Quantitative extraction of the bedrock exposure rate based on unmanned aerial vehicle data and Landsat-80LI image in a karst environment

In the karst regions of southwest China, rocky desertification is one of the most serious problems in land degradation. The bedrock exposure rate is an important index to assess the degree of rocky desertification in karst regions. Because of the inherent merits of macro-scale, frequency, efficiency, and synthesis, remote sensing is a promising method to monitor and assess karst rocky desertification on a large scale. However, actual measurement of the bedrock exposure rate is difficult and existing remote-sensing methods cannot directly be exploited to extract the bedrock exposure rate owing to the high complexity and heterogeneity of karst environments. Therefore, using unmanned aerial vehicle （UAV） and Landsat-8 Operational Land Imager （OLI） data for Xingren County, Guizhou Province, quantitative extraction of the bedrock exposure rate based on multi-scale remote-sensing data was developed. Firstly, we used an object-oriented method to carry out accurate classification of UAV images. From the results of rock extraction, the bedrock exposure rate was calculated at the 30 m grid scale. Parts of the calculated samples were used as training data; other data were used for model validation. Secondly, in each grid the band reflectivity ofLandsat-80LI data was extracted and a variety of rock and vegetation indexes （e.g., NDVI and SAVI） were calculated. Finally, a network model was established to extract the bedrock exposure rate. The correlation coefficient of the network model was 0.855, that of the validation model was 0.677 and the root mean square error of the validation model was 0.073. This method is valuable for wide-scale estimation of bedrock exposure rate in karst environments. Using the quantitative inversion model, a distribution map of the bedrock exposure rate in Xingren County was obtained.

Remote sensing-based global crop monitoring: experiences with China’s CropWatch system

Monitoring the production of main agricultural crops is important to predict and prepare for disruptions in food supply and fluctuations in global crop market prices.China’s global crop-monitoring system(CropWatch)uses remote sensing data combined with selected field data to determine key crop production indicators:crop acreage,yield and production,crop condition,cropping intensity,crop-planting proportion,total food availability,and the status and severity of droughts.Results are combined to analyze the balance between supply and demand for various food crops and if needed provide early warning about possible food shortages.CropWatch data processing is highly automated and the resulting products provide new kinds of inputs for food security assessments.This paper presents a comprehensive overview of CropWatch as a remote sensingbased system,describing its structure,components,and monitoring approaches.The paper also presents examples of monitoring results and discusses the strengths and limitations of the CropWatch approach,as well as a comparison with other global crop-monitoring systems.

Land use and land cover classification using Chinese GF-2 multispectral data in a region of the North China Plain

The newly launched GF-2 satellite is now the most advanced civil satellite in China to collect high spatial resolution remote sensing data.This study investigated the capability and strategy of GF?2 multispectral data for land use and land cover (LULC) classification in a region of the North China Plain.The pixel-based and object-based classifications using maximum likelihood (MLC) and support vector machine (SVM) classifiers were evaluated to determine the classification strategy that was suitable for GF?2 multispectral data.The validation results indicated that GF-2 multispectral data achieved satisfactory LULC classification performance,and object-based classification using the SVM classifier achieved the best classification accuracy with an overall classification accuracy of 94.33% and kappa coefficient of 0.911.Therefore,considering the LULC classification performance and data characteristics,GF-2 satellite data could serve as a valuable and reliable high-resolution data source for land surface monitoring.Future works should focus on improving LULC classification accuracy by exploring more classification features and exploring the potential applications of GF-2 data in related applications.