Fire monitoring in savanna ecosystems using MODIS data: a case study of Kruger National Park, South Africa

The heterogeneity of savanna ecosystems is guaranteed by disturbance events like fires, droughts, floods and browsing and grazing by herbivores. For conservation areas with limited space to preserve biodiversity, fire monitoring is crucial. Long periods of satellite remotely sensed data provide an alternative solution to estimate the distribution of different vegetation types and fire-affected patches over time. This study focusses on the application of MODIS data to detect, identify and delineate fire-affected areas in Kruger National Park (KNP), South Africa, for the period 2001–2003. Fire scars on KNP’s savanna were identified using threshold and supervised classification methods on moderate-resolution imaging spectroradiometer (MODIS) with 500-m resolution and 32-day global composites using a combination of band 1 (red), 2 (NIR, near infrared), 4 (green) and 6 (SWIR, short wave infrared). On identified fire scars, the spectral indexes of albedo, normalised difference infrared index (NDII) and normalised difference vegetation index (NDVI) were extracted. The following four broad habitat types were used for this analysis: riparian woodland, dense woodland, mixed woodland and open-tree savanna. The values of albedo, NDII and NDVI during the dry season (June to October) for different years are lower on fire-affected patches. Mixed woodland is the largest habitat burned with 21%, 43% and 2% of the KNP area affected by fire in 2001, 2002 and 2003, respectively. Riparian woodland is the least affected by fire. The supervised classification method has a greater accuracy for fire scars detection in KNP savannas during the dry season. We conclude that MODIS data can be used successfully for fire monitoring in savanna ecosystems.     

Remote sensing of upland vegetation: the potential of high spatial resolution satellite sensors

Aim Traditional methodologies of mapping vegetation, as carried out by ecologists, consist primarily of field surveying or mapping from aerial photography. Previous applications of satellite imagery for this task (e.g. Landsat TM and SPOT HRV) have been unsuccessful, as such imagery proved to have insufficient spatial resolution for mapping vegetation. This paper reports on a study to assess the capabilities of the recently launched remote sensing satellite sensor Ikonos, with improved capabilities, for mapping and monitoring upland vegetation using traditional image classification methods. Location The location is Northumberland National Park, UK. Methods Traditional remote sensing classification methodologies were applied to the Ikonos data and the outputs compared to ground data sets. This enabled an assessment of the value of the improved spatial resolution of satellite imagery for mapping upland vegetation. Post‐classification methods were applied to remove noise and misclassified pixels and to create maps that were more in keeping with the information requirements of the NNPA for current management processes. Results The approach adopted herein for quick and inexpensive land cover mapping was found to be capable of higher accuracy than achieved with previous approaches, highlighting the benefits of remote sensing for providing land cover maps. Main conclusions Ikonos imagery proved to be a useful tool for mapping upland vegetation across large areas and at fine spatial resolution, providing accuracies comparable to traditional mapping methods of ground surveys and aerial photography.     

Vegetation survey in the former brown coal mining area of eastern Germany by integrating remote sensing and ground-based methods

Abstract. In the former brown coal mining area of eastern Germany, now scheduled as a nature conservation area, an analysis of the spatial distribution of vegetation was considered as an important tool in landscape planning. Therefore a comprehensive vegetation survey by means of satellite imagery (Landsat-TM), airborne imagery (CASI), and ground-based methods, notably habitat mapping and vegetation sampling was carried out. With respect to the scales of resolution the classification results of the four methods are, to a certain degree, comparable. Differences in the outcome can be ascribed to the fact that methods of low resolution result in a discrete array of polygons whereas methods of high resolution depict a mosaic structure with an underlying, continuously changing gradient. Provided that the biological meaning of the remote sensing classification is known, a shift from single vegetation patterns to the landscape scale will be possible. Neither satellite nor airborne imagery is restricted to the purpose of mapping but may also serve for vegetation classification itself.     

