Publications

Analysis and representation of vegetation continua from Landsat Thematic Mapper data for lowland heaths

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.

Double down on remote sensing for biodiversity estimation: a biological mindset

In the light of unprecedented planetary changes in biodiversity, real-time and accurate ecosystem and biodiversity assessments are becoming increasingly essential for informing policy and sustainable development. Biodiversity monitoring is a challenge, especially for large areas such as entire continents. Nowadays, spaceborne and airborne sensors provide information that incorporate wavelengths that cannot be seen nor imagined with the human eye. This is also now accomplished at unprecedented spatial resolutions, defined by the pixel size of images, achieving less than a meter for some satellite images and just millimeters for airborne imagery. Thanks to different modeling techniques, it is now possible to study functional diversity changes over different spatial and temporal scales. At the heart of this unifying framework are the “spectral species”—sets of pixels with a similar spectral signal—and their variability over space. The aim of this paper is to summarize the power of remote sensing for directly estimating plant species diversity, particularly focusing on the spectral species concept.

Remote sensing of tropical forest environments: Towards the monitoring of environmental resources for sustainable development

The potential to derive indicators of sustainable resource use from satellite remote sensing is discussed. Particular attention focuses on indicators related to land cover condition and type in tropical forest environments. This includes the mapping of forest cover, estimation of biomass and biodiversity as well as the impacts of extreme events such as drought on the forest. Each of these issues is discussed with emphasis placed on the potential to increase the level of information extraction beyond that derived with conventional approaches in order to more usefully inform sustainable development practices.

Fuzzy classification for the mapping of disturbance in tropical forests

No abstract is provided for this article.

Land cover classification by support vector machine: towards efficient training

The accuracy of supervised classification is dependent to a large extent on the input training data. In general, the analyst aims to capture a large training set to fully describe the classes spectrally with the conventional statistical classifier in mind. However, it is not always necessary to provide a complete description of the classes if using a support vector machine (SVM) as the classifier. A key attraction of the SVM based approach to classification is that it seeks to fit an optimal hyperplane between the classes and since it uses only the training samples that lie at the edge of the class distributions in feature space (support vectors) it may require only a small training sample. The paper shows the potential of SVM of using only a fraction of the training data (support vectors) collected by the usual random scheme for a study carried in the south western part of Punjab state of India

Characterizing tropical forest regeneration in Cameroon using NOAA AVHRR data

To facilitate estimation of the carbon sink associated with tropical forests in Cameroon, regenerating and mature forests were mapped using an unsupervised classification of AVHRR channels 1, 2 and 3. Stages of regeneration were defined using nonlinear relationships between AVHRR channel 3 radiance and basal area, estimated using data collected from 183 plots (1 ha in size) in an area south-east of the capital, Yaounde. The overall extent and patterns of regenerating forest were comparable to those generated in previous studies. Older stages of regeneration could not, however, be discriminated adequately from mature forest, suggesting that areas of tropical forest disturbance may be underestimated when mapped using AVHRR data. closed tropical forests were regenerating and that their rate of expansion million ha y 1. These regenerating forests accumulate biomass more rapidly

Map comparison in GIS

No abstract is provided for this article.

Tackling large-scale problems by scaling-up

No abstract is provided for this article.

Fully fuzzy supervised classification of land cover from remotely sensed imagery with an artificial neural network

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

One class classification for EU priority habitats monitoring

No abstract is provided for this article.

Exploring the utility of NOAA AVHRR middle infrared reflectance to monitor the impacts of ENSO-induced drought stress on Sabah rainforests

The ever-wet tropics are under threat from ENSO events and there is a need for a monitoring system to analyse and describe their responses to such events. This letter explores the relative value of using NOAA AVHRR middle infrared (MIR) reflectance data and NDVI data for the monitoring of ENSO-related drought stress of a tropical forest ecosystem in Sabah, Malaysia. Relationships between rainfall and MIR reflectance were examined. Correlation coefficients are generally large and significant (at 0.1 level) while those between rainfall and NDVI were small and insignificant. This letter concludes that there is potential in using MIR reflectance for monitoring the effects of ENSO-induced drought stress on these forests and this has a bearing on how NOAA AVHRR data may be used to further our knowledge on the impacts of ENSO events on tropical forest environments.

The role of soft classification techniques in the refinement of estimates of ground control point location

Mis-registration of data sets is one of the largest sources of error in many remote sensing studies. An initial contribution to this error arises through the mis-location of ground control points (GCPs) used to derive geometrical transformation equations. Here, it is proposed that a soft classification of land cover may be used to direct the estimation of GCP location. The soft classification provides an estimate of the class composition of each image pixel. The spatial distribution of a pixel's component land covers may then be modeled over the area it represents and used to reduce the error in estimating the location of a GCP that lies within this area. An example is provided in which the error in locating a set of GCPs was reduced by up to 35.7 percent when information from a soft classification was available to aid the estimation of their location at a sub-pixel scale.

