Coupling spectral unmixing and multiseasonal remote sensing for temperate dryland land-use/land-cover mapping in Minqin County, China

Abstract

Temperate dryland ecosystems in China are undergoing accelerated changes due to natural and anthropogenic disturbances. Using the Minqin oasis as a case study area, this article examined linear spectral mixture analysis (LSMA) with the fixed fourimage endmember (EM) model comprising sand, green vegetation, saline land, and dark materials (i.e. the degraded symptoms/EMs) for temperate dryland land-use and land-cover mapping. Dryland covers defined by landscape seasonality of the four EMs at the subpixel level are more easily interpreted to achieve acceptable accuracy, and allow better understanding of the processes of land degradation, such as two distinct salt/water movements were found in the study area. The Minqin oasis faces a significant challenge that requires a long-term monitoring system to understand the relationship between land-use decisions and ecological consequences. The approach developed with the mutiseasonal LSMA, representing dryland land-cover seasonality with the common surface degradation types (i.e. the four EMs) in a tree-structure framework, promises a robust and operative tool for land degradation assessment and monitoring and would be applied in a more different dryland environment in the future.

Agricultural Soil Alkalinity and Salinity Modeling in the Cropping Season in a Spectral Endmember Space of TM in Temperate Drylands, Minqin, China

“…The geometrically-rectified 2008 images [21] were used as references to co-register and correct the TM images in 2010, and the average half-pixel image-to-image co-registration accuracy was achieved. These images were atmospherically corrected using the FLAASH (Fast Line-of-sight Atmospheric Analysis of Hypercubes) module of the ENVI 4.8 software for radiance to reflectance conversion, according to the corresponding image information.…”

“…A full description of the dimensional mixing space and EMs selection in the study area was given in Sun and Liu [21]. The mean overall root mean squared error (RMSE) and its Based on the 2008 dryland use map [21], we sampled 38 field sites during 7-15 September 2010, in agricultural areas (Figure 1). Each site was a 30 × 30 m 2 plot around the center point, and was further divided into four subplots to sample soil in the shallow root zone (0-20 cm).…”

“…Based on the 2008 dryland use map [21], we sampled 38 field sites during 7-15 September 2010, in agricultural areas (Figure 1). Each site was a 30 × 30 m 2 plot around the center point, and was further divided into four subplots to sample soil in the shallow root zone (0-20 cm).…”

“…The SVD, however, is needed to calibrate in the different geographic/ecological areas for local basic cover types [21]. The specific SVD four-endmember model (i.e., SL, sand land; SA, saline land; GV, green vegetation; and DA, dark materials) in the multidate TM imagery was validated and applied to intra-annual cover classification and inter-annual change detection in the temperate dryland system of Minqin, China [21,22].…”

“…The SVD, however, is needed to calibrate in the different geographic/ecological areas for local basic cover types [21]. The specific SVD four-endmember model (i.e., SL, sand land; SA, saline land; GV, green vegetation; and DA, dark materials) in the multidate TM imagery was validated and applied to intra-annual cover classification and inter-annual change detection in the temperate dryland system of Minqin, China [21,22]. In the stable SVD space, land cover can be identified without the known training areas; moreover, EMs (spectral endmembers) are easily validated and converted into the typical biophysical attributes [23][24][25].…”