High‐resolution riparian vegetation mapping to prioritize conservation and restoration in an impaired desert river

In highly impaired watersheds, it is critical to identify both areas with desirable habitat as conservation zones and impaired areas with the highest likelihood of improvement as restoration zones. We present how detailed riparian vegetation mapping can be used to prioritize conservation and restoration sites within a riparian and instream habitat restoration program targeting 3 native fish species on the San Rafael River, a desert river in southeastern Utah, United States. We classified vegetation using a combination of object‐based image analysis (OBIA) on high‐resolution (0.5 m), multispectral, satellite imagery with oblique aerial photography and field‐based data collection. The OBIA approach is objective, repeatable, and applicable to large areas. The overall accuracy of the classification was 80% (Cohen's κ = 0.77). We used this high‐resolution vegetation classification alongside existing data on habitat condition and aquatic species' distributions to identify reaches' conservation value and restoration potential to guide management actions. Specifically, cottonwood (Populus fremontii) and tamarisk (Tamarix ramosissima) density layers helped to establish broad restoration and conservation reach classes. The high‐resolution vegetation mapping precisely identified individual cottonwood trees and tamarisk thickets, which were used to determine specific locations for restoration activities such as beaver dam analogue structures in cottonwood restoration areas, or strategic tamarisk removal in high‐density tamarisk sites. The site prioritization method presented here is effective for planning large‐scale river restoration and is transferable to other desert river systems elsewhere in the world.     

Mapping and monitoring of coastal wetlands of Çukurova Delta in the Eastern Mediterranean region

The aim of this research was to link vegetation characteristics, such as spatial and temporal distribution, and environmental variables, with land cover information derived from remotely sensed satellite images of the Eastern Mediterranean coastal wetlands of Turkey. The research method was based on (i) recording land cover characteristics by means of a vegetation indicator, and (ii) classifying and mapping coastal wetlands utilizing a Landsat Thematic Mapper (TM) image of Çukurova Deltas in Turkey. Vegetation characteristics of various habitats, such as sand dunes, salt marshes, salty plains and afforestation areas, were identified by field surveys. A Landsat TM image of 4 July 1993 was pre-processed and then classified using the Maximum Likelihood (ML) algorithm and Artificial Neural Networks (ANN). As a result of this supervised classification, the land cover types were classified with a largest accuracy of 90.2% by ANN. The classified satellite sensor imagery was linked to vegetation and bird census data, which were available through literature in a Geographical Information System (GIS) environment to determine the spatial distribution of plant and bird biodiversity in this coastal wetland. The resulting data provide an important baseline for further investigations such as monitoring, change detections and designing conservation policies in this coastal ecosystem.     

Comparison of the use of Landsat 8, Sentinel-2, and Gaofen-2 images for mapping soil pH in Dehui,northeastern China

Mapping the distribution and quantity of soil properties is important for black soil protection, management, and restoration in northeastern China. The objective of this study was to evaluate the effect of the spatial resolution on soil pH mapping using satellite images of the black soil region in northeastern China. A high spatial resolution Gaofen (GF)-2 high-definition image and multispectral images acquired by the Landsat 8 operational land imager and Sentinel-2 multi-spectral instrument were used to compare their performance in soil pH prediction. The spectral variables, including the original bands of the three satellite images and a variety of spectral indices derived from the original bands, were employed. Then, a machine learning model (quantile regression forest) was used to determine the relationships between the spectral variables and the measured soil pH, and prediction models were established to estimate the soil pH and to characterize the spatial pattern of the soil pH. The results revealed that the soil pH prediction model based on the GF-2 image had a slightly higher prediction accuracy than the models constructed using the Landsat 8 and Sentinel-2 images. The prediction models for Landsat 8, Sentinel-2, and GF-2 had root mean square errors of 0.34, 0.39, and 0.31, respectively. The use of remote sensing images with a high spatial resolution may not substantially increase the prediction accuracy of soil pH mapping compared with the results derived from medium-resolution images.     

基于图像融合与混合像元分解的城市植被盖度提取

城市植被盖度提取对于开展城市绿色空间保护和城市规划具有重要意义。随着遥感技术的发展,混合像元分解模型被广泛用于从中等分辨率的多光谱影像提取城市植被盖度,但较低的影像空间分辨率限制了该模型的应用领域。为此,以杭州市为例,首先引入Gram-Schmidt(GS)方法对Landsat ETM+的多光谱波段和全色波段进行融合,再通过混合像元分解模型从ETM+融合影像上提取城市植被盖度,最后利用SPOT影像进行精度检验。结果发现,采用GS方法对影像进行融合后,标准差、信息熵、平均梯度提高,相对偏差小于0.07,说明在保留多光谱信息的基础上提高了其空间分辨率。与SPOT影像相比,在融合影像上75%以上样本的植被盖度值相似,误差较大的区域是市区植被特别稀疏或茂盛的像元。与源影像相比,从融合影像上提取的植被盖度的均方根误差和系统误差降低了0.01。该方法在降低城市植被监测成本、提高监测精度方面具有潜力。     