Measuring River Wetted Width From Remotely Sensed Imagery at the Subpixel Scale With a Deep Convolutional Neural Network

River wetted width (RWW) is an important variable in the study of river hydrological and biogeochemical processes. Presently, RWW is often measured from remotely sensed imagery, and the accuracy of RWW estimation is typically low when coarse spatial resolution imagery is used because river boundaries often run through pixels that represent a region that is a mixture of water and land. Thus, when conventional hard classification methods are used in the estimation of RWW, the mixed pixel problem can become a large source of error. To address this problem, this paper proposes a novel approach to measure RWW at the subpixel scale. Spectral unmixing is first applied to the imagery to obtain a water fraction image that indicates the proportional coverage of water in image pixels. A fine spatial resolution river map from which RWW may be estimated is then produced from the water fraction image by superresolution mapping (SRM). In the SRM analysis, a deep convolutional neural network is used to eliminate the negative effects of water fraction errors and reconstruct the geographical distribution of water. The proposed approach is assessed in two experiments, with the results demonstrating that the convolutional neural network‐based SRM model can effectively estimate subpixel scale details of rivers and that the accuracy of RWW estimation is substantially higher than that obtained from the use of a conventional hard image classification. The improvement shows that the proposed method has great potential to derive more accurate RWW values from remotely sensed imagery.

Land Cover Mapping from Remotely Sensed Data with a Neural Network: Accommodating Fuzziness

Neural networks are attractive for the supervised classification of remotely sensed data. There are, however, many problems with their use, restricting the realisation of their full potential. This article focuses on the accommodation of fuzziness in the...

Spatial-temporal fraction map fusion with multi-scale remotely sensed images

Given the common trade-off between the spatial and temporal resolutions of current satellite sensors, spatial-temporal data fusion methods could be applied to produce fused remotely sensed data with synthetic fine spatial resolution (FR) and high repeat frequency. Such fused data are required to provide a comprehensive understanding of Earth's surface land cover dynamics. In this research, a novel Spatial-Temporal Fraction Map Fusion (STFMF) model is proposed to produce a series of fine-spatial-temporal-resolution land cover fraction maps by fusing coarse-spatial-fine-temporal and fine-spatial-coarse-temporal fraction maps, which may be generated from multi-scale remotely sensed images. The STFMF has two main stages. First, FR fraction change maps are generated using kernel ridge regression. Second, a FR fraction map for the date of prediction is predicted using a temporal-weighted fusion model. In comparison to two established spatial-temporal fusion methods of spatial-temporal super-resolution land cover mapping model and spatial-temporal image reflectance fusion model, STFMF holds the following characteristics and advantages: (1) it takes account of the mixed pixel problem in FR remotely sensed images; (2) it directly uses the fraction maps as input, which could be generated from a range of satellite images or other suitable data sources; (3) it focuses on the estimation of fraction changes happened through time and can predict the land cover change more accurately. Experiments using synthetic multi-scale fraction maps simulated from Google Earth images, as well as synthetic and real MODIS-Landsat images were undertaken to test the performance of the proposed STFMF approach against two benchmark spatial-temporal reflectance fusion methods: the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) and the Flexible Spatiotemporal Data Fusion (FSDAF) model. In both visual and quantitative evaluations, STFMF was able to generate more accurate FR fraction maps and provide more spatial detail than ESTARFM and FSDAF, particularly in areas with substantial land cover changes. STFMF has great potential to produce accurate time-series fraction maps with fine-spatial-temporal-resolution that can support studies of land cover dynamics at the sub-pixel scale.

Classification accuracy comparison: Hypothesis tests and the use of confidence intervals in evaluations of difference, equivalence and non-inferiority

The comparison of classification accuracy statements has generally been based upon tests of difference or inequality when other scenarios and approaches may be more appropriate. Procedures for evaluating two scenarios with interest focused on the similarity in accuracy values, non-inferiority and equivalence, are outlined following a discussion of tests of difference (inequality). It is also suggested that the confidence interval of the difference in classification accuracy may be used as well as or instead of conventional hypothesis testing to reveal more information about the disparity in the classification accuracy values compared.

Estimating the biomass of South East Asian forests from Landsat TM data

The Future of VGI

In this final chapter, we speculate on future developments in the field of Volunteered Geographic Information (VGI); we focus on how VGI will be affected by future technological developments, but we also consider issues such as VGI