5,014 research outputs found

    ์ ‘๊ทผ๋ถˆ๊ฐ€์ง€์—ญ์ธ ๋ถํ•œ์˜ ์‹œ๊ณ„์—ด ํ† ์ง€ํ”ผ๋ณต๋„ ๋งคํ•‘ ๋ฐ ์‚ฐ๋ฆผ ๋ณ€ํ™” ๋™ํ–ฅ ๋ถ„์„

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๋†์—…์ƒ๋ช…๊ณผํ•™๋Œ€ํ•™ ์ƒํƒœ์กฐ๊ฒฝยท์ง€์—ญ์‹œ์Šคํ…œ๊ณตํ•™๋ถ€(์ƒํƒœ์กฐ๊ฒฝํ•™), 2021.8. ์ด๋™๊ทผ.North Korea, as an inaccessible area, has little research on land cover change, but it is very important to understand the changing trend of LULCC and provide information previously unknown to North Korea. This study therefore aimed to construct and analyze a 30-m resolution modern time-series land use land cover (LULC) map to identify the LULCCs over long time periods across North Korea and understand the forest change trends. A land use and land cover (LULC) map of North Korea from 2001 to 2018 was constructed herein using semi-permanent point classification and machine learning techniques on satellite image time-series data. The resultant relationship between cropland and forest cover, and the LULC changes were examined. The classification results show the effectiveness of the methods used in classifying the time series of Landsat images for LULC, wherein the overall accuracy of the LULC classification results was 97.5% ยฑ 0.9%, and the Kappa coefficient was 0.94 ยฑ 0.02. Using LULC change detection, our research effectively explains the change trajectory of North Koreaโ€™s current LULC, providing new insights into the change characteristics of North Koreaโ€™s croplands and forests. Further, our results show that North Koreaโ€™s urban area has increased significantly, its forest cover has increased slightly, and its cropland cover has decreased. We determined that North Koreaโ€™s Forest protection policies have led to the forest restoration. Thus, as agriculture is one of North Koreaโ€™s main economic contributors, croplands have been forced to relocate, expanding to other regions to compensate for the land loss caused by forest restoration.