Object-oriented methods for habitat mapping at multiple scales – Case studies from Northern Germany and Wye Downs, UK

This paper presents an application of object-oriented techniques for habitat classification based on remotely sensed images and ancillary data. The study reports the results of habitat mapping at multiple scales using Earth Observation (EO) data at various spatial resolutions and multi temporal acquisition dates. We investigate the role of object texture and context in classification as well as the value of integrating knowledge from ancillary data sources. Habitat maps were produced at regional and local scales in two case studies; Schleswig-Holstein, Germany and Wye Downs, United Kingdom. At the regional scale, the main task was the development of a consistent object-oriented classification scheme that is transferable to satellite images for other years. This is demonstrated for a time series of Landsat TM/ETM+ scenes. At the local scale, investigations focus on the development of appropriate object-oriented rule networks for the detailed mapping of habitats, e.g. dry grasslands and wetlands using very high resolution satellite and airborne scanner images. The results are evaluated using statistical accuracy assessment and visual comparison with traditional field-based habitat maps. Whereas the application of traditional pixel-based classification result in a pixelised (salt and pepper) representation of land cover, the object-based classification technique result in solid habitat objects allowing easy integration into a vector-GIS for further analysis. The level of detail obtained at the local scale is comparable to that achieved by visual interpretation of aerial photographs or field-based mapping and also retains spatially explicit, fine scale information such as scrub encroachment or ecotone patterns within habitats.     

A new forest cover map of continental southeast Asia derived from SPOT‐VEGETATION satellite imagery

Question: Can recent satellite imagery of coarse spatial resolution support forest cover assessment and mapping at the regional level? Location: Continental southeast Asia. Methods: Forest cover mapping was based on digital classification of SPOT4‐VEGETATION satellite images of 1 km spatial resolution from the dry seasons 1998/1999 and 1999/2000. Following a geographical stratification, the spectral clusters were visually assigned to land cover classes. The forest classes were validated by an independent set of maps, derived from interpretation of satellite imagery of high spatial resolution (Landsat TM, 30 m). Forest area estimates from the regional forest cover map were compared to the forest figures of the FAO database. Results: The regional forest cover map displays 12 forest and land cover classes. The mapping of the region's deciduous and fragmented forest cover remained challenging. A high correlation was found between forest area estimates obtained from this map and from the Landsat TM derived maps. The regional and sub‐regional forest area estimates were close to those reported by FAO. Conclusion: SPOT4‐VEGETATION satellite imagery can be used for mapping consistently and uniformly the extent and distribution of the broad forest cover types at the regional scale. The new map can be considered as an update and improvement on existing regional forest cover maps.     

What is the Value of a Good Map? An Example Using High Spatial Resolution Imagery to Aid Riparian Restoration

Riparian areas contain structurally diverse habitats that are challenging to monitor routinely and accurately over broad areas. As the structural variability within riparian areas is often indiscernible using moderate-scale satellite imagery, new mapping techniques are needed. We used high spatial resolution satellite imagery from the QuickBird satellite to map harvested and intact forests in coastal British Columbia, Canada. We distinguished forest structural classes used in riparian restoration planning, each with different restoration costs. To assess the accuracy of high spatial resolution imagery relative to coarser imagery, we coarsened the pixel resolution of the image, repeated the classifications, and compared results. Accuracy assessments produced individual class accuracies ranging from 70 to 90% for most classes; whilst accuracies obtained using coarser scale imagery were lower. We also examined the implications of map error on riparian restoration budgets derived from our classified maps. To do so, we modified the confusion matrix to create a cost error matrix quantifying costs associated with misclassification. High spatial resolution satellite imagery can be useful for riparian mapping; however, errors in restoration budgets attributable to misclassification error can be significant, even when using highly accurate maps. As the spatial resolution of imagery increases, it will be used more routinely in ecosystem ecology. Thus, our ability to evaluate map accuracy in practical, meaningful ways must develop further. The cost error matrix is one method that can be adapted for conservation and planning decisions in many ecosystems.     

