Land cover mapping via image classification is sometimes realized through object-based image analysis. Objects are typically constructed by partitioning imagery into spatially contiguous groups of pixels through image segmentation and used as the basic spatial unit of analysis. As it is typically desirable to know the accuracy with which the objects have been delimited prior to undertaking the classification, numerous methods have been used for accuracy assessment. This paper reviews the state-of-the-art of image segmentation accuracy assessment in land cover mapping applications. First the literature published in three major remote sensing journals during 2014–2015 is reviewed to provide an overview of the field. This revealed that qualitative assessment based on visual interpretation was a widely-used method, but a range of quantitative approaches is available. In particular, the empirical discrepancy or supervised methods that use reference data for assessment are thoroughly reviewed as they were the most frequently used approach in the literature surveyed. Supervised methods are grouped into two main categories, geometric and non-geometric, and are translated here to a common notation which enables them to be coherently and unambiguously described. Some key considerations on method selection for land cover mapping applications are provided, and some research needs are discussed.
The impacts of intra-class spectral variation on the use of soft classification outputs for super-resolution mapping was assessed. The accuracy of soft classification and super- resolution mapping was negatively related to the degree of intra-class spectral variation present in the data set. The provision of a distribution of possible sub-pixel fractional covers from a soft classification may reflect the impacts of intra-class variation and help to enhance super-resolution mapping. A possible approach to reduce the impacts of intra-class spectral variation was investigated. This was based on an approach that reduces the degree of intra-class spectral variation by defining spectral subclasses for use in the soft classification. The use of this approach increased the accuracy of soft classification predictions from r = 0.87 to r = 0.94 and decreased the RMSE in super-resolution mapping of an inter-class boundary from 44.7 m to 37.2 m. The results highlighted that reducing intra-class spectral variation may be used to increase the accuracy of soft classification and super-resolution mapping.
Area estimation is a common application of remote sensing especially in relation to studies of land cover change. The use of an imperfect, nongold-standard, reference is shown to be a source of substantial error in estimates of change area. The relationships between the accuracy of land cover classifications, both real and perceived, together with area of change are explored over the full range of change abundance. The magnitude of mis-estimation varies with the abundance of change and the quality of the data sets used but may be large. For scenarios based on realistic values of data set quality for the common situation in which change is rare, ≤1%, change area was overestimated by up to two orders of magnitude because of reference data error. The results highlight that the ability of remote sensing to provide accurate information on change area is limited by reference data error and is, in part, a function of change abundance.
The impact of intra-class spectral variability on the estimation of sub-pixel land-cover class composition with a linear mixture model is explored. It is shown that the nature of intra-class variation present has a marked impact on the accuracy of sub-pixel class composition estimation, as it violates the assumption that a class can be represented by a single spectral endmember. It is suggested that a distribution of possible class compositions can be derived from pixels instead of a single class composition prediction. This distribution provides a richer indication of possible subpixel class compositions and highlights a limitation for super-resolution mapping. Moreover, the class composition distribution information may be used to derive different scenarios of changes when used in a post-classification comparison type approach to change detection. This latter issue is illustrated with an example of forest cover change in Brazil from Landsat TM data.
The recent rise of neogeography and citizen sensing has increased the opportunities for the use of crowdsourcing as a means to acquire data to support geographical research. The value of the resulting volunteered geographic information is, however, often limited by concerns associated with its quality and the degree to which the contributing data sources may be trusted. Here, information on the quality of sources of volunteered geographic information was derived using a latent class analysis. The volunteered information was on land cover interpreted visually from satellite sensor images and the main focus was on the labeling of 299 sites by seven of the 65 volunteers who contributed to an Internet‐based collaborative project. Using the information on land cover acquired by the multiple volunteers it was shown that the relative, but not absolute, quality of the data from different volunteers could be characterized accurately. Additionally, class‐specific variations in the quality of the information provided by a single volunteer could be characterized by the analysis. The latent class analysis, therefore, was able to provide information on the quality of information provided on an inter‐ and intra‐volunteer basis.