๋ถํ•œ์€ ์„ธ๊ณ„์—์„œ ๊ฐ€์žฅ ์‹ฌ๊ฐํ•˜๊ฒŒ ํ™ฉํํ™”๋œ ์‚ฐ๋ฆผ ์ค‘ ํ•˜๋‚˜๋ฅผ ํฌํ•จํ•˜๊ณ  ์žˆ์ง€๋งŒ ์ตœ๊ทผ์—๋Š” ์‚ฐ๋ฆผ ๋ณต์›์„ ๊ฐ•์กฐํ•˜๊ณ  ์žˆ๋‹ค. ์‚ฐ๋ฆผ ๋ณต์›์ด ์ผ์–ด๋‚˜๋Š” ์ •๋„๋ฅผ ์ดํ•ดํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ํ† ์ง€ ์ด์šฉ๊ณผ ํ† ์ง€ ํ”ผ๋ณต ๋ณ€ํ™” ๊ฒฝํ–ฅ (LULCC)์„ ์ดํ•ดํ•ด์•ผ ํ•œ๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ๋Š” 30m ํ•ด์ƒ๋„์˜ ํ˜„๋Œ€ ์‹œ๊ณ„์—ด ํ† ์ง€ ์ด์šฉ ํ† ์ง€ ํ”ผ๋ณต (LULC)์ง€๋„๋ฅผ ๊ตฌ์„ฑ ๋ฐ ๋ถ„์„ํ•˜์—ฌ ๋ถํ•œ ์ „์—ญ์˜ ์žฅ๊ธฐ LULCC๋ฅผ ์‹๋ณ„ํ•˜๊ณ  ์‚ฐ๋ฆผ ๋ณ€ํ™” ์ถ”์„ธ๋ฅผ ์ดํ•ดํ•˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•œ๋‹ค. 2001 - 2018 ๋…„ ๊ธฐ๊ฐ„ ๋™์•ˆ ๊ตญ๊ฐ€์˜ LULC์ง€๋„๋Š” 30m ํ•ด์ƒ๋„ ์œ„์„ฑ ์ด๋ฏธ์ง€ ์‹œ๊ณ„์—ด ๋ฐ์ดํ„ฐ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ๋ฐ˜์˜๊ตฌ์  ํฌ์ธํŠธ ๋ถ„๋ฅ˜ ๋ฐ ๊ธฐ๊ณ„ ํ•™์Šต์„ ์‚ฌ์šฉํ•˜์—ฌ ๊ตฌ์„ฑ๋˜์—ˆ์œผ๋ฉฐ, ์ด๋Š” GEE (Google Earth Engine)์—์„œ ์ˆ˜์ง‘ ํ•œ ํ˜„์ƒ ํ•™์  ์ •๋ณด์™€ ํ•จ๊ป˜ ์‚ฌ์šฉ๋˜๊ณ  ์žˆ๋‹ค. ๋˜ํ•œ LULCC ํƒ์ง€๊ธฐ ๋ฒ•๊ณผ ๊ฒฝ์ž‘์ง€ ๋ณ€ํ™”์™€ ๊ณ ๋„์˜ ๊ด€๊ณ„๋ฅผ ๊ณ ๋ คํ•˜์—ฌ 2001 - 2018 ๋…„ ๋ถํ•œ์˜ ์‚ฐ๋ฆผ ๋ณ€ํ™”๋ฅผ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. LULC ๋งต ๊ฒฐ๊ณผ์˜ ์ „์ฒด ๋ถ„๋ฅ˜ ์ •ํ™•๋„๋Š” 97.5 % ยฑ 0.9 %์ด๊ณ , Kappa ๊ณ„์ˆ˜๋Š” 0.94 ยฑ 0.02 ์ด๋‹ค. LULCC ํƒ์ง€๋Š” ๋˜ํ•œ 2001 - 2018 ๋…„์— ๋ถํ•œ์˜ ์‚ฐ๋ฆผ ๋ฉด์ ์ด ์•ฝ๊ฐ„ ์ฆ๊ฐ€ํ•œ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ผ๋ฐ˜์ ์œผ๋กœ ์‚ฐ๋ฆผ ํ”ผ๋ณต ๋ฉด์ ์€ ํฌ๊ฒŒ ๋ณ€ํ•˜์ง€ ์•Š์•˜์œผ๋‚˜ ๋‚จ๋ถ€์™€ ์ค‘๋ถ€ ์ง€์—ญ์˜ ์‚ฐ๋ฆผ ๋ณต์›๊ณผ ๋ถ๋ถ€์™€ ์„œ๋ถ€์˜ ๊ฒฝ์ž‘์ง€ ์ƒ๋Œ€์  ์ฆ๊ฐ€ ์ธก๋ฉด์—์„œ ๋šœ๋ ทํ•œ ๊ณต๊ฐ„์  ๋ณ€ํ™”๊ฐ€ ๊ด€์ฐฐ๋˜์—ˆ๋‹ค. ๋ถํ•œ์˜ ํŠน์„ฑ๊ณผ ์‚ฐ๋ฆผ ์ •์ฑ… ๋ฌธ์„œ๋ฅผ ๊ฒ€ํ†  ํ•œ ๊ฒฐ๊ณผ ๋ถํ•œ ๊ทผ๋Œ€ ์‚ฐ๋ฆผ์˜ ์ผ๋ถ€ ์ง€์—ญ์ด ๋ณต์›๋˜๊ณ  ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค.Chapter 1. Introduction 1 Chapter 2. Study Area 7 Chapter 3. Materials and Methods 8 3.1. Study overview 8 3.2. Data Collection 9 3.3. Data Processing 11 3.4. Classification Process 12 3.5. LULCC Analysis 14 3.6. Reference Data Collection and Classification Accuracy Validation 15 Chapter 4. Results 17 4.1. LULC Classification Accuracy Assessment 17 4.2. LULC Classification Results 20 4.3. LULC Change Detection 22 4.4. Relation with mountainous cropland and elevation 26 Chapter 5. Discussion 28 5.1. Interpretation and explanation of the forest change in North Korea 28 5.2. Importance of spatial analysis and future research directions 30 5.3. Limits and Advantages 32 Chapter 6. Conclusion 34 Bibliography 36 Appendix 44 Abstract in Korean 51์„

