No abstract is provided for this article.
The species-area curve has been used to link the biological with the geographical. Larger areas of land would seem to contain more species as a result of both the effect of sampling (i.e. more samples are taken to represent larger areas) and ecological processes (i.e. island biogeography theory and hypotheses relating to habitat diversity, successional development, species-energy, target-area, incidence function, small island habitat and disturbance). Unfortunately, the species-area curve is usually interpreted as though it was due entirely to ecological processes when it could be due largely to sampling. Modelled and real data (for forests in Ghana) demonstrated that while the effect of both ecological processes alone and sampling alone increased species number with area, only ecological processes could be expected to increase the number of species per unit area. These results suggest that before a species-area curve could be used as an indicator of ecological processes the effect of sampling on the species-area curve must first be removed.
Studies of land cover dynamics would benefit greatly from the generation of land cover maps at both fine spatial and temporal resolutions. Fine spatial resolution images are usually acquired relatively infrequently, whereas coarse spatial resolution images may be acquired with a high repetition rate but may not capture the spatial detail of the land cover mosaic of the region of interest. Traditional image spatial–temporal fusion methods focus on the blending of pixel spectra reflectance values and do not directly provide land cover maps or information on land cover dynamics. In this research, a novel Spatial–Temporal remotely sensed Images and land cover Maps Fusion Model (STIMFM) is proposed to produce land cover maps at both fine spatial and temporal resolutions using a series of coarse spatial resolution images together with a few fine spatial resolution land cover maps that pre- and post-date the series of coarse spatial resolution images. STIMFM integrates both the spatial and temporal dependences of fine spatial resolution pixels and outputs a series of fine spatial–temporal resolution land cover maps instead of reflectance images, which can be used directly for studies of land cover dynamics. Here, three experiments based on simulated and real remotely sensed images were undertaken to evaluate the STIMFM for studies of land cover change. These experiments included comparative assessment of methods based on single-date image such as the super-resolution approaches (e.g., pixel swapping-based super-resolution mapping) and the state-of-the-art spatial–temporal fusion approach that used the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) and the Flexible Spatiotemporal DAta Fusion model (FSDAF) to predict the fine-resolution images, in which the maximum likelihood classifier and the automated land cover updating approach based on integrated change detection and classification method were then applied to generate the fine-resolution land cover maps. Results show that the methods based on single-date image failed to predict the pixels of changed and unchanged land cover with high accuracy. The land cover maps that were obtained by classification of the reflectance images outputted from ESTARFM and FSDAF contained substantial misclassification, and the classification accuracy was lower for pixels of changed land cover than for pixels of unchanged land cover. In addition, STIMFM predicted fine spatial–temporal resolution land cover maps from a series of Landsat images and a few Google Earth images, to which ESTARFM and FSDAF that require correlation in reflectance bands in coarse and fine images cannot be applied. Notably, STIMFM generated higher accuracy for pixels of both changed and unchanged land cover in comparison with other methods.
Super-resolution mapping is used to produces thematic maps at a scale finer than the source images. This paper presents a new super-resolution mapping approach that exploits the typically fine temporal resolution of coarse spatial resolution images as it input and an adoption of an active threshold surface using Hopfield neural network as a means to map land cover at a sub-pixel scale. The results demonstrated that the proposed technique is slightly more accurate than the existence technique in terms of site specific accuracy and produce better visualization on individual land cover map.
. It is now widely accepted that an accuracy assessment should be part of a thematic mapping programme. Authoritative good or best practices for accuracy assessment have been defined but are often impractical to implement. Key reasons for this situation are linked to the ground reference data used in the accuracy assessment. Typically, it is a challenge to acquire a large sample of high quality reference cases in accordance to desired sampling designs specified as conforming to good practice and the data collected are normally to some degree imperfect limiting their value to an accuracy assessment which implicitly assumes the use of a gold standard reference. Citizen sensors have great potential to aid aspects of accuracy assessment. In particular, they may be able to act as a source of ground reference data that may, for example, reduce sample size problems but concerns with data quality remain. The relative strengths and limitations of citizen contributed data for accuracy assessment are reviewed in the context of the authoritative good practices defined for studies of land cover by remote sensing. The article will highlight some of the ways that citizen contributed data have been used in accuracy assessment as well as some of the problems that require further attention, and indicate some of the potential ways forward in the future.
This paper presents a super-resolution mapping technique as a means to gain accurate information of land cover, and especially its spatial pattern, at a sub-pixel scale. This technique extends the application of an established Hopfield neural network of super-resolution mapping technique by providing its input with a fusion of a time series coarse spatial but fine temporal resolution images. To illustrate this technique, a series of daily MODIS 250m images was acquired and fused. Using a Landsat ETM+ 30m image as ground data, results demonstrated that a Hopfield network that uses time series information produces significantly more accurate representation of land cover mapping in terms of thematic accuracy and spatial pattern prediction than by using a single image into Hopfield network or into hard classification techniques.
