Spatial Resolution Land Cover Map Research Articles

2010 land cover map of insular Southeast Asia in 250-m spatial resolution

In this letter, we present the methodology and accuracy assessment of a new regional 250-m spatial resolution land cover map of insular Southeast Asia. Nearly 500 daily Moderate Resolution Imaging Spectroradiometer images (acquired 2 January–3 July 2010) were used in the production of the map. Additionally, peatland maps, elevation information and Daichi-Advanced Land Observing Satellite mosaic data were utilized in the mapping process. The map covers the Malaysian Peninsula and the major islands of Sumatra, Java, Borneo, Sulawesi and Mindanao, in addition to the western part of New Guinea and numerous smaller islands. The classification scheme of 13 classes reflects the special characteristics of land cover of insular Southeast Asia, including, for example, peatswamp forests and large-scale palm plantations. The map was assessed to have 85% overall accuracy using high-resolution (10–30 m) satellite data as reference material. It can therefore be considered to provide reliable information on the current status of land cover in this region experiencing rapid land cover changes.

Possibilities and limitations of artificial neural networks for subpixel mapping of land cover

Although developments in remote sensing have greatly improved land cover mapping, the mixed pixel problem has not yet been fully addressed. Soft classification techniques have been introduced to address the problem, but they do not show the spatial location of the class proportions in a pixel. Subpixel mapping has been introduced to address the drawbacks of soft classifications. In this work, the feedforward backpropagating neural network (FFBPNN) was used for subpixel mapping. A set of class proportion images, which are to be treated as soft classification results, were created from a high spatial resolution (25 m) land cover dataset. For this purpose, the land cover dataset was aggregated both thematically (into two, four or eight land cover classes) and spatially (into proportion images with pixel sizes of 75, 150 and 300 m). This resulted in nine different combinations that were considered here as study cases. Several FFBPNNs were trained using these proportion images and the original land cover dataset (which was used as a target). Subsequently, the best networks were used to reconstruct high spatial resolution land cover maps of two heterogeneous areas in the south of The Netherlands. The overall accuracies obtained revealed that the networks were influenced by the spatial frequency, shape and size of the different land cover types. Moreover, it was revealed that most of the errors were on the class boundaries where highly mixed pixels are to be expected. The accuracies spanned a wide range of values depending on the complexity of the cases. Although it was not possible to exhaustively explore all network architectures, the results demonstrate the potential of the FFBPNN for subpixel mapping.

Forest fragmentation, structure, and age characteristics as a legacy of forest management

The combination of forest inventory and satellite-derived landscape composition and structure provides otherwise unavailable information on regional forest conditions and enables investigation of the cumulative effects of forest management over time. Forest pattern results from a range of both natural and anthropogenic factors. This study characterizes the forest pattern of Vancouver Island, British Columbia (>32,000 km 2) at the watershed scale, using new national datasets of Canadian forest composition and fragmentation, and relating these to forest inventory-derived age structure. Vancouver Island is extensively forested, possessing highly valuable and productive forests, and is managed to meet a range of stakeholder interests. Forest fragmentation metrics were derived from a satellite-derived, 25-m spatial resolution land cover map (i.e., grain) to represent patterns within 1 km analysis units (i.e., extent) for the entire forested area of Canada. We summarized forest fragmentation island-wide and compared trends between forest-dominated (>85% forest) and less-forested (<85% forest) watersheds. We also explored these patterns with partial canonical correspondence analyses to determine the independent and shared relationships of landscape composition, forest fragmentation, and spatial variables with forest age structure. Less-forested watersheds are more fragmented than forest-dominated watersheds, as indicated by more (5.6 versus 2.7 per km 2) and smaller (36 versus 63 ha) forest patches, and a greater edge density (82 versus 55 m/ha). Of the 1283 watersheds examined, 91% were forest-dominated; island-wide trends are thus similar to those of the forest-dominated watersheds. Forest age is related to landscape composition and forest fragmentation, which collectively explain 27% and 53% of the age structure of all and less-forested watersheds, respectively. In both sets of watersheds, young forest stands (1–120 years) are associated with broadleaf forests, agreeing with successional expectations, and patchy forest distributions. For all watersheds, old growth forest stands (>240 years) are associated with dense coniferous forest, but also with early successional communities, smaller patch sizes, and greater edge densities, indicating fragmentation of these forests and reflecting strategies for managing old growth on the landscape. In contrast, watersheds with an abundance of mature forest (121–240 years) are compositionally similar to watersheds with old growth, but are much less fragmented. Our results indicate that although old growth forest stands on Vancouver Island have been retained, they are not typically found within a continuous forest matrix. Medium spatial resolution Earth observation products are now available for the entire forested area of Canada, providing valuable insight into spatial and temporal forest dynamics (i.e., succession, harvesting).