    Automatic mapping of burned areas using Landsat 8 time-series images in Google Earth engine: a case study from Iran

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    Due to the natural conditions and inappropriate management responses, large part of plains and forests in Iran have been burned in recent years. Given the increasing availability of open-access satellite images and open-source software packages, we developed a fast and cost-effective remote sensing methodology for characterizing burned areas for the entire country of Iran. We mapped the fire-affected areas using a post-classification supervised method and Landsat 8 time-series images. To this end, the Google Earth Engine (GEE) and Google Colab computing services were used to facilitate the downloading and processing of images as well as allowing for effective implementation of the algorithms. In total, 13 spectral indices were calculated using Landsat 8 images and were added to the nine original bands of Landsat 8. The training polygons of the burned and unburned areas were accurately distinguished based on the information acquired from the Iranian Space Agency (ISA), Sentinel-2 images, and Fire Information for Resource Management System (FIRMS) products. A combination of Genetic Algorithm (GA) and Neural Network (NN) approaches was then implemented to specify 19 optimal features out of the 22 bands. The 19 optimal bands were subsequently applied to two classifiers of NN and Random Forest (RF) in the timespans of 1 January 2019 to 30 December 2020 and of 1 January 2021 to 30 September 2021. The overall classification accuracies of 94% and 96% were obtained for these two classifiers, respectively. The omission and commission errors of both classifiers were also less than 10%, indicating the promising capability of the proposed methodology in detecting the burned areas. To detect the burned areas caused by the wildfire in 2021, the image differencing method was used as well. The resultant models were finally compared to the MODIS fire products over 10 sampled polygons of the burned areas. Overall, the models had a high accuracy in detecting the burned areas in terms of shape and perimeter, which can be further implicated for potential prevention strategies of endangered biodiversity.Peer ReviewedPostprint (published version

    Mapping Land Coverage in the Kapuas Watershed Using Machine Learning in Google Earth Engine

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    Land cover information is essential data in the management of watersheds. The challenge in providing land cover information in the Kapuas watershed is the cloud cover and its significant area coverage, thus requiring a large image scene. The presence of a cloud-based spatial data processing platform that is Google Earth Engine (GEE) can be answered these challenges. Therefore this study aims to map land cover in the Kapuas watershed using machine learning-based classification on GEE. The process of mapping land cover in the Kapuas watershed requires about ten scenes of Landsat 8 satellite imagery. The selected year is 2019, with mapped land cover classes consisting of bodies of water, vegetation cover, open land, and built-up area. Machine learning that tested included CART, Random Forest, GMO Max Entropy, SVM Voting, and SVM Margin. The results of this study indicate that the best machine learning in mapping land cover in the Kapuas watershed is GMO Max Entropy, then CART. This research still has many limitations, especially mapped land cover classes. So that research needs to be developed with more detailed land cover classes, more diverse and multi-time input data

    Sustainable Intensification of Agriculture: Opportunities and Challenges for Food Security and Agrarian Adaptation to Environmental Change in Bangladesh

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    This dissertation investigates three unique aspects of sustainable agricultural intensification (SAI) in the context of Bangladeshi rice production. The first article presents a qualitative analysis of SAI and farmer surveys in the embanked polder region of coastal Bangladesh. The second article investigates the global food security and environmental impacts of already adopted High Yielding Variety (HYV) rice and double-cropped rice systems in Bangladesh using a spatial partial equilibrium trade model and a Life Cycle Assessment (LCA). The final article demonstrates a remote sensing methodology for monitoring dry season rice production at 30 m resolution in Bangladesh using a harmonic time series model, the Landsat archive, and Google Earth Engine. Major findings from this dissertation include: (1) agrarian communities in the polder region face food insecurity during the peak of monsoonal paddy rice production and could improve production by adopting HYV or second season crops, (2) agrarian communities in the polders identify water management issues as the primary agricultural concern, followed by pest infestation and soil salinity, (3) HYV rice provides enough additional production in Bangladesh to feed nearly 26 million Bangladeshis per annum and is more environmentally efficient than traditional rice in terms of global warming potential, land use, water use, and fertilizer use, and (4) the combination of a harmonic time series model, spectral indices, and rice phenology can produce relatively accurate predictions of dry season rice in Bangladesh compared to district-level reference information. Overall, the findings from this investigation of SAI support continued efforts to improve food security, increase agricultural output, and decrease environmental impacts in Bangladesh

