Spatio-Temporal Dynamics of Land-Use and Land-Cover in the Mu Us Sandy Land, China, Using the Change Vector Analysis Technique

Abstract

Abstract:The spatial extent of desertified vs. rehabilitated areas in the Mu Us Sandy Land, China, was explored. The area is characterized by complex landscape changes that were caused by different drivers, either natural or anthropogenic, interacting with each other, and resulting in multiple consequences. Two biophysical variables, NDVI, positively correlated with vegetation cover, and albedo, positively correlated with cover of exposed sands, were computed from a time series of merged NOAA-AVHRR and MODIS images (1981 to 2010). Generally, throughout the study period, NDVI increased and albedo decreased. Improved understanding of spatial and temporal dynamics of these environmental processes was achieved by using the Change Vector Analysis (CVA) technique applied to NDVI and albedo data extracted from four sets of consecutive Landsat images, several years apart. Changes were detected for each time step, as well as over the entire period (1978 to 2007). Four categories of land cover were created-vegetation, exposed sands, water bodies and wetlands. The CVA's direction and magnitude enable detecting and quantifying finer changes compared to separate NDVI or albedo difference/ratio images OPEN ACCESS Remote Sens. 2014, 6 9317 and result in pixel-based maps of the change. Each of the four categories has a biophysical meaning that was validated in selected hot-spots, employing very high spatial resolution images (e.g., Ikonos). Selection of images, taking into account inter and intra annual variability of rainfall, enables differentiating between short-term conservancies (e.g., drought) and long-term alterations. NDVI and albedo, although comparable to tasseled cap's brightness and greenness indices, have the advantage of being computed using reflectance values extracted from various Landsat platforms since the early 1970s. It is shown that, over the entire study period, the majority of the Mu Us Sandy Land area remained unchanged. Part of the area (6%), mainly in the east, was under human-induced rehabilitation processes, in terms of increasing vegetation cover. In other areas (5.1%), bare sands were found to expand to the central-north and the southwest of the area.

SPOT-Based Sub-Field Level Monitoring of Vegetation Cover Dynamics: A Case of Irrigated Croplands

“…The CVA approach has been shown to be effective and accurate for mapping land cover changes within managed and naturally arid landscapes at high spatial detail [28,29]. Although SPOT imagery is effective for land cover change detection in a number of applications, a multi-resolution approach that combines both coarse and fine scale data sets is more effective for characterization of vegetation dynamics in irrigated cropland environments as described here.…”

“…This method offers two principal advantages: it enables detection of gradual land cover changes, and it allows concurrent analyses of a given change in all data layers, rather than focusing on a few selected bands [27]. Although the utility of this technique has been clearly demonstrated [28], few studies currently exist documenting the use of CVA for assessment of land conditions in arid/drylands regions, even with the inclusion of high spatial resolution image data. Additionally, the potential of combining CVA with high spatial resolution imagery and sub-pixel mapping techniques to improve discrimination of land cover dynamics remains to be investigated [29].…”

A combined change detection procedure to study desertification using opensource tools

“…The first quadrant (between 0°and 90°) is indicative of a change towards moisture reduction and in desert areas has been found connected with a change towards salty surfaces, i.e. drying lakes [34]; the fourth quadrant (270°-360°), with increasing Brightness and decreasing Greenness, is also connected to a change towards drier conditions, namely towards bare soil/sand expansion and deforestation. The other two quadrants, with decreasing Brightness, are connected with changes towards more wet conditions: the second one (90°-180°) with changes towards chlorophyll increase and forest regeneration, and the third (180°-270°) with changes towards higher moisture land and water [23,31].…”

“…2. In literature, Albedo and NDVI (Normalized Difference Vegetation Index) [34] or the Tasselled Cap features Brightness and Greenness [8] have been used for this purpose. The first quadrant (between 0°and 90°) is indicative of a change towards moisture reduction and in desert areas has been found connected with a change towards salty surfaces, i.e.…”

A Hybrid Kernel-Based Change Detection Method for Remotely Sensed Data in a Similarity Space

“…Change detection methods are divided into six categories in the literature: (a) Images algebra (b) Change Vector Analysis (CVA); (c) Image transformation; (d) Post-classification comparison (e) Direct classification; and (f) Hybrid CD [5][6][7]. In images algebra method, mathematical operations such as subtraction or division applied to multi-temporal imagery; providing the difference or ratio images [8,9].…”