This project conducts a statistical model using the Mann-Kendell and a Sens Slope on the completed MODIS LST mission data for analysis of climate change thermal shifts across the Republic of Kosovo. This project leverages Google Earth Engine open data to build the statistical models that are extracted and analyzed in Q-GIS. This approach uses non-parametric statistical timeseries analysis for completed MODIS LST mission data to analyze and understand day and night land surface temperature shifts over different temporal periods to gather an understanding of the current and project the future expected impacts of climate change on various developing tourist economies in the Republic of Kosovo. Water balance is utilized as a function of understanding the impacts of climate change on wine grape capacity and the attempt to functionally understand the future disruptions of climate change through linear geographic regressions. These regressions will guide the understanding of the climate changes that are occurring with the country and provide a basis for analysis to develop resilience methods. This model will be broken down into viticultural regionality to understand the dynamism of the impacts across the country. The two data sets used will be MODIS land surface temperature and Tropical Rainfall Measuring Mission which will create an understanding of surface temperature shifts within Kosovo and water balance shifts that are occurring due to climate change in Kosovo. The datasets also be correlated between each other using a Pearson’s correlation coefficient to understand if a relationship exists between land surface temperature and water balance within wine region of Kosovo. The findings of this project will reveal geographic dispersion of anomalous rain patterns and long-term temperature shifts occurring that can have disruptive impacts on agricultural production of grapes. The results will provide insights based on known geographic extent of wine grape region to determine the significant temperature changes occurring over the past 20 years and the trends for both Day and Night LST within the Republic of Kosovo. Further, the analysis will seek to develop an understanding on the immediate to long-term impacts based on the satellite data trends. Water balance data analysis will provide precipitation shifts that are occurring on a monthly basis and can be assessed with Land Surface Temperature as a means of understanding areas that are susceptible to flood-based natural hazards and amplification through increased temperatures and loss of water balance. The connection between the two can be assessed to understand systemic vulnerabilities occurring within regions that require environmental quality for success. Additionally, the project is a novel framework for timeseries analysis of bigdata to provide insights into climate change impacts on the economies of the developing world. The analysis will focus on the geographic dispersion of touristic economy assets that are being built and improve the use of big data approaches to derive an understanding of temperature changes in data poor environments. The results of this paper will leverage open datasets, an analysis of the impacts of temperature changes on the developing tourist economy in the Republic of Kosovo, and the knowledge of capacity for leveraging large geographic datasets for open climate change research. The use of big data and open modeling provides a considerable resource for governments, municipalities, and NGOs to develop an understanding of how climate change will impact their communities. The paper discusses the statistical concepts used on the MODIS complete dataset and interpretations of the results. The major concepts approached are the use of Google Earth Engine utilization for modeling remote sensed data to understand the environmental conditions being caused by climate change. The underlying data analysis and implications draw connections within local conditions and how human environmental conditions are impacted for wine tourism development. This paper does not assess the loss of economic value but rather interpret the data to understand the positionality of the underlying environmental commodity conditions such as snowpack and grape vine stock. We discuss an analysis utilization within a novel framework for open-source climate intelligence building for those regions without the resources for pay-to-use products and data. This paper will build an understanding of methodological analysis approaches with multiple models to develop and deliver products capable of informing national and regional climate adaptation strategies in both the long and short-term. The Republic of Kosovo is working towards developing many touristic economic sectors that are heavily reliant on climate including the wine region of Rahovec and Prizren, both of which face tremendous uncertainty in the face of climate change. Development of tools and technics to display the capabilities of open big data analysis and provide vital analysis into the impacts of climate change. We seek to explore the capability for utilization of open-source learning tools, to build open-data models capable of providing vital insights into the impacts of climate change in countries that have the least resources and the most risk.
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Speakers: Dustin Sanchez