    Land Degradation Assessment with Earth Observation

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    This Special Issue (SI) on โ€œLand Degradation Assessment with Earth Observationโ€ comprises 17 original research papers with a focus on land degradation in arid, semiarid and dry-subhumid areas (i.e., desertification) in addition to temperate rangelands, grasslands, woodlands and the humid tropics. The studies cover different spatial, spectral and temporal scales and employ a wealth of different optical and radar sensors. Some studies incorporate time-series analysis techniques that assess the general trend of vegetation or the timing and duration of the reduction in biological productivity caused by land degradation. As anticipated from the latest trend in Earth Observation (EO) literature, some studies utilize the cloud-computing infrastructure of Google Earth Engine to cope with the unprecedented volume of data involved in current methodological approaches. This SI clearly demonstrates the ever-increasing relevance of EO technologies when it comes to assessing and monitoring land degradation. With the recently published IPCC Reports informing us of the severe impacts and risks to terrestrial and freshwater ecosystems and the ecosystem services they provide, the EO scientific community has a clear obligation to increase its efforts to address any remaining gapsโ€”some of which have been identified in this SIโ€”and produce highly accurate and relevant land-degradation assessment and monitoring tools

    Remote Sensing Applications in Monitoring of Protected Areas

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    Protected areas (PAs) have been established worldwide for achieving long-term goals in the conservation of nature with the associated ecosystem services and cultural values. Globally, 15% of the worldโ€™s terrestrial lands and inland waters, excluding Antarctica, are designated as PAs. About 4.12% of the global ocean and 10.2% of coastal and marine areas under national jurisdiction are set as marine protected areas (MPAs). Protected lands and waters serve as the fundamental building blocks of virtually all national and international conservation strategies, supported by governments and international institutions. Some of the PAs are the only places that contain undisturbed landscape, seascape and ecosystems on the planet Earth. With intensified impacts from climate and environmental change, PAs have become more important to serve as indicators of ecosystem status and functions. Earthโ€™s remaining wilderness areas are becoming increasingly important buffers against changing conditions. The development of remote sensing platforms and sensors and the improvement in science and technology provide crucial support for the monitoring and management of PAs across the world. In this editorial paper, we reviewed research developments using state-of-the-art remote sensing technologies, discussed the challenges of remote sensing applications in the inventory, monitoring, management and governance of PAs and summarized the highlights of the articles published in this Special Issue