Partial table of contents: Environmental Issues at Regional to Global Scales (P. Curran & G. Foody). Explaining and Monitoring Land Cover Dynamics in Drylands Using Multi-Temporal Analysis of NOAA AVHRR Imagery (A. Millington, et al.). Global Land Cover: Comparison of Ground-Based Data Sets to Classifications with AVHRR Data (R. DeFries & J. Townshend). A Near-Real-Time Heat Source Monitoring System Using NOAA Polar Orbiting Meteorological Satellites (G. Smith & R. Vaughan). Attempts to Drive Ecosystem Simulation Models at Local to Regional Scales (P. Curran). Environmental Monitoring Using Multiple-View-Angle (MVA) Remotely-Sensed Data (M. Barnsley). Spatial Data: Data Types, Data Applications and Reasons for Partial Adoption and Non-Integration (J. Allan). Index.
No abstract is provided for this article.
Landscape diversity generally relates to species diversity at a range of ecological levels like species community diversity and genetic diversity. Species–based measures of diversity like species richness or species turnover are the most commonly used metrics for quantifying the diversity of an area. Nonetheless, the assessment of species diversity in relatively large areas has always been a challenging task for ecologists, mainly because of the intrinsic difficulty in judging the completeness of species lists and in quantifying the sampling effort (Palmer et al., 2002). Since the variability in the remotely sensed signal is expected to be related to landscape diversity, it could be used as a good proxy of diversity at species level. \nIt has been demonstrated that the relation between species diversity and landscape heterogeneity measured from remotely sensed data or land use maps varies with scale. However, Free and Open Source tools (allowing an access to the source code, see Rocchini and Neteler, 2012) for assessing landscape heterogeneity at different spatial scales are still lacking today. In this paper, we aim at: i) providing a theoretical background of the mostly used diversity indices stemmed from information theory that are commonly applied to quantify landscape heterogeneity from remotely sensed data and ii) proposing a free and robust Open Source tool (r.diversity) with its source code for calculating diversity indices (and allowing an easy potential implementation of new metrics by multiple contributors globally) at different spatial scales from remotely-sensed imagery or land use maps, running under the widely used Open Source program GRASS GIS. \nr.diversity can be a valuable tool for calculating landscape heterogeneity in an Open Source space, on the strength of its major advantages like: i) the availability of multiple indices at a time and ii) the possibility to create new indices directly reusing the code, iii) the possibility to calculate landscape heterogeneity at multiple spatial scales in an explicit way based on varying moving windows thus iv) reducing the problems of hidden patterns of the relation between field- and landscape-based diversity due to scale mismatch. \nWe expect that the theme proposed in this paper will stimulate discussions on the opportunities offered by Free and Open Source Software to calculate landscape diversity.
The surface urban heat island (SUHI) effect poses a significant threat to the urban environment and public health. This paper utilized the Local Climate Zone (LCZ) classification and land surface temperature (LST) data to analyze the seasonal dynamics of SUHI in Wuhan based on the Google Earth Engine platform. In addition, the SUHI intensity derived from the traditional urban-rural dichotomy was also calculated for comparison. Seasonal SUHI analysis showed that (1) both LCZ classification and the urban-rural dichotomy confirmed that Wuhan's SHUI effect was the strongest in summer, followed by spring, autumn and winter; (2) the maximum SUHI intensity derived from LCZ classification reached 6.53 °C, which indicated that the SUHI effect was very significant in Wuhan; (3) LCZ 8 (i.e., large low-rise) had the maximum LST value and LCZ G (i.e., water) had the minimum LST value in all seasons; (4) the LST values of compact high-rise/midrise/low-rise (i.e., LCZ 1-3) were higher than those of open high-rise/midrise/low-rise (i.e., LCZ 4-6) in all seasons, which indicated that building density had a positive correlation with LST; (5) the LST values of dense trees (i.e., LCZ A) were less than those of scattered trees (i.e., LCZ B) in all seasons, which indicated that vegetation density had a negative correlation with LST. This paper provides some useful information for urban planning and contributes to the healthy and sustainable development of Wuhan.