Classifying the Neotropical savannas of Belize using remote sensing and ground survey

Aim This paper evaluates a method of combining data from GPS ground survey with classifications of medium spatial resolution LANDSAT imagery to distinguish variations within Neotropical savannas and to characterize the boundaries between savanna areas and the associated gallery forests, seasonally dry forests and wetland communities. Location Rio Bravo Conservation Area, Orange Walk District, Belize, Central America. Methods Dry season LANDSAT data for 10 April 1993 and 9 March 2001 covering a conservation area of 240,000 acres (97,459 ha), were rectified to sub‐pixel accuracy using ground control points positioned by GPS ground survey. The 1993 image was used to assess the accuracy with which the boundaries between the savanna matrix and gallery forests, high forests, wetlands and water bodies could be discriminated. The image was classified by a maximum likelihood (ML) classifier and the shapes and areas of forest and wetland classes were compared with an interpretation of these land cover types from 1 : 24,000 aerial photography, mapped at 1 : 50,000 scale in 1993. The 2001 image was used to assess whether different subtypes of savanna could be distinguished from LANDSAT data. This required the creation of a reference (‘ground truth’) data set for testing classifications of the image. One hundred and sixty sample patches (650 ha, distributed over an area of 7000 ha) of ten sub‐types of savanna vegetation and associates identified using a physiognomic classification scheme, were delineated on the ground by GPS and divided into two subsets for training and testing. Continuous classifications of LANDSAT data covering the savannas were developed that estimated potential contributions from up to five sub‐types of land cover (grassland, wetland, pine woodland, gallery forest and palmetto). The accuracy of each classification was assessed by comparison against ground data. An ML classification was also produced for the 2001 image using the same areas for training. This allowed a comparison of the relative accuracy of both continuous and Boolean ML methods for classifying savanna areas. Results The boundary between savannas and evergreen forests, gallery forests and open water in the study region could be delineated by the ML classifier to within 2 pixels (60 m) using LANDSAT imagery. However, the constituent sub‐types within the savanna were poorly discriminated. Whilst the shape and extent of closed canopy forest, gallery forest, wetlands and water bodies agreed closely with the distributions interpreted from aerial photography, classes such as ‘open pine savanna’ or ‘grassland’ were only 45–65% accurate when tested against ground data. A continuous classification, estimating the proportions of three savanna vegetation subtypes (grassland, marshland and woodland) present in each pixel, correctly classified more of the ground data for these cover types than the comparable ML result. Proportional mixtures of the land cover estimated by the continuous classifier also compared realistically with the vegetation formations observed along ground transects. Main conclusions By using GPS, a ground survey of vegetation cover was accurately matched to remotely sensed imagery and the accuracy of delineating boundaries and classifying areas of savanna was assessed directly. This showed that ML classification techniques can reliably delineate the boundaries of savannas, but continuous classifiers more accurately and realistically represent the distribution of the subtypes comprising savanna land cover. By combining these ground survey and image classification methods, medium spatial resolution satellite sensor data can provide an affordable means for land managers to assess the nature, extent and distribution of savanna formations. Over time, using the archives of LANDSAT (and SPOT) data together with marker sites surveyed in the field, quantitative changes in the extents and boundaries of savannas in response to both natural (e.g. fire, hurricane and drought) and anthropogenic (e.g. cutting and disturbance) factors can be assessed.     

Monitoring temporal dynamics of Great Artesian Basin wetland vegetation,Australia, using MODIS NDVI