High Spatial Resolution Land Cover Mapping Using Remotely Sensed Image

Land cover classification from remotely sensed data is an important topic in remote sensing applications. This paper present an economical analysis of land covers in Mebok Estuary, Penang, Malaysia. This study investigated the potential of using digital camera for land cover mapping Mebok Estuary, Penang, Malaysia. Airborne digital imagery has proved to be an effective tool for land cover studies. A Kodak camera, model DC290, was used to capture images from an elevation of 2438.4 meter on board Cessna 172Q. The use of digital camera as a sensor to capture digital images is cheaper and economical compared to the use of other airborne sensor. This technique overcomes the problem of the difficulty in obtaining cloud-free scenes in the Equatorial region from a satellite platform. Supervised classification techniques were used in the classification analysis. Supervised classification technique (Parallelepiped with Maximum Likelihood as tie breaker classifier, PML, Maximum Likelihood, ML, Minimum Distance-to-Mean, MDM, and Parallelepiped, P) was applied to the digital camera spectral bands (red, green and blue) to extract the thematic information from the acquired scenes. The best supervised classifier was chosen based on the highest overall accuracy and Kappa statistic. The accuracy of the classified images was validated using a reference data set. The study revealed that the Parallelepiped with Maximum Likelihood as tie breaker classifier produced superior result and achieved a high degree of accuracy. The classified land cover map was geometrically corrected to provide a geocode map. The results produced by this study indicated that land cover features could be clearly identified and classified into a land cover map. This study suggested that the land cover types of Mebok Estuary, Penang, Malaysia can be accurately mapped.

Effects of cutline density and land-cover heterogeneity on landscape metrics in western Alberta

Forest cutlines are narrow, linear features created in geophysical surveys. In many areas of Canada, forest cutlines are not consistently detected using relatively coarse spatial resolution land-cover maps, such as those produced by classification of Landsat Thematic Mapper (TM) imagery. However, such features may be important in certain wildlife management applications, including those which require an assessment of landscape structure, or forest fragmentation, at various scales. Higher spatial resolution satellite imagery obtained from sensors on platforms such as Satellite Pour l’Observation de la Terre (SPOT) and the Indian Remote Sensing (IRS) system may be used to map forest cutlines for these applications. In this study, a TM-based land-cover map of western Alberta is analyzed with forest cutlines mapped from a TM-IRS fusion image, and the effect of increasing cutline density is quantified on five commonly used landscape metrics used to characterize landscape structure in grizzly bear habitat assessment. The accuracy of the fusion image interpretation was determined to be 88%. Simulated landscapes were tested first, and the study area was divided into 104 hexagon-shaped sample landscapes of about 6 km diameter each. Across these sample landscapes, cutline density and initial landscape heterogeneity were significant parameters in explaining change in three metrics, namely edge density, mean patch size, and patch context (expressed as mean nearest-neighbour distance). Patch size variability (expressed as the coefficient of variation of mean patch size) and patch dispersion (expressed as the coefficient of variation of mean nearest-neighbour distance) required additional information on cutline positioning. Overall, the density of the introduced cutline network and the pre-cutline metric value reliably predicted and quantified the response of landscape metrics of interest to grizzly bear biologists. This study shows the importance of mapping forest cutlines regarding their role in changing landscape structure quantification and points out the necessity of using additional remotely sensed data when the feature responsible for the landscape transformation is of too small a size to appear reliably in common TM-based classified imagery.

National Park vegetation mapping using multitemporal Landsat 7 data and a decision tree classifier

Decision tree classifiers have received much recent attention, particularly with regards to land cover classifications at continental to global scales. Despite their many benefits and general flexibility, the use of decision trees with high spatial resolution data has not yet been fully explored. In support of the National Park Service (NPS) Vegetation Mapping Program (VMP), we have examined the feasibility of using a commercially available decision tree classifier with multitemporal satellite data from the Enhanced Thematic Mapper-Plus (ETM+) instrument to map 11 land cover types at the Delaware Water Gap National Recreation Area near Milford, PA. Ensemble techniques such as boosting and consensus filtering of the training data were used to improve both the quality of the input training data as well as the final products. Using land cover classes as specified by the National Vegetation Classification Standard at the Formation level, the final land cover map has an overall accuracy of 82% ( κ=0.80) when tested against a validation data set acquired on the ground ( n=195). This same accuracy is 99.5% when considering only forest vs. nonforest classes. Usage of ETM+ scenes acquired at multiple dates improves the accuracy over the use of a single date, particularly for the different forest types. These results demonstrate the potential applicability and usability of such an approach to the entire National Park system, and to high spatial resolution land cover and forest mapping applications in general.

A new SPOT4-VEGETATION derived land cover map of Northern Eurasia

The European Commission's Joint Research Centre and the Russian Academy of Science's Centre for Forest Ecology and Productivity have produced a new 1 km spatial resolution land cover map of Eurasia from 1999 SPOT4-VEGETATION data. The legend is designed to serve users from science programmes, policy makers, environmental convention secretariats, non-governmental organizations, development-aid projects and the national forest service. The 1999 map is also being updated as part of an international exercise to map Global Land Cover for the year 2000. This Letter describes the map legend, the image classification method, the map accuracy assessment process and presents the land cover map.