Information on the temporal variation of surface water area of reservoirs is fundamental for water resource management and is often monitored by satellite remote sensing. Moderate Resolution Imaging Spectroradiometer (MODIS) imagery is an attractive data source for the routine monitoring of reservoirs, however, the accuracy is often limited due to the negative impacts associated with its coarse spatial resolution and the effects of cloud contamination. Methods have been proposed to solve these two problems independently but it remains challenging to address both problems simultaneously. To overcome this, this paper proposes a new approach that aims to monitor reservoir surface water area variations accurately and timely from daily MODIS images by exploring sub-pixel scale information. The proposed approach used estimates of reservoir water areas obtained from cloud-free and relatively fine spatial resolution Landsat images and water fraction images by spectral unmixing of coarse MODIS imagery as reference data. For each MODIS pixel, these reference reservoir water areas and their corresponding pixel water fractions were used to construct a linear regression equation, which in turn may be applied to predict the time series of reservoir water areas from daily MODIS water fraction images. The proposed approach was assessed with 21 reservoirs, where the correlation coefficients between reservoir water areas predicted by the common pixel-based analysis method and altimetry water levels were all less than 0.5. With the proposed sub-pixel analysis method, the resultant correlation coefficients were much improved, with eleven values larger than 0.5 including six values larger than 0.8 and the highest value of 0.94. The results show that the proposed sub-pixel analysis method is superior to the pixel based analysis method. The proposed method makes it possible to directly estimate the whole reservoir water area from, potentially, an individual cloud-free MODIS pixel, and is a promising way to improve the accuracy in the usability of MODIS images for the monitoring of reservoir surface water area variations.
Remote Sensing Data Selection Issues - Timothy A. Warner, Duane Nellis, and Giles M. Foody PART ONE: INTRODUCTION Remote Sensing Data Selection Issues - Timothy A. Warner, Duane Nellis, and Giles M. Foody Remote Sensing Policy - Ray Harris PART TWO: ELECTROMAGNETIC RADIATION & THE TERRESTRIAL ENVIRONMENT Visible, Near-IR & Shortwave IR Spectral Characteristics of Terrestrial Surfaces - Willem van Leeuwen Interactions of Middle Infrared (3-5 m) Radiation with the Environment - Arthur Cracknell and D. S. Boyd Thermal Remote Sensing in Earth Science Research - Dale Quattrochi and Jeffrey C. Luvall Polarimetric SAR Phenomenology and Inversion Techniques for Vegetated Terrain - Mahta Moghaddam PART THREE: DIGITAL SENSORS AND IMAGE CHARACTERISTICS Optical Sensor Technology - John Kerekes Fine spatial resolution optical sensors - Thierry Toutin Moderate Spatial Resolution Optical Sensors - Samuel N. Goward, Terry Arvidson, Darrel L. Williams, Richard Irish and Jim Irons Coarse Resolution Optical Sensors - Chris Justice and Compton Tucker Airborne Digital Multispectral Imaging - Doug Stow, Lloyd L. Coulter and Cody A. Benkelman PART FOUR: REMOTE SENSING ANALYSIS: DESIGN AND IMPLEMENTATION Imaging Spectrometers - Michael Schaepma Active and Passive Microwave Systems - Josef Kellndorfer and Kyle McDonald Airborne Laser Scanning - Juha Hyyppa, W. Wagner, M. Hollaus and H. Hyyppa Radiometry and reflectance: From terminology concepts to measured quantities - Gabriela Schaepman-Strub, Michael E. Schaepman, John V. Martonchik, Thomas H. Painter and Stefan Dangel Pre-Processing of Optical Imagery - Freek van der Meer and Harald van der Werff and Steven de Jong Surface Reference Data Collection - Chris Johannsen and Craig S. T. Daughtry Integrating Remote Sensing and Geographic Information Systems - James Merchant and Sunil Narumalani Image Classification - John Jensen, Jungho Im, Perry Hardin, Ryan R. Jensen Quantitative Models and Inversion in Optical Remote Sensing - Shunlin Liang Accuracy Assessment - Steve Stehman, Giles Foody PART FIVE: REMOTE SENSING ANALYSIS: APPLICATIONS A. LITHOSPHERIC SCIENCES Making Sense of the Third Dimension Through Topographic Analysis - Yongxin Deng Remote Sensing of Geology - Xianfeng Chen and David Campagna Remote Sensing of Soils - Jim Campbell B. PLANT SCIENCES Remote sensing for studies of vegetation condition: Theory and application - Mike Wulder, Joanne C. White, Nicholas C. Coops and