Abstract Many methods of analysing remotely sensed data assume that pixels are pure, and so a failure to accommodate mixed pixels may result in significant errors in data interpretation and analysis. The analysis of data containing a large proportion of mixed pixels may therefore benefit from the decomposition of the pixels into their component parts. Methods for unmixing the composition of pixels have been used in a range of studies and have often increased the accuracy of the analyses. However, many of the methods assume linear mixing and require end-member spectra, but mixing is often non-linear and end-member spectra are difficult to obtain. In this paper, an alternative approach to unmixing the composition of image pixels, which makes no assumptions about the nature of the mixing and does not require end-member spectra, is presented. The method is based on an artificial neural network (ANN) and shown in a case study to provide accurate estimates of sub-pixel land cover composition. The results of this case study showed that accurate estimates of the proportional cover of a class and its areal extent may be made. It was also shown that there was a tendency for the accuracy of the unmixing to increase with the complexity of the network and the intensity of training. The results indicate the potential to derive accurate information from remotely sensed data sets dominated by mixed pixels.
The spectral separability of twelve tropical rain-forest classes was examined in Landsat Thematic Mapper (TM∥ imagery of the Tambopata-Candamo Reserved Zone, south-east Peru. Spatial filtering of the imagery increased inter-class separability, although spectral overlap between the twelve forest classes was such that only four broad forest groups could be separated. These four spectrally separable forest groups appeared to differ in terms of structure and crown characteristics.
Spatio-temporal image fusion methods have become a popular means to produce remotely sensed data sets that have both fine spatial and temporal resolution. Accurate prediction of reflectance change is difficult, especially when the change is caused by both phenological change and land cover class changes. Although several spatio-temporal fusion methods such as the Flexible Spatiotemporal DAta Fusion (FSDAF) directly derive land cover phenological change information (such as endmember change) at different dates, the direct derivation of land cover class change information is challenging. In this paper, an enhanced FSDAF that incorporates sub-pixel class fraction change information (SFSDAF) is proposed. By directly deriving the sub-pixel land cover class fraction change information the proposed method allows accurate prediction even for heterogeneous regions that undergo a land cover class change. In particular, SFSDAF directly derives fine spatial resolution endmember change and class fraction change at the date of the observed image pair and the date of prediction, which can help identify image reflectance change resulting from different sources. SFSDAF predicts a fine resolution image at the time of acquisition of coarse resolution images using only one prior coarse and fine resolution image pair, and accommodates variations in reflectance due to both natural fluctuations in class spectral response (e.g. due to phenology) and land cover class change. The method is illustrated using degraded and real images and compared against three established spatio-temporal methods. The results show that the SFSDAF produced the least blurred images and the most accurate predictions of fine resolution reflectance values, especially for regions of heterogeneous landscape and regions that undergo some land cover class change. Consequently, the SFSDAF has considerable potential in monitoring Earth surface dynamics.
Mixed pixels are a major problem in mapping land cover from remotely sensed imagery. Unfortunately, such imagery may be dominated by mixed pixels, and the
No abstract is provided for this article.
Big data has now become a strong focus of global interest that is increasingly attracting the attention of academia, industry, government and other organizations. Big data can be situated in the disciplinary area of traditional geospatial data handling theory and methods. The increasing volume and varying format of collected geospatial big data presents challenges in storing, managing, processing, analyzing, visualizing and verifying the quality of data. This has implications for the quality of decisions made with big data. Consequently, this position paper of the International Society for Photogrammetry and Remote Sensing (ISPRS) Technical Commission II (TC II) revisits the existing geospatial data handling methods and theories to determine if they are still capable of handling emerging geospatial big data. Further, the paper synthesises problems, major issues and challenges with current developments as well as recommending what needs to be developed further in the near future. Keywords: Big data, Geospatial, Data handling, Analytics, Spatial Modeling, Review
Google Earth Engine (GEE) provides a convenient platform for applications based on optical satellite imagery of large areas. With such data sets, the detection of cloud is often a necessary prerequisite step. Recently, deep learning-based cloud detection methods have shown their potential for cloud detection but they can only be applied locally, leading to inefficient data downloading time and storage problems. This letter proposes a method to directly perform cloud detection in Landsat-8 imagery in GEE based on deep learning (DeepGEE-CD). A deep convolutional neural network (DCNN) was first trained locally, and then the trained DCNN was deployed in the JavaScript client of GEE. An experiment was undertaken to validate the proposed method with a set of Landsat-8 images and the results show that DeepGEE-CD outperformed the widely used function of mask (Fmask) algorithm. The proposed DeepGEE-CD approach can accurately detect cloud in Landsat-8 imagery without downloading it, making it a promising method for routine cloud detection of Landsat-8 imagery in GEE.
Conventional classification of remotely-sensed data seeks to allocate observations into distinct classes. For many regions, such as lowland heaths, this approach may be inappropriate since the vegetation types lie along continua and do not exist in a well-defined mosaic. In conditions like these the adoption of ordinatory techniques may be preferable to those of classification and the results may be displayed as probability surfaces. This paper presents the results of a preliminary investigation which demonstrates the use of such an approach for heathland in Surrey.
