Current estimates of agricultural ammonia (NH₃) emissions in China differ by more than a factor of 2, hindering our understanding of their environmental consequences. Here we apply both bottom-up statistical and top-down inversion methods to quantify NH₃ emissions from agriculture in China for the year 2008. We first assimilate satellite observations of NH₃ column concentration from the Tropospheric Emission Spectrometer (TES) using the GEOS-Chem adjoint model to optimize Chinese anthropogenic NH₃ emissions at the 1∕2° × 2∕3° horizontal resolution for March–October 2008. Optimized emissions show a strong summer peak, with emissions about 50 % higher in summer than spring and fall, which is underestimated in current bottom-up NH₃ emission estimates. To reconcile the latter with the top-down results, we revisit the processes of agricultural NH₃ emissions and develop an improved bottom-up inventory of Chinese NH₃ emissions from fertilizer application and livestock waste at the 1∕2° × 2∕3° resolution. Our bottom-up emission inventory includes more detailed information on crop-specific fertilizer application practices and better accounts for meteorological modulation of NH₃ emission factors in China. We find that annual anthropogenic NH₃ emissions are 11.7 Tg for 2008, with 5.05 Tg from fertilizer application and 5.31 Tg from livestock waste. The two sources together account for 88 % of total anthropogenic NH₃ emissions in China. Our bottom-up emission estimates also show a distinct seasonality peaking in summer, consistent with top-down results from the satellite-based inversion. Further evaluations using surface network measurements show that the model driven by our bottom-up emissions reproduces the observed spatial and seasonal variations of NH₃ gas concentrations and ammonium (NH₄⁺) wet deposition fluxes over China well, providing additional credibility to the improvements we have made to our agricultural NH₃ emission inventory.

Light detection and ranging (LIDAR) systems based on unmanned aerial vehicles (UAVs) recently are in rapid advancement, while mini-UAV-borne laser scanning has few reported progress, notwithstanding so extensively required. This study established a pioneered mini-UAV-borne LIDAR system - Sensei, schematically with an Ibeo Lux scanner mounted on a small Align T-Rex 600E helicopter. Furthermore, the associated data processing involved in the coordinate triple, pulse intensity, and multiechoes per pulse was explored to validate its applicability for fine-scale mapping, in terms of, e.g., tree height estimation, pole detection, road extraction, and digital terrain model refinement. The feasibility and advantages of mini-UAV-borne LIDAR have been demonstrated by the promising results based on the real-measured data.

Spatial–temporal analysis of land-use/land-cover (LULC) change as well as the monitoring and modeling of urban expansion are essential for the planning and management of urban environments. Such environments reflect the economic conditions and quality of life of the individual country. Urbanization is generally influenced by national laws, plans and policies and by power, politics and poor governance in many less-developed countries. Remote sensing tools play a vital role in monitoring LULC change and measuring the rate of urbanization at both the local and global levels. The current study evaluated the LULC changes and urban expansion of Jhapa district of Nepal. The spatial–temporal dynamics of LULC were identified using six time-series atmospherically-corrected surface reflectance Landsat images from 1989 to 2016. A hybrid cellular automata Markov chain (CA–Markov) model was used to simulate future urbanization by 2026 and 2036. The analysis shows that the urban area has increased markedly and is expected to continue to grow rapidly in the future, whereas the area for agriculture has decreased. Meanwhile, forest and shrub areas have remained almost constant. Seasonal rainfall and flooding routinely cause predictable transformation of sand, water bodies and cultivated land from one type to another. The results suggest that the use of Landsat time-series archive images and the CA–Markov model are the best options for long-term spatiotemporal analysis and achieving an acceptable level of prediction accuracy. Furthermore, understanding the relationship between the spatiotemporal dynamics of urbanization and LULC change and simulating future landscape change is essential, as they are closely interlinked. These scientific findings of past, present and future land-cover scenarios of the study area will assist planners/decision-makers to formulate sustainable urban development and environmental protection plans and will remain a scientific asset for future generations.