Opium poppies are a major source of traditional drugs, which are not only harmful to physical and mental health, but also threaten the economy and society. Monitoring poppy cultivation in key regions through remote sensing is therefore a crucial task; the location coordinates of poppy parcels represent particularly important information for their eradication by local governments. We propose a new methodology based on deep learning target detection to identify the location of poppy parcels and map their spatial distribution. We first make six training datasets with different band combinations and slide window sizes using two ZiYuan3 (ZY3) remote sensing images and separately train the single shot multibox detector (SSD) model. Then, we choose the best model and test its performance using 225 km² verification images from Lao People’s Democratic Republic (Lao PDR), which exhibits a precision of 95% for a recall of 85%. The speed of our method is 4.5 km²/s on 1080TI Graphics Processing Unit (GPU). This study is the first attempt to monitor opium poppies with the deep learning method and achieve a high recognition rate. Our method does not require manual feature extraction and provides an alternative way to rapidly obtain the exact location coordinates of opium poppy cultivation patches.

Detailed information pertaining to soil erosion is required to ensure eco-environment protection and economic development for Xinjiang Uygur Autonomous Region, and remote sensing and geographical information system technologies are adopted to obtain an accurate distribution of soil erosion. An improved estimation method for wind erosion is developed, which proves to be effective with an overall precision of 87.24%. The proposed model uses six critical factors: number of snow cover days, soil erodibility, aridity index, vegetation fraction, soil crust index, and wind field intensity. Results show that soil wind erosion is widely distributed throughout the Xinjiang Uygur Autonomous Region. The average erosion intensity is 0.50, which is determined to be a moderate erosion level. The wind erosion intensity increases from a slight level in the western part to a severe level in the mid-eastern part. The mean intensity of soil wind erosion for varying land use types is ranked in descending magnitude of barren or sparse vegetation, open shrubland, grassland, cropland, evergreen coniferous forest, and mixed forest. There are large differences in soil wind erosion intensities among soil types; grassland has severe wind erosion and Luvic Arenosols has the lowest erosion intensity.

In order to quantitatively evaluate the intensity of FT erosion, eight typical factors, including annual FT cycle days, precipitation, average diurnal phase-changed water content, rainfall erosivity during the FT period, wind field intensity during the FT period, slope, aspect, and vegetation, were introduced to establish an improved evaluation method of FT erosion in the TRSR, which had better applicability in TRSR with an overall precision of 93%. Results showed that FT erosion was widely distributed in the TRSR, with zones of slight, mild, and moderated erosion being the most widely distributed. During 2000–2015, a slight improvement can be observed in the condition of FT erosion over the whole study region. Vegetation coverage was the dominant factor affecting the intensity of FT erosion in the zones with sparse vegetation or bare land, whereas the climate factors played an important role in high vegetation coverage area. Vegetation coverage played a dominant role in affecting the FT erosion intensity in zones with 0.3 ≤ VC < 0.6, whereas the terrain and climate factors played an more important role in areas with sparse vegetation (VC < 0.3) or high vegetation coverage (VC ≥ 0.6). Meanwhile, slope was of great importance in affecting the process of FT erosion in zones with slopes of >18°.