The Great Artesian Basin springs (Australia) are unique groundwater dependent wetland ecosystems of great significance, but are endangered by anthropogenic water extraction from the underlying aquifers. Relationships have been established between the wetland area associated with individual springs and their discharge, providing a potential means of monitoring groundwater flow using measurements of vegetated wetland area. Previous attempts to use this relationship to monitor GAB springs have used aerial photography or high resolution satellite images and gave sporadic temporal information. These “snapshot” studies need to be placed within a longer and more regular context to better assess changes in response to aquifer draw-downs. In this study we test the potential of 8 years of Moderate Resolution Imaging Spectroradiometer Normalised Difference Vegetation Index data as a long-term tracer of the temporal dynamics of wetland vegetation at the Dalhousie Springs Complex of the Great Artesian Basin. NDVI time series were extracted from MODIS images and phenologies of the main wetland vegetation species defined. Photosynthetic activity within wetlands could be discriminated from surrounding land responses in this medium resolution imagery. The study showed good correlation between wetland vegetated area and groundwater flow over the 2002–2010 period, but also the important influence of natural species phenologies, rainfall, and anthropogenic activity on the observed seasonal and inter-annual vegetation dynamics. Declining trends in the extent (km²) of vegetated wetland areas were observed between 2002 and 2009 followed by a return of wetland vegetation since 2010. This study underlines the need to continue long-term medium resolution satellite studies of the GAB to fully understand variability and trends in the spring-fed wetlands. The MODIS record allows a good understanding of variability within the wetlands, and gives a high temporal-frequency context for less frequent higher spatial resolution studies, therefore providing a strong baseline for assessment of future changes.     

Hyperspectral Analysis of Columbia Spotted Frog Habitat

Abstract: Wildlife managers increasingly are using remotely sensed imagery to improve habitat delineations and sampling strategies. Advances in remote sensing technology, such as hyperspectral imagery, provide more information than previously was available with multispectral sensors. We evaluated accuracy of high-resolution hyperspectral image classifications to identify wetlands and wetland habitat features important for Columbia spotted frogs (Rana luteiventris) and compared the results to multispectral image classification and United States Geological Survey topographic maps. The study area spanned 3 lake basins in the Salmon River Mountains, Idaho, USA. Hyperspectral data were collected with an airborne sensor on 30 June 2002 and on 8 July 2006. A 12-year comprehensive ground survey of the study area for Columbia spotted frog reproduction served as validation for image classifications. Hyperspectral image classification accuracy of wetlands was high, with a producer's accuracy of 96% (44 wetlands) correctly classified with the 2002 data and 89% (41 wetlands) correctly classified with the 2006 data. We applied habitat-based rules to delineate breeding habitat from other wetlands, and successfully predicted 74% (14 wetlands) of known breeding wetlands for the Columbia spotted frog. Emergent sedge microhabitat classification showed promise for directly predicting Columbia spotted frog egg mass locations within a wetland by correctly identifying 72% (23 of 32) of known locations. Our study indicates hyperspectral imagery can be an effective tool for mapping spotted frog breeding habitat in the selected mountain basins. We conclude that this technique has potential for improving site selection for inventory and monitoring programs conducted across similar wetland habitat and can be a useful tool for delineating wildlife habitats.     

Remote sensing imagery in vegetation mapping: a review

Aims Mapping vegetation through remotely sensed images involves various considerations, processes and techniques. Increasing availability of remotely sensed images due to the rapid advancement of remote sensing technology expands the horizon of our choices of imagery sources. Various sources of imagery are known for their differences in spectral, spatial, radioactive and temporal characteristics and thus are suitable for different purposes of vegetation mapping. Generally, it needs to develop a vegetation classification at first for classifying and mapping vegetation cover from remote sensed images either at a community level or species level. Then, correlations of the vegetation types (communities or species) within this classification system with discernible spectral characteristics of remote sensed imagery have to be identified. These spectral classes of the imagery are finally translated into the vegetation types in the image interpretation process, which is also called image processing. This paper presents an overview of how to use remote sensing imagery to classify and map vegetation cover.Methods Specifically, this paper focuses on the comparisons of popular remote sensing sensors, commonly adopted image processing methods and prevailing classification accuracy assessments.Important findings The basic concepts, available imagery sources and classification techniques of remote sensing imagery related to vegetation mapping were introduced, analyzed and compared. The advantages and limitations of using remote sensing imagery for vegetation cover mapping were provided to iterate the importance of thorough understanding of the related concepts and careful design of the technical procedures, which can be utilized to study vegetation cover from remote sensed images.     

Drug resistance to sulphadoxine-pyrimethamine in Plasmodium falciparum malaria in Mlimba, Tanzania

Background

In the past two decades the east African highlands have experienced several major malaria epidemics. Currently there is a renewed interest in exploring the possibility of anopheline larval control through environmental management or larvicide as an additional means of reducing malaria transmission in Africa. This study examined the landscape determinants of anopheline mosquito larval habitats and usefulness of remote sensing in identifying these habitats in western Kenya highlands.