Stephanie Ortlepp Remote Sensing of Cropland Agriculture - M. Duane Nellis, Kevin Price and Don Rundquist C. HYDROSPHERIC & CRYSOPHERIC SCIENCES Optical Remote Sensing of the Hydrosphere: From the open ocean to inland waters - Samantha Lavender Remote Sensing of the Cryosphere - Jeff Dozier D. GLOBAL CHANGE AND HUMAN ENVIRONMENTS Remote Sensing for Terrestrial Biogeochemical Modeling - Greg Asner and Scott V. Ollinger Remote Sensing of Urban Areas - Janet Nichol Remote sensing and the social sciences - Kelley Crews and Stephen J. Walsh Hazard Assessment and Disaster Management using Remote Sensing - Richard Teeuw, Paul Aplin, Nick McWilliam, Toby Wicks, Matthieu Kervyn and Gerald Ernst Remote Sensing of Land Cover Change - Timothy A. Warner, Abdullah Almutairi and Jong Yeol Lee PART SIX:. CONCLUSIONS Remote Sensing: A Look to the Future - Giles M. Foody, Timothy A. Warner and M. Duane Nellis
The remote sensing science and application communities have developed increasingly reliable, consistent, and robust approaches for capturing land dynamics to meet a range of information needs. Statistically robust and transparent approaches for assessing accuracy and estimating area of change are critical to ensure the integrity of land change information. We provide practitioners with a set of “good practice” recommendations for designing and implementing an accuracy assessment of a change map and estimating area based on the reference sample data. The good practice recommendations address the three major components: sampling design, response design and analysis. The primary good practice recommendations for assessing accuracy and estimating area are: (i) implement a probability sampling design that is chosen to achieve the priority objectives of accuracy and area estimation while also satisfying practical constraints such as cost and available sources of reference data; (ii) implement a response design protocol that is based on reference data sources that provide sufficient spatial and temporal representation to accurately label each unit in the sample (i.e., the “reference classification” will be considerably more accurate than the map classification being evaluated); (iii) implement an analysis that is consistent with the sampling design and response design protocols; (iv) summarize the accuracy assessment by reporting the estimated error matrix in terms of proportion of area and estimates of overall accuracy, user's accuracy (or commission error), and producer's accuracy (or omission error); (v) estimate area of classes (e.g., types of change such as wetland loss or types of persistence such as stable forest) based on the reference classification of the sample units; (vi) quantify uncertainty by reporting confidence intervals for accuracy and area parameters; (vii) evaluate variability and potential error in the reference classification; and (viii) document deviations from good practice that may substantially affect the results. An example application is provided to illustrate the recommended process.
No abstract is provided for this article.
The production of thematic maps, such as those depicting land cover, using an image classification is one of the most common applications of remote sensing. Considerable research has been directed at the various components of the mapping process, including the assessment of accuracy. This paper briefly reviews the background and methods of classification accuracy assessment that are commonly used and recommended in the research literature. It is, however, evident that the research community does not universally adopt the approaches that are often recommended to it, perhaps a reflection of the problems associated with accuracy assessment, and typically fails to achieve the accuracy targets commonly specified. The community often tends to use, unquestioningly, techniques based on the confusion matrix for which the correct application and interpretation requires the satisfaction of often untenable assumptions (e.g., perfect coregistration of data sets) and the provision of rarely conveyed information (e.g., sampling design for ground data acquisition). Eight broad problem areas that currently limit the ability to appropriately assess, document, and use the accuracy of thematic maps derived from remote sensing are explored. The implications of these problems are that it is unlikely that a single standardized method of accuracy assessment and reporting can be identified, but some possible directions for future research that may facilitate accuracy assessment are highlighted.
