Land change science has emerged as a fundamental component of global environmental change and sustainability research (Turner et al., 2007). The importance of mapping, quantifying and monitoring the increasing change of the land surface, caused by natural processes and human activities, is now widely recognized. There is a considerable interest in deriving land cover change information for important societal concerns, for instance in terms of biodiversity impacts as well as for climate change studies. Indeed, land cover change can be a cause and a consequence of climate change (Turner et al., 2007). A better understanding of landscape changes due to human activities specifically is critical information to help policymakers to limit or anticipate environmental damages, such as large-scale deforestation that is now decimating rainforests, through the development of effective sustainable management practices (Zhang et al., 2014).
Land cover change information is also required to evaluate and model the influence of changes on environmental processes (Pouliot et al., 2014). At global scale, land cover change studies are particularly needed as the net flux of carbon from land use and land cover change is by far the most uncertain term in the global carbon budget (Lewis, 2006; Ramankutty, 2007; Houghton et al., 2012).
4.1. Current land cover change detection methods
Singh (1989) defines change detection as “the process of identifying
differences in the state of an object or phenomenon by observing it at different times”. With their high temporal frequency, synoptic view and wide
range of spatial and spectral resolution, the data from remote sensing satellites provide opportunities to acquire suitable information for change
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detection studies (Hussain et al., 2013). In the last 30 years, a large number of change detection methodologies, techniques and algorithms, utilizing remotely sensed data, have been developed to identify the geographical location and type of changes. Numerous researches have been conducted to study land cover change from local to regional and global scales, relying on the alteration of the spectral behaviour to detect land surface changes.
Recent change detection reviews (Hussain et al., 2013; Tewkesbury et al., 2015) organized the many change detection approaches according to units of image analysis and comparison methods used to highlight change. The following description summaries their main conclusions. Typically, to identify change, the input images are compared and a decision is made as to the presence or degree of change (Tewkesbury et al., 2015). The analysis units that are compared over time to highlight change are principally individual image pixels and image-object created previously from segmentation of the image, but could also include groups of pixels, vector polygons or a combination of these. Comparing pixel intensities for change detection is a simple concept applying arithmetic operations to continuous band radiance or reflectance, or integer class labels. However, the pixel- based techniques are not considered appropriate for Very High Resolution (VHR) imagery because they face a challenge posed by higher spectral variability, mixed pixels and image registration issues on these VHR data, producing spurious change pixels. The object-based change detection techniques are considered more suitable for VHR image data as they reduce these effects. A segmentation process partitions an image into homogenous objects which are spectrally similar and spatially adjacent (Bontemps et al., 2008). Image objects get richer information such as texture or shape and allow the exploitation of the spatial context. Different techniques exist, such as the image-object overlay comparing objects segmented from one image, the image-object comparison where images are segmented independently then compared, or the multi-temporal image-object where image segmentation is applied to stacked multi-temporal images. However, in object-based analysis, the segmentation scale and image resolution must be carefully chosen so as to adequately define change features of interest, which is not straightforward. The size of the target change must be known prior to perform the analysis.
A variety of different methods of comparisons have been developed, based on the pixel-based and object-based approaches. These methods can
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be categorized in pre-classification and post-classification techniques. The pre-classification approach is mainly based on the direct and continuous analysis of spectral signature (radiance or reflectance values), for instance in SPOT-HRV, Landsat images and MODIS time series (Desclée et al., 2006; Bontemps et al., 2008, 2012b; Duveiller et al., 2008; Ernst et al., 2013; Hansen et al., 2013; Jin et al., 2013; Mayaux et al., 2013; Brink et al., 2014; Huang and Friedl, 2014; Pouliot et al., 2014; Zhang et al., 2014). Among many techniques, the most common are simple arithmetic operations, such as subtraction or division applied to multi-temporal imagery depicting radiance or vegetation indices differences, change vector analysis (CVA) and principal component analysis (PCA). In CVA, the pixel values are treated as vectors of spectral bands. The magnitude of the change vector gives the degree to which the image radiance has changed, containing limited thematic content, while the direction indicates the types of change. If this technique allows the simultaneous analysis of multiple image bands, a limitation is that both the magnitude and direction can be ambiguous. Data transformation such as PCA is a method of data reduction by suppressing correlated information, supposed to be the areas of no change, and highlighting variance, corresponding to areas of change. When applied to a multi-temporal stack of remotely sensed images, there is the potential to highlight image change, since it should be uncorrelated between the respective datasets. Transformations are however scene dependent and may prove difficult to interpret. The application of spectral-based techniques at large scales is challenged by the variability in the spectral properties of the images in space and over time, and strongly requires the implementation of radiometric calibration and atmospheric corrections at broad spatial and temporal scales (Bontemps, 2010).
