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Faculty of Mathematics and Natural Sciences - Geomatics

Faculty of Mathematics and Natural Sciences | Geography Department | Geomatics | Projects | Completed Projects | Land degradation | Land degradation monitoring using remote sensing in arid and semi-arid environment

Land degradation monitoring using remote sensing in arid and semi-arid environment

Project period: 04/2010 - 05/2014

Description of the project

The scientific focus of this project was to monitoring land use and land cover change (LULCC) using hyper-temporal remote sensing time series to better understand how land surfaces are impacted by human decisions. In the first part of the project, the opportunities and limitations of using coarse resolution imagery time series for monitoring long-term land changes were first examined. The most widely used remote sensing time series from AVHRR-GIMMS, SPOT VGT and MODIS were inter-compared for checking their temporal trend consistencies. The second part of the project aimed to develop an approach detecting annual changes between multiple land categories. The utility of a trajectory-based change detection approach applied to MODIS data to map annual land use and land cover change was tested. The last part of the project applied the methodology developed in the second part to investigate three processes that are mostly related to China’s land restoration programs in Inner Mongolia: deforestation, forest regeneration (i.e., afforestation and reforestation) and conversion from cropland to grassland (cropland retirement).

Results

The results of this project were successfully published in international peer-reviewed journals/books and contributed to several international conferences (publications). In summary, the following major conclusions can be drawn from the achieved results:

  • The spatial pattern of vegetation trends derived from SPOT VGT and MODIS Terra exhibited great similarities, and the regression analysis between these two NDVI product-derived trend parameters further confirmed a strong agreement. However, major disagreements became apparent when inter-annual trends between SPOT VGT and AVHRR GIMMS NDVI were compared.
  • Combing trajectory-based approach and land cover probability derived from machine learning approach achieved reliable accuracy for annual land cover change mapping. Both the abrupt and gradual land change processes were well depicted, though the levels of accuracy vary from one type to another. Forest change, for example, was found to be most reliably detected, while changes between herbaceous land cover types were less well detected due to their spectral similarity.
  • The fire-excluded deforestation rate rapidly decreased after 2000 in Inner Mongolia, and the counties that were enrolled in China’s ecological programs showed more decline in deforestation. Most of the forest re-generation and conversions from cropland to grassland occurred at the early stage of the land restoration programs.

Satellite Image and graph land degradation

Conversion from forest to grassland. Landsat composites (p122r27, RGB=4, 5, 3) for 2002 (A), 2003 (B) and 2011 (C). D: LULCC map. E: Source values and fitted probability trajectories in red (grassland) and green (forest); source location marked in cyan in A-D. Source: Yin et al. (2014)

Publications

Peer-reviewed papers
Yin, H., Pflugmacher, D., Kennedy, R.E., Sulla-Menashe, D. Hostert, P., Mapping annual land cover changes using MODIS time series. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7, 3421-3427.

Li, Z.G., Yang, P., Tang, H.J., Wu, W.B., Yin, H., Liu, Z.H., Zhang, Li. (2014): Response of maize phenology to climate warming in Northeast China between 1990 and 2012. Regional Environmental Change. 14, 39-48.

Liu, X.Q., Wang, Y.L., Peng, J., Ademola, K.B., Yin, H. (2013): Assessing vulnerability to drought based on exposure, sensitivity and adaptive capacity: A case study in middle Inner Mongolia of China. Chinese Geographical Science, 1, 13-25.

Yin, H., Udelhoven, T., Fensholt, R., Pflugmacher, D., Hostert, P. (2012): How Normalized Difference Vegetation Index (NDVI) trends from Advanced Very High Resolution Radiometer (AVHRR) and Système Probatoire d’Observation de la Terre VEGETATION (SPOT VGT) time series differ in agricultural areas: An Inner Mongolian case study. Remote Sensing, 4, 3364-3389.

Non Peer-reviewed conference contributions

Yin, H., Pflugmacher, D. Li, Z.G., and Hostert, P. (2014): Land use and land cover change in Inner Mongolia – understanding the effects of China’s re-vegetation programs. 2nd Global Land Project Open Science Meeting. March 2014, Berlin, Germany.

Li, Z.G., Yin, H., Yang, P., Tang, H.J., Wu, W.B., Chen, Z.X., Liu, Z.H., Tan, J.Y., and Zhang, L. (2014): Response of maize cropping system to climate warming in Northeast China in the past 30 years. 2nd Global Land Project Open Science Meeting. March 2014, Berlin, Germany.

Yin, H., Pflugmacher, D., and Hostert, P. (2014): Mapping annual land cover changes using MODIS time series. Joint workshop of the EARSeL 5th Land Use & Land Cover Workshop and NASA LCLUC Science Team. March 2014, Berlin, Germany.

Yin, H., Pflugmacher, D. Li, Z.G., and Hostert, P. (2013): Assessing land degradation in Inner Mongolia using MODIS time series. 7th International Workshop on the Analysis of Multi-temporal Remote Sensing Images. June 2013, Banff, Canada.


Lab members involved
Funding

This project was funded by the China Scholarship Council (CSC) and Humboldt Innovation.