Invasion, spread and establishment of invasive alien species in a new environment causes serious ecosystem perturbation and native species extinctions. The problem is further aggravated under climate change as some invasive alien species perform better under elevated temperature and carbon dioxide regimes. Currently unsuitable regions such as high-altitude areas and mountains are likely to become more suited to invasion under future climatic conditions. We have modelled the distribution of one of the most invasive alien species, parthenium weed ( Parthenium hysterophorus L.) which is rapidly colonizing different parts of Bhutan. We have implemented ensemble modelling approach using the BIOMOD2 package in R environment. Under current climate scenario, about 2.83% (1, 099.01 km 2 ) of the country’s total area is predicted to be suitable for parthenium weed invasion, covering 17 out of the 20 districts in Bhutan. Under future climate scenarios, the highest suitability is predicted under RCP4.5 2050 period with about 5, 419.69 km 2 anticipated to be suitable. Except for Bumthang, all districts show suitability to invasions under future climate scenarios. Generally, districts located in the west and south show more suitability than those in the east and central region. The highest elevation of suitability is predicted to be at 2, 931 m above sea level; an upward shift of about 753 m. Based on these findings, there is an urgent need to develop management programs and raise public awareness on the adverse impacts of parthenium weed in Bhutan.
Creating annual crop type maps for enabling improved food security decision making has remained a challenge in Bhutan. This is in part due to the level of effort required for data collection, technical model development, and reliability of an on-the-ground application. Through focusing on advancing Science, Technology, Engineering, and Mathematics (STEM) in Bhutan, an effort to co-develop a geospatial application known as the Agricultural Classification and Estimation Service (ACES) was created. This paper focuses on the co-development of an Earth observation informed climate smart crop type framework which incorporates both modeling and training sample collection. The ACES web application and subsequent ACES modeling software package enables stakeholders to more readily use Earth observation into their decision making process. Additionally, this paper offers a transparent and replicable approach for addressing and combating remote sensing limitations due to topography and cloud cover, a common problem in Bhutan. Lastly, this approach resulted in a Random Forest “LTE 555” model, from a set of 3,600 possible models, with an overall test Accuracy of 85% and F-1 Score of .88 for 2020. The model was independently validated resulting in an independent accuracy of 83% and F-1 Score of .45 for 2020. The insight into the model perturbation via hyperparameter tuning and input features is key for future practitioners.
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