and monitoring fine scale forest attributes, as well as the potential to resolve fire damage at the individual tree level. 相似文献
14.
Recent changes in rice crop management within Northern Italy rice district led to a reduction of seeding in flooding condition, which may have an impact on reservoir water management and on the animal and plant communities that depend on the flooded paddies. Therefore, monitoring and quantifying the spatial and temporal variability of water presence in paddy fields is becoming important. In this study we present a method to estimate dynamics of presence of standing water (i.e. fraction of flooded area) in rice fields using MODIS data. First, we produced high resolution water presence maps from Landsat by thresholding the Normalised Difference Flood Index (NDFI) made: we made it by comparing five Landsat 8 images with field-obtained information about rice field status and water presence. Using these data we developed an empirical model to estimate the flooding fraction of each MODIS cell. Finally we validated the MODIS-based flooding maps with both Landsat and ground information. Results showed a good predictability of water surface from Landsat (OA = 92%) and a robust usability of MODIS data to predict water fraction (R2 = 0.73, EF = 0.57, RMSE = 0.13 at 1 × 1 km resolution). Analysis showed that the predictive ability of the model decreases with the greening up of rice, so we used NDVI to automatically discriminate estimations for inaccurate cells in order to provide the water maps with a reliability flag. Results demonstrate that it is possible to monitor water dynamics in rice paddies using moderate resolution multispectral satellite data. The achievement is a proof of concept for the analysis of MODIS archives to investigate irrigation dynamics in the last 15 years to retrieve information for ecological and hydrological studies. 相似文献
15.
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
16.
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
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.
Seagrass habitats in subtidal coastal waters provide a variety of ecosystem functions and services and there is an increasing need to acquire information on spatial and temporal dynamics of this resource. Here, we explored the capability of IKONOS (IKO) data of high resolution (4 m) for mapping seagrass cover [submerged aquatic vegetation (%SAV) cover] along the mid-western coast of Florida, USA. We also compared seagrass maps produced with IKO data with that obtained using the Landsat TM sensor with lower resolution (30 m). Both IKO and TM data, collected in October 2009, were preprocessed to calculate water depth invariant bands to normalize the effect of varying depth on bottom spectra recorded by the two satellite sensors and further the textural information was extracted from IKO data. Our results demonstrate that the high resolution IKO sensor produced a higher accuracy than the TM sensor in a three-class % SAV cover classification. Of note is that the OA of %SAV cover mapping at our study area created with IKO data was 5–20% higher than that from other studies published. We also examined the spatial distribution of seagrass over a spatial range of 4–240 m using the Ripley’s K function [L(d)] and IKO data that represented four different grain sizes [4 m (one IKO pixel), 8 m (2 × 2 IKO pixels), 12 m (3 × 3 IKO pixels), and 16 m (4 × 4 IKO pixels)] from moderate-dense seagrass cover along a set of six transects. The Ripley’s K metric repeatedly indicated that seagrass cover representing 4 m × 4 m pixels displayed a dispersed (or slightly dispersed) pattern over distances of <4–8 m, and a random or slightly clustered pattern of cover over 9–240 m. The spatial pattern of seagrass cover created with the three additional grain sizes (i.e., 2 × 24 m IKO pixels, 3 × 34 m IKO pixels, and 4 × 4 m IKO pixels) show a dispersed (or slightly dispersed) pattern across 4–32 m and a random or slightly clustered pattern across 33–240 m. Given the first report on using satellite observations to quantify seagrass spatial patterns at a spatial scale from 4 m to 240 m, our novel analyses of moderate-dense SAV cover utilizing Ripley’s K function illustrate how data obtained from the IKO sensor revealed seagrass spatial information that would be undetected by the TM sensor with a 30 m pixel size. Use of the seagrass classification scheme here, along with data from the IKO sensor with enhanced resolution, offers an opportunity to synoptically record seagrass cover dynamics at both small and large spatial scales. 相似文献
19.
Compared with traditional ground surveys, remote sensing has the potential to map the spatial extent of non-native invasive species rapidly and reliably. This paper assesses the potential of spectroradiometry to distinguish and characterise the status of invasive non-native rhododendron (Rhododendron ponticum). Absolute reflectance of target plant material was measured with an ASD Fieldspec Pro System under standardised laboratory conditions and in the field to characterise spectral signatures in the winter, during leaf-off conditions for woodland overstory, and in the summer when mature rhododendrons are flowering. A logistic regression model of absolute reflectance at key wavelengths (490, 550, 610, 1040 and 1490 nm) was used to determine the success of discriminating rhododendron from three other shrubby species likely to be encountered in woodlands during the winter. The logistic regression model was highly significant (p < 0.001), with 93.5% of 246 leaf sets correctly identified as rhododendron or non-rhododendron (i.e. cherry laurel (Prunus laurocerasus), holly (Ilex aquifolium), and beech (Fagus sylvatica)). Rescaling the data to emulate the spectral resolution of airborne and satellite acquired data decreased the total success rate of correctly identifying rhododendron by only 0.4%; although this error rate will likely increase for airborne or satellite data as a result of atmospheric attenuation and reduced spatial resolution. This demonstrates the potential to map bush presence using hyperspectral data and indicates the optimum spectral wavelengths required. Such information is critical to the development of successful strategic management plans to eradicate rhododendron (and the associated Phytophthora ramorum pathogen) effectively from a site. 相似文献
20.
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