Methods

Panchromatic aerial photos, Ikonos and Landsat Thematic Mapper 7 satellite images were acquired for a study area in Kakamega, western Kenya. Supervised classification of land-use and land-cover and visual identification of aquatic habitats were conducted. Ground survey of all aquatic habitats was conducted in the dry and rainy seasons in 2003. All habitats positive for anopheline larvae were identified. The retrieved data from the remote sensors were compared to the ground results on aquatic habitats and land-use. The probability of finding aquatic habitats and habitats with Anopheles larvae were modelled based on the digital elevation model and land-use types.

Results

The misclassification rate of land-cover types was 10.8% based on Ikonos imagery, 22.6% for panchromatic aerial photos and 39.2% for Landsat TM 7 imagery. The Ikonos image identified 40.6% of aquatic habitats, aerial photos identified 10.6%, and Landsate TM 7 image identified 0%. Computer models based on topographic features and land-cover information obtained from the Ikonos image yielded a misclassification rate of 20.3–22.7% for aquatic habitats, and 18.1–25.1% for anopheline-positive larval habitats.

Conclusion

One-metre spatial resolution Ikonos images combined with computer modelling based on topographic land-cover features are useful tools for identification of anopheline larval habitats, and they can be used to assist to malaria vector control in western Kenya highlands.     

Seeing the forest for the invasives: mapping buckthorn in the Oak Openings

Landsat TM and ETM+ satellite images from 2001 to 2011 were used to map the extent and change of the invasive shrubs common and glossy buckthorn (Frangula alnus and Rhamnus cathartica) at Irwin Prairie State Nature Preserve (IPSNP), and throughout Oak Openings, a 1,500 km² region, located in NW Ohio, USA and SE Michigan near Lake Erie. In the Oak Openings, buckthorn directly threatens native biodiversity and habitat health of this globally rare ecosystem. Buckthorn that forms as dense shrub thicket in the understory is often obscured from satellite view by other canopy and is not spectrally dissimilar enough to be characterized using multispectral images. To address this, time series tasseled cap greenness images of land surface areas dominated by buckthorn thicket (which exhibit early leaf-out, late senescence) was used to identify areas covered by thicket with a heterogeneous background. A time series of vegetation index values was calculated from 49 Landsat images and combined with in-situ observations to define the land surface phenology of buckthorn thicket and other vegetation types. The phenological differences among land surfaces dominated by distinct vegetation types in the Oak Openings Region were used to map the extent of buckthorn thicket using a supervised classification method. Buckthorn thicket was identified in 0.43 % of the classified pixels (940 ha) in the 2007–2011 imagery and in 0.31 % (690 ha) of the 2001–2006 images. A Kappa test of the 2007–2011 classification yielded a value of 0.73 with 88 % overall accuracy of presence or absence of thicket based on 60 samples throughout the Oak Openings. The areal extent of buckthorn thicket increased by 39 % (255 ha) in the study area over the time period from 2001 to 2011.     

High-Resolution Satellite Imagery Is an Important yet Underutilized Resource in Conservation Biology

Technological advances and increasing availability of high-resolution satellite imagery offer the potential for more accurate land cover classifications and pattern analyses, which could greatly improve the detection and quantification of land cover change for conservation. Such remotely-sensed products, however, are often expensive and difficult to acquire, which prohibits or reduces their use. We tested whether imagery of high spatial resolution (≤5 m) differs from lower-resolution imagery (≥30 m) in performance and extent of use for conservation applications. To assess performance, we classified land cover in a heterogeneous region of Interior Atlantic Forest in Paraguay, which has undergone recent and dramatic human-induced habitat loss and fragmentation. We used 4 m multispectral IKONOS and 30 m multispectral Landsat imagery and determined the extent to which resolution influenced the delineation of land cover classes and patch-level metrics. Higher-resolution imagery more accurately delineated cover classes, identified smaller patches, retained patch shape, and detected narrower, linear patches. To assess extent of use, we surveyed three conservation journals (Biological Conservation, Biotropica, Conservation Biology) and found limited application of high-resolution imagery in research, with only 26.8% of land cover studies analyzing satellite imagery, and of these studies only 10.4% used imagery ≤5 m resolution. Our results suggest that high-resolution imagery is warranted yet under-utilized in conservation research, but is needed to adequately monitor and evaluate forest loss and conversion, and to delineate potentially important stepping-stone fragments that may serve as corridors in a human-modified landscape. Greater access to low-cost, multiband, high-resolution satellite imagery would therefore greatly facilitate conservation management and decision-making.     