    Advancing large-scale analysis of human settlements and their dynamics

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    Due to the importance for a range of sustainability challenges, it is important to understand the spatial dynamics of human settlements. The rapid expansion of built-up land is among the most extensive global land changes, even though built-up land occupies only a small fraction of the terrestrial biosphere. Moreover, the different ways in which human settlements are manifested are crucially important for their environmental and socioeconomic impacts. Yet, current analysis of human settlements heavily relies on land cover datasets, which typically have only one class to represent human settlements. Consequently, the analysis of human settlements does often not account for the heterogeneity within urban environment or their subtle changes. This simplistic representation severely limits our understanding of change processes in human settlements, as well as our capacity to assess socioeconomic and environmental impacts. This thesis aims to advance large-scale analysis of human settlements and their dynamics through the lens of land systems, with a specific focus on the role of land-use intensity. Chapter 2 explores the use of human settlement systems as an approach to understanding their variation in space and changes over time. Results show that settlement systems exist along a density gradient, and their change trajectories are typically gradual and incremental. In addition, results indicate that the total increase in built-up land in village landscapes outweighs that of dense urban regions. This chapter suggests that we should characterize human settlements more comprehensively to advance the analysis of human settlements, going beyond the emergence of new built-up land in a few mega-cities only. In Chapter 3, urban land-use intensity is operationalized by the horizontal and vertical spatial patterns of buildings. Particularly, I trained three random forest models to estimate building footprint, height, and volume, respectively, at a 1-km resolution for Europe, the US, and China. The models yield R2 values of 0.90, 0.81, and 0.88 for building footprint, height, and volume, respectively. The correlation between building footprint and building height at a pixel level was 0.66, illustrating the relevance of mapping these properties independently. Chapter 4 builds on the methodological approach presented in chapter 3. Specifically, it presents an improved approach to mapping 3D built-up patterns (i.e., 3D building structure), and applies this to map building footprint, height, and volume at a global scale. The methodological improvement includes an optimized model structure, additional explanatory variables, and updated input data. I find distance decay functions from the centre of the city to its outskirts for all three properties for major cities in all continents. Yet, again, the height, footprint (density), and volume differ drastically across these cities. Chapter 5 uses built-up land per person as an operationalization for urban land-use intensity, in order to investigate its temporal dynamics at a global scale. Results suggest that the decrease of urban land-use intensity relates to 38.3%, 49.6%, and 37.5% of the built-up land expansion in the three periods during 1975-2015, but with large local variations. In the Global South, densification often happens in regions where human settlements are already used intensively, suggesting potential trade-offs with other living standards. These chapters represent the recent advancements in large-scale analysis of human settlements by revealing a large variation in urban fabric. Urban densification is widely acknowledged as one of the tangible solutions to satisfy the increased land demand for human settlement while conserving other land, suggesting the relevance of these findings to inform sustainable development. Nevertheless, local settlement trajectories towards intensive forms should also be guided in a large-scale context with broad considerations, including the quality of life for inhabitants, because these trade-offs and synergies remain largely unexplored in this analysis

    ENHANCING CONSERVATION WITH HIGH RESOLUTION PRODUCTIVITY DATASETS FOR THE CONTERMINOUS UNITED STATES

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    Human driven alteration of the earthโ€™s terrestrial surface is accelerating through land use changes, intensification of human activity, climate change, and other anthropogenic pressures. These changes occur at broad spatio-temporal scales, challenging our ability to effectively monitor and assess the impacts and subsequent conservation strategies. While satellite remote sensing (SRS) products enable monitoring of the earthโ€™s terrestrial surface continuously across space and time, the practical applications for conservation and management of these products are limited. Often the processes driving ecological change occur at fine spatial resolutions and are undetectable given the resolution of available datasets. Additionally, the links between SRS data and ecologically meaningful metrics are weak. Recent advances in cloud computing technology along with the growing record of high resolution SRS data enable the development of SRS products that quantify ecologically meaningful variables at relevant scales applicable for conservation and management. The focus of my dissertation is to improve the applicability of terrestrial gross and net primary productivity (GPP/NPP) datasets for the conterminous United States (CONUS). In chapter one, I develop a framework for creating high resolution datasets of vegetation dynamics. I use the entire archive of Landsat 5, 7, and 8 surface reflectance data and a novel gap filling approach to create spatially continuous 30 m, 16-day composites of the normalized difference vegetation index (NDVI) from 1986 to 2016. In chapter two, I integrate this with other high resolution datasets and the MOD17 algorithm to create the first high resolution GPP and NPP datasets for CONUS. I demonstrate the applicability of these products for conservation and management, showing the improvements beyond currently available products. In chapter three, I utilize this dataset to evaluate the relationships between land ownership and terrestrial production across the CONUS domain. The main results of this work are three publically available datasets: 1) 30 m Landsat NDVI; 2) 250 m MODIS based GPP and NPP; and 3) 30 m Landsat based GPP and NPP. My goal is that these products prove useful for the wider scientific, conservation, and land management communities as we continue to strive for better conservation and management practices

    Remotely sensed albedo allows the identification of two ecosystem states along aridity gradients in Africa