Often, in remote sensing, interest is focused on just one of the many classes that are typically represented in the area covered by an image. Various binary classifiers may be used to separate this specific class of interest from all others. It can, however, be difficult to identify the most appropriate classifier in advance. The selection of classifier is also often further complicated by a desire for information on classification uncertainty to indicate the spatial variation in classification quality. Here, five classifiers (a discriminant analysis, decision tree, support vector machine, multi‐layer perceptron, and radial basis function neural network) were used to map fenland, an important class for conservation activities, from Landsat ETM+data. The classifications derived ranged in accuracy from 81.2 to 96.8%. The outputs of the classifications were also combined using a simple voting procedure to determine class allocation. The accuracy of this ensemble approach was 95.6%. Although marginally, but insignificantly (at 95% level of confidence), less accurate than the most accurate individual classifier, it is difficult to specify the most appropriate classifier in advance. In addition, the ensemble approach yielded class‐allocation uncertainty information that may be used to help post‐classification refinement operations and later analyses. For example, as only a small proportion of cases were allocated with a high degree of uncertainty and these contained most of the mis‐classifications, targeting such sites for fieldwork could be one simple and efficient means of increasing classification accuracy. Alternatively, the cases for which all five classifiers agreed on an allocation could be treated as being correctly labelled with a high degree of confidence.
A large proportion of the workforce in the brick kilns of the Brick Belt of Asia are modern-day slaves. Work to liberate slaves and contribute to UN Sustainable Development Goal 8.7 would benefit from maps showing the location of brick kilns. Previous work has shown that brick kilns can be accurately identified and located visually from fine spatial resolution remote-sensing images. Furthermore, via crowdsourcing, it would be possible to map very large areas. However, concerns over the ability to maintain a motivated crowd to allow accurate mapping over time together with the development of advanced machine learning methods suggest considerable potential for rapid, accurate and repeatable automated mapping of brick kilns. This potential is explored here using fine spatial resolution images of a region of Rajasthan, India. A contemporary deep-learning classifier founded on region-based convolution neural networks (R-CNN), the Faster R-CNN, was trained to classify brick kilns. This approach mapped all of the brick kilns within the study area correctly, with a producer’s accuracy of 100%, but at the cost of substantial over-estimation of kiln numbers. Applying a second classifier to the outputs substantially reduced the over-estimation. This second classifier could be visual classification, which, as it focused on a relatively small number of sites, should be feasible to acquire, or an additional automated classifier. The result of applying a CNN classifier to the outputs of the original classification was a map with an overall accuracy of 94.94% with both low omission and commission error that should help direct anti-slavery activity on the ground. These results indicate that contemporary Earth observation resources and machine learning methods may be successfully applied to help address slavery from space.
Spatial records of species are commonly misidentified, which can change the predicted distribution of a species obtained from a species distribution model (SDM). Experiments were undertaken to predict the distribution of real and simulated species using MaxEnt and presence-only data “contaminated” with varying rates of misidentification error. Additionally, the difference between the niche of the target and contaminating species was varied. The results show that species misidentification errors may act to contract or expand the predicted distribution of a species while shifting the predicted distribution towards that of the contaminating species. Furthermore the magnitude of the effects was positively related to the ecological distance between the species’ niches and the size of the error rates. Critically, the magnitude of the effects was substantial even when using small error rates, smaller than common average rates reported in the literature, which may go unnoticed while using a standard evaluation method, such as the area under the receiver operating characteristic curve. Finally, the effects outlined were shown to impact negatively on practical applications that use SDMs to identify priority areas, commonly selected for various purposes such as management. The results highlight that species misidentification should not be neglected in species distribution modeling.
