Surveying sandy deserts and desertified lands in north‐western China by remote sensing

Understanding the spatial and temporal dynamics of vegetation is essential in drylands. In this paper, we evaluated three vegetation indices, namely the Normalized Difference Vegetation Index (NDVI), the Soil-Adjusted Vegetation Index (SAVI) and the Enhanced Vegetation Index (EVI), derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) Surface-Reflectance Product in the Xinjiang Uygur Autonomous Region, China (XUAR), to assess index time series' suitability for monitoring vegetation dynamics in a dryland environment. The mean annual VI and its variability were generated and analyzed from the three VI time series for the period 2001-2012 across XUAR. Two phenological metrics, start of the season (SOS) and end of the season (EOS), were detected and compared for each vegetation type. The mean annual VI images showed similar spatial patterns of vegetation conditions with varying magnitudes. The EVI exhibited high uncertainties in sparsely vegetated lands and forests. The phenological metrics derived from the three VIs are consistent for most vegetation types, with SOS and EOS generated from NDVI showing the largest deviation.

Section: Study Areamentioning

confidence: 99%

Evaluation of Three MODIS-Derived Vegetation Index Time Series for Dryland Vegetation Dynamics Monitoring

Kuenzer

Wang

et al. 2015

“…Early data, from the 1960s, on land cover were manually interpreted from black and white aerial photographs and supplementary maps [23]. Most studies analyzed Landsat MSS/TM/ETM+ data from selected years on a regional scale [6,8,[11][12][13][15][16][17][18][20][21][22][24][25][26], supplemented by thematic, administrative and topographic maps. Others used low-resolution imagery data from AVHRR and MODIS [12,21,27].…”

Section: Introductionmentioning

confidence: 99%

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

Karnieli

Qin

et al. 2014

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.

“…Most disagreements occur in the class assignments of the vegetation types. For example, the UMD product has an obvious overestimation of shrub lands where barren lands are distributed [21]. Although within these products vast areas of herbaceous vegetation were detected in the middle of the XUAR, only a small part was classified, for example, within the GlobCover data set.…”

Section: The Requirement For a Novel Land Cover Product Ove R The Xuarmentioning

confidence: 99%

“…The Tarim Basin and the Taklamakan Desert are situated between the Tianshan and Kunlun Mountains. The XUAR is primarily covered by grassland and sandy desert [21]. Forest areas are sparsely scattered within the high mountains and along the rivers.…”

Section: Study Areamentioning

confidence: 99%

“…As mass data (more than one hundred Landsat scenes) processing is required to produce a land cover product for the entire XUAR, previous studies have focused primarily on subsets of the overall area. For example, land cover and land-use dynamics have been mapped using satellite images for selected oases in the XUAR [20][21][22]. Studies on land cover mapping throughout the XUAR are still lacking.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Land Cover Classification Map Based on a MODIS Time-Series in Xinjiang, China

Kuenzer

Guo

et al. 2014

Accurate mapping of land cover on a regional scale is useful for climate and environmental modeling. In this study, we present a novel land cover classification product based on spectral and phenological information for the Xinjiang Uygur Autonomous Region (XUAR) in China. The product is derived at a 500 m spatial resolution using an innovative approach employing moderate resolution imaging spectroradiometer (MODIS) surface reflectance and the enhanced vegetation index (EVI) time series. The classification results capture regional scale land cover patterns and small-scale phenomena. By applying a regionally specified classification scheme, an extensive collection of training data, and regionally tuned data processing, the quality and consistency of the phenological maps are significantly improved. With the ability to provide an updated land cover product considering the heterogenic environmental and climatic conditions, the novel land cover map is valuable for research related to environmental change in this region.