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    Empirical verification of multiple states in drylands is scarce, impeding the design of indicators to anticipate the onset of desertification. Remote sensingโ€derived indicators of ecosystem states are gaining new ground due to the possibilities they bring to be applied inexpensively over large areas. Remotely sensed albedo has been often used to monitor drylands due to its close relationship with ecosystem status and climate. Here, we used a spaceโ€forโ€timeโ€substitution approach to evaluate whether albedo (averaged from 2000 to 2016) can identify multiple ecosystem states in African drylands spanning from the Saharan desert to tropical Africa. By using latent class analysis, we found that albedo showed two states (low and high; the cutโ€off level was 0.22 at the shortwave band). Potential analysis revealed that albedo exhibited an abrupt and discontinuous increase with increased aridity (1 โˆ’ [precipitation/potential evapotranspiration]). The two albedo states coโ€occurred along aridity values ranging from 0.72 to 0.78, during which vegetation cover exhibited a rapid, continuous decrease from ~90% to ~50%. At aridity values of 0.75, the low albedo state started to exhibit less attraction than the high albedo state. Low albedo areas beyond this aridity value were considered as vulnerable regions where abrupt shifts in albedo may occur if aridity increases, as forecasted by current climate change models. Our findings indicate that remotely sensed albedo can identify two ecosystem states in African drylands. They support the suitability of albedo indices to inform us about discontinuous responses to aridity experienced by drylands, which can be linked to the onset of land degradation.This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant XDA19030500), the National Key Research and Development Program of China (Grant 2016YFC0503302), the European Research Council (BIODESERT project, ERC Grant Agreement 647038), the Joint PhD, Training Program of the University of Chinese Academy of Sciences, and the Research Foundation of Henan University of Technology (Grant 31401178)

    Modeling the Egyptian Goose (Alopochen aegyptiaca) Invasion; and Future Concerns

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    In an increasingly interconnected world, the ecological and financial cost of invasive species is expected to continue to climb through the movement of exotic biota. Understanding the driving forces behind how a species invades, what environments promote their establishment, and what impacts they are likely to have on the invaded environment are all critical for management. Waterfowl, order Anseriformes, are one such category of invasive species of concern due to their popularity of accidental introduction, ease of movement, and propensity to affect both terrestrial and aquatic ecosystems. The Egyptian Goose (Alopochen aegyptiaca) is a native to the African continent that spread and established itself as a damaging invasive species in Europe in the 1700s and is now an incipient invader in North America. Much is unknown about the future of the invasion of the Egyptian Goose in North America. Understanding habitat suitability of the species can help predict areas where the species may invade in the future and highlight regions of immediate management concern. Furthermore, understanding how previous invasive waterfowl have influenced North America and how the Egyptian Goose has interacted both in its established invaded range and its native range, can help predict what could occur with the incipient invasion. The goal of this work is to 1. Establish concerns about the Egyptian Goose invasion through a literature review of the current and historical impacts of invasive waterfowl in North America 2. Model the invasion of the Egyptian Goose. To establish the concerns about the Egyptian Goose invasion in North America, we performed a systematic literature review. We used the PRISMA 2010 guidelines for performing holistic and quality literature reviews as well as the โ€˜litsearchrโ€™ package in R to improve the quality of search terms. Our results show that these species are significant reservoirs of multiple diseases, including Escherichia coli (E. coli), Avian Paramyxovirus-1 (Newcastle disease), and avian influenza. Additionally, we found considerable gaps in the literature; particularly, field studies of newer invasive species and direct interactions with native avifauna. We found key gaps where the Egyptian Goose could pose a novel threat to North American ecosystems. To understand the invasion of the Egyptian Goose, we utilized Species Distribution Modeling techniques through Random Forest Classified modeling in Google Earth Engine. The volume of historical and current distribution data from eBird, as well as the three distinct geographical locations, allowed for a robust test of adaptations of invading species. We found strong evidence to support the niche shift hypothesis for the Egyptian Goose. Suitable climate conditions strongly varied between continents with Africa and North America having similarly median annual temperatures (20.6oC and 20.7oC) while Europe had a significantly lower median annual temperature (11.0oC). Egyptian Geese showed increasing affinity for urban environments with invasion stage doubling from Africa to Europe and tripling from Africa to North America. The strength of the suitability of highly urbanized areas increasing with recency of arrival suggests that urban environments may be acting as foothold habitat for the Egyptian Geese
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