Numeric classification as an aid to spectral mapping of vegetation communities

This study demonstrates a vegetation mapping methodology that relates the reflectance information contained in multispectral imagery to traditionally accepted ecological classifications. Key elements of the approach used are (a) the use of cover rather than density or presence/absence to quantify the vegetation, (b) the inclusion of physical components as well as vegetation cover to describe and classify field sites, (c) development of an objective land cover classification from this quantitative data, (d) use of the field sample sites as training areas for the spectral classification, and (e) the use of a discriminant function to effectively tie the two classifications together. Land cover over 39000 ha of Australian chenopod shrubland was classified into nine groups using agglomerative hierarchical clustering, a discriminant function developed to relate cover and spectral classes, and the vegetation mapped using a maximum likelihood classification of multi-date Landsat TM imagery. The accuracy of the mapping was assessed with an independent set of field samples and by comparison with a map of land systems previously interpreted from aerial photography. Overall agreement between the digital classification and the land system map was good. The units that have been mapped are those derived from numeric vegetation classification, demonstrating that accepted ecological methods and sound image analysis can be successfully combined.     

Transferability of object-based rule sets for mapping coastal high marsh habitat among different regions in Georgian Bay,Canada

Coastal wetlands of eastern and northern Georgian Bay, Canada provide critical habitat for a variety of biota yet few have been delineated and mapped because of their widespread distribution and remoteness. This is an impediment to conservation efforts aimed at identifying significant habitat in the Laurentian Great Lakes. We propose to address this deficiency by developing an approach that relies on use of high-resolution remote sensing imagery to map wetland habitat. In this study, we use IKONOS satellite imagery to classify coastal high marsh vegetation (seasonally inundated) and assess the transferability of object-based rule sets among different regions in eastern Georgian Bay. We classified 24 wetlands in three separate satellite scenes and developed an object-based approach to map four habitat classes: emergent, meadow/shrub, senescent vegetation and rock. Independent rule sets were created for each scene and applied to the other images to empirically examine transferability at broad spatial scales. For a given habitat feature, the internally derived rule sets based on field data collected from the same scene provided significantly greater accuracy than those derived from a different scene (80.0 and 74.3%, respectively). Although we present a significant effect of ruleset origin on accuracy, the difference in accuracy is minimal at 5.7%. We argue that this should not detract from its transferability on a regional scale. We conclude that locally derived and object-based rule sets developed from IKONOS imagery can successfully classify complex vegetation classes and be applied to different regions without much loss of accuracy. This indicates that large–scale mapping automation may be feasible with images with similar spectral, spatial, contextual, and textural properties.     

GF1-WFV与Landsat8-OLI对植被信息的提取差异研究

随着遥感技术的不断发展,中高分辨率影像逐渐在植被监测中发挥着重要的作用。为了明确高分辨率传感器在不同生态系统植被提取中的必要性,以内蒙古鄂伦春自治旗为研究对象,设置城市区域和森林区域两个应用靶区,以GF1-WFV和Landsat8-OLI两种传感器同期影像为对比数据集,探究不同分辨率下遥感植被信息提取差异。结果表明:①Landsat 8对比GF-1在城市区域和森林区域的植被指数高估、低估状态相反,城市区域GF-1的NDVI(Normalized Difference Vegetation Index,NDVI)和SAVI(Soil-Adjusted Vegetation Index,SAVI)均值比Landsat 8低5.69%和1.41%,在森林区域则高出0.77%和5.86%;②高分辨率影像避免了城市中绿化植被(GF-1植被占比71.30%和71.31%,Landsat 8为58.28%和58.30%)和森林中裸地、道路(GF-1植被占比94.97%和94.92%,Landsat 8为95.00%和94.99%)被漏提。③在分级面积上,Landsat 8相比GF-1数据在城市区域存在低覆盖度等级的6.67%和6.77%低估,在森林区域出现高覆盖度等级的12.11%和12.47%高估。研究结果反映了低绿化建成区和高密度林区更加需要使用高分影像作为植被监测工具。