Conference Agenda

The assessment of land use sustainability requires a precise and objective accounting of land degradation and regeneration rates. This is a main basis of the international initiative on achieving Land Degradation Neutrality (LDN), defined by the United Nations Convention to Combat Desertification as ‘a state whereby the amount and quality of land resources, necessary to support ecosystem functions and services and enhance food security, remains stable or increases within specified temporal and spatial scales and ecosystems’.

We present here a pilot study developed in the drylands of China and based on geospatial data archives of vegetation and climate for the hydrological years 2002 through 2012. Net Primary Productivity (NPP) yearly summaries, derived from MERIS satellite data by the CASA algorithm, were regressed in a stepwise form against matching aridity index data and year number. Such regressions were made pixel by pixel using the data transformed to standardized residuals, to enable comparisons between the effects of both predictors. Effects of time, after discarding aridity, were assumed as land degradation or regeneration depending on the sign of the standard partial regression coefficient (SPRC), negative and positive respectively. Significance was set at 90%. Then, the Mann-Whintney U test was used to compare the relative magnitudes of negative and positive SRPC, both by aridity zones and land uses. Spatial resolution was of 4 km. The drylands domain was taken from a published study on the determination of the Potential Extent of Desertification in China.

Overall, degrading trends prevail over regeneration ones, which is particularly noticeable in grasslands, deserts and croplands, and in all the aridity zones. Further to that, land degradation rates were found significantly faster than regeneration rates in grasslands and deserts, and in the semi-arid and dry sub-humid zones. Croplands, on the contrary, did result in faster regeneration than degradation, albeit the latter prevails in extent as mentioned above.

These results are still being interpreted. In general, they must be seen in the context of a high variability mosaic, where strong intensification of productive land uses (e.g. croplands) coexists with strict environmental conservation policies applied to large areas of inherited desertification that still may have not had time to show a trend change (e.g. grasslands), and with natural or seminatural areas with no detectable trend.

In parallel with the interpretation, the next step will be an essay of accounting LDN using a methods endorsed by UNCCD.

As a special vegetation types, Ulmus pumila L. sparse forest were widely distributed in the Otindag Sandy Land. It is an important component of the Otindag Sandy Land ecosystem, and also plays an important role in windbreak and sand fixation, climate regulation and grassland ecosystem maintenance. Most of the researches of Elm sparse forest information extraction are mostly based on traditional methods such as survey, field visits and historical documents. Thus, they are laborious and have great financial resources, and the investigation period is long and difficult to update and difficult to meet the demand of obtaining a wide range of Elm spatial distribution. Therefore, techniques for automatically identification the spatial distribution of Elm trees is necessary. With the development of remote sensing techniques, the spatial resolution of remote sensing images is getting higher and higher. The crown of each tree can be clearly seen in the high resolution remote sensing image. According to the characteristics of the geometric shape, size and spatial pattern displayed on the image, tree crown information can be estimated accurately. The accurate information of Elm sparse forest canopy is a prerequisite for other scientific and rational research, and it is also an important reference for decision makers.

Based on the homemade GF-2 data, the hinterland of Otindag Sandy Land where the Elm widely distributed was selected as the study area, and a technique for Elm Sparse Forest information extraction was promoted. First of all , by analyzing the performance of different objects of remote sensing images in the Otindag Sandy Land, the extracted NDVI was nonlinearly stretched to construct a feature image for detecting Elm spots; Secondly, by combining different sizes of filtering kernels and standard deviation, Gaussian filtering is adopted to generate multi-scale feature space to meet the needs for different scales of Elm distribution extraction; Next, the Laplacian operator is applied to the multi-scale feature space, and then detect the bright spot center on different scales, ie The center of the Elm target; Finally, based on field survey results, the accuracy of Elm detection results was evaluated.

Desertification is one of the most important environmental problems in drylands of China, and the damage is very serious. It is of great significance to carry out monitoring of desertification in large areas to grasp the status and dynamics of desertification and formulate scientific and effective prevention and control strategies. Soil organic matter is one of the important indicators of desertification conditions. However, due to data lack and disturbances of vegetation signals, etc., large areas of soil organic matter acquisition have always faced greater difficulties. Compared with traditional ground-based observations, remote sensing technology has the potential to provide more reliable, time- and labor-saving estimates of soil organic matter content in large areas, which in turn provides data support for desertification monitoring and assessment.

This study, uses Google Earth Engine (GEE) with mass remote sensing data provision and cloud computing capabilities, exploring different machine learning methods such as CART, Random Forest (RF), and Support Vector Machine (SVM) to estimate the soil organic matter content of desertified land in China drylands, based on Sentinel-2 high-resolution image reflectance (non-growth season), topographic data, climate data, characteristic spectral index data, and ground measured soil organic matter content data (0-20cm). Overall, CART showed better accuracy than RF and SVM. The CART model obtained moderate results with R² of 0.48 and RMSE 0.35 without considering ancillary factors. By including the terrain, climate and characteristic spectral factors, the model accuracy improved greatly (R² can reach 0.86, the RMSE to 0.16, and the precision increased by 53%), which fully highlighting the importance of including the characteristic index and climate and topography factor when estimating the soil organic matter content. In particular, compared with other existing soil products in the region, this study obtained a full-coverage, higher-resolution and more reliable spatial distribution map of soil organic matter content, which could provide better support for desertification monitoring in China drylands in the future. .