Dustin Sanchez

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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Kosovo is one of the most environmentally degraded countries in Europe. It is also one of the poorest. The country lacks the capacity to conduct environmental assessments to gauge the scale of its environmental problems. It has even less capacity to understand its vulnerability to climate change and its prognosis for sustainable development. This paper describes the use of available (open) resources by the technically trained to understand environmental changes and provide a framework for developmental research that provides practical understandings of climate impacts. There tends to be a lack of awareness of the tools and scant knowledge of their use towards sustainable development. An environmental assessment of Kosovo using large and open remote-sensing data from Google Earth Engine is explained through an embedded multi-case design. Our approach used publicly available models and code walkthroughs from the book Cloud-based Remote Sensing with Google Earth Engine. The models were coded for Kosovo and the greater western Balkans region in JavaScript using Google Earth Engine open datasets to analyze environmental conditions in this region. This work demonstrates the value of free and open tool development and analysis for development of environmental sustainability. The use of open data requires careful analytical designs and the application of correct tools for specific regions and particular uses. Complex environmental conditions can muddle the data and analyses generated from open datasets. The “un-muddled” analysis performed here adds to the knowledge base of the environmental conditions within Kosovo and provides insight into regional assessment of changing climates. Models for air pollution and population exposure, groundwater monitoring with GRACE, urban environments, and deforestation viewed from multiple sensors were compiled into an environmental assessment of the scopes and scales of several environmental issues that plague Kosovo. The air pollution and population exposure model assesses the human toll of air pollution in Kosovo. Groundwater monitoring with Gravity Recovery and Climate Experiment (GRACE) appraises the health of aquifers and the security of water resources. Urban-environment analysis evaluates the changes that are occurring in urban locations in Kosovo. And the deforestation model is used to determine and evaluate the changes to several environments in Kosovo. The project will also include discussions of scalability to understand how the interconnected environmental conditions of the Balkans region can be further studies. The models, analytical frameworks, and overarching goals provide a robust strategy towards practical leveraging of remote sensed data to provide intrinsic value into developmental countries. The methods are interchangeable and replicable for climate-change analysis, sustainability decision making, and monitoring of environmental change. The urban expansion in Kosovo from 2010 till 2020 is studied with Landsat and MODIS mission data to understand the consequences of land use change. The air pollution and population exposure model employs Sentinel-5 TROPOMI and population density data to help discern air pollution levels and the human toll of environmental degradation. The groundwater monitoring application uses Gravity Recovery and Climate Experiment strives to clarify water storage capacities and trends within Kosovo’s aquifers. The forest degradation and deforestation model uses Landsat mission data to understand the changes occurring within the forests of Kosovo. The combination of these models creates a comprehensive case study of the environmental conditions within Kosovo and provides a baseline for understanding the effects of changing climates in the region. This information is crucial in developing effective strategies to address the challenges posed by climate change and to ensure a sustainable future for the region. This paper clarifies the methods used for modeling of big data sets in Google Earth Engine to generate products that can be used to assess both climate change and environmental change. We explore the frameworks for cloud computing of open-data environmental analyses by evaluating data selection and analytical techniques to provide an analytical framework for future development. Further building the cross-sectional understanding of the leverage utility of Google Earth Engine with analytical frameworks that provide utility with developing academic frameworks for resilience building and products that can traverse into government institutional knowledge building, private sector sustainable developmental gaps, public sector environmental and climate developmental strategies. The emergence of new technologies has provided opportunities for new approaches to broadly understand the impacts of global climate change and free-to-use frameworks places the capacity to understand attainable for developing countries. The use of this technology enables development of a regional understanding of climate change, its impacts, and the approaches for enhancing resilience through analysis of petabytes of open satellite data. This paper delivers a framework with which remotely sensed data can be assessed to understand how human-environment interactions in developing nations will be influenced by changing climates. These models which are all functionally different have environmental links that through development provides the future of open big data for building climate change resilience through a remote sensed top to bottom understanding of what the data means and how it can be applied.

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