The post-classification and direct classification approaches compare independent classifications to derive change information (Chen et al. 2012; McRoberts, 2013; Sexton et al., 2013). Using classifications produced over a year avoid radiometric change related to illumination and viewing angles, phenology or atmospheric conditions that cause spurious change detection. It allows to directly looking at semantic changes. Post-classification change detection is the process of overlaying coincident thematic maps from different time periods to identify changes between them. The distinct advantage of this technique is that the baseline classification and the change transitions are explicitly known. It is a thematically rich technique able to answer specific change questions, making it suitable for a range of different
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applications. However, it is limited by map production issues and compounded errors. Furthermore, input maps may be produced using differing data and algorithms. In this case, a distinction must be made between classification inconsistencies and real change. Another technique is the direct classification of a time series of images. However, deriving training datasets for such a classification can by very challenging and supervised approaches can prove unresponsive to small magnitude change pattern. Less frequent are the hybrid analysis that refers to the use of two or more methods for change detection (Griffiths et al., 2013).
Appropriate selection of unit of analysis and comparison method is related to the objective of the study, depending for instance on the level of change information needed (i.e. simple “change/no-change” information or a detailed change direction), on the size of the study and on the spatial resolution.
4.2. Challenges in observing land cover change
Remote sensing researches mainly focused on detection of land cover change in order to highlight and sometimes to delineate the area of change. Detecting where a change is occurring is only a part of the answer. Another challenge in land cover change detection is first to identify the fate of the changed area, for example the fate of cleared land (Houghton et al., 2012). Indeed, for many applications, detailed information regarding the land cover class observed after change is needed (Ramankutty et al., 2006), but a simple binary change vs. no change is often described as sufficient for many studies (Hussain et al., 2013). Secondly, very few studies provide information on the year of change, although it is critical information for climate models. Thirdly, most of land cover change studies focus solely on forest change, because of the current focus of environmental monitoring concerning the global carbon cycle and biodiversity loss (Hansen and Loveland, 2012). Another reason is also the fact that this is easier to observe processes of forest conversion with regards to other land cover transition. However, other types of change such as urbanization, cropland extension, dryland degradation or lake and waterways drying up are also transforming the planet’s surface with critical environmental impacts (Foley et al., 2005; Lepers, et al., 2005), and deserve dedicated studies. Finally, among many change detection studies, most are completed at local, national or regional scale. Except for forest change (Hansen et al., 2013), our understanding of
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the extent of land cover change is limited (Giri et al., 2013) and significant uncertainties persist about the global rate and patterns of change of agricultural lands, drylands, wetlands, built-up areas or water, among others.
Launched in 1972 by NASA, Landsat has become the data source of choice for many change detection studies, especially since the opening of the USGS Landsat archive in 2009 that has enabled exploration of methods for higher-frequency, time-serial monitoring of land cover dynamics (Sexton et al., 2013). However, while such data cover the globe, working with such high resolution (30 m) raises numerous challenges for continental to global scale studies. For instance, the low temporal frequency coupled with the frequent cloud cover and phenological effects induces problems in the resulting scene coverage, and the effort involved in classifying thousands of images is consequent (Ramankutty et al., 2006; Griffiths et al., 2013).
Unlike high resolution data, medium resolution long time series provide consistency in image acquisition to get cloud-free observations, a full phenological information during the year to distinguish land cover types, and a global coverage. In addition, it allows avoiding radiometric change related to illumination or viewing geometry that are not reflecting actual change on the ground, for instance through a bi-temporal comparison with high resolution images (Tewkesbury et al., 2015). They are under-used to derive change delineation and quantification at global scale. Although their spatial resolution clearly limits their ability for detecting small changes, they could serve as a convenient and suitable alternative to fulfill challenges such as the detection of the year of change and the fate of the changed area, for all types of land cover changes and at global scale.