6 research outputs found
Remotely-Sensed Early Warning Signals of a Critical Transition in a Wetland Ecosystem
The response of an ecosystem to external drivers may not always be gradual and reversible. Discontinuous and sometimes irreversible changes, called 'regime shifts' or 'Critical transitions', can occur. The likelihood of such shifts is expected to increase for a variety of ecosystems, and it is difficult to predict how close an ecosystem is to a critical transition. Recent modelling studies identified indicators of impending regime shifts that can be used to provide early warning signals of a critical transition. The identification of such transitions crucially depends on the ability to monitor key ecosystem variables, and their success may be limited by lack of appropriate data. Moreover, empirical demonstrations of the actual functioning of these indicators in real-world ecosystems are rare. This paper presents the first study which uses remote sensing data to identify a critical transition in a wetland ecosystem. In this study, we argue that a time series of remote sensing data can help to characterize and determine the timing of a critical transition. This can enhance our abilities to detect and anticipate them. We explored the potentials of remotely sensed vegetation (NDVI), water (MNDWI), and vegetation- water (VWR) indices, obtained from time series of MODIS satellite images to characterize the stability of a wetland ecosystem, Dorge Sangi, near the lake Urmia, Iran, that experienced a regime shift recently. In addition, as a control case, we applied the same methods to another wetland ecosystem in Lake Arpi, Armenia which did not experience a regime shift. We propose a new composite index (MVWR) based on combining vegetation and water indices, which can improve the ability to anticipate a critical transition in a wetland ecosystem. Our results revealed that MVWR in combination with autocorrelation at-lag-1 could successfully provide early warning signals for a critical transition in a wetland ecosystem, and showed a significantly improved performance compared to either vegetation (NDVI) or water (MNDWI) indices alone.Peer reviewe
Exacerbated grassland degradation and desertification in Central Asia during 2000-2014.
Grassland degradation and desertification is a complex process, including both state conversion (e.g., grasslands to deserts) and gradual within-state change (e.g., greenness dynamics). Existing studies hardly separated the two components and analyzed it as a whole based on time series vegetation index data, which cannot provide a clear and comprehensive picture for grassland degradation and desertification. Here we propose an integrated assessment strategy, by considering both state conversion and within-state change of grasslands, to investigate grassland degradation and desertification process in Central Asia. First, annual maps of grasslands and sparsely vegetated land were generated to track the state conversions between them. The results showed increasing grasslands were converted to sparsely vegetated lands from 2000 to 2014, with the desertification region concentrating in the latitude range of 43-48° N. A frequency analysis of grassland vs. sparsely vegetated land classification in the last 15 yr allowed a recognition of persistent desert zone (PDZ), persistent grassland zone (PGZ), and transitional zone (TZ). The TZ was identified in southern Kazakhstan as one hotspot that was unstable and vulnerable to desertification. Furthermore, the trend analysis of Enhanced Vegetation Index during thermal growing season (EVITGS ) was investigated in individual zones using linear regression and Mann-Kendall approaches. An overall degradation across the area was found; moreover, the second desertification hotspot was identified in northern Kazakhstan with significant decreasing in EVITGS , which was located in PGZ. Finally, attribution analyses of grassland degradation and desertification were conducted by considering precipitation, temperature, and three different drought indices. We found persistent droughts were the main factor for grassland degradation and desertification in Central Asia. Considering both state conversion and gradual within-state change processes, this study provided reference information for identification of desertification hotspots to support further grassland degradation and desertification treatment, and the method could be useful to be extended to other regions
40-Years of Lake Urmia Restoration Research: Synthesis and Next Steps
Lake Urmiaβs desiccation and recent nascent recovery have garnered international and Iranian attention. Lake restoration at this scale requires integration across many sciences, technology, engineering, management, and governance topics. Here, we synthesized 544 peer-reviewed articles on Lake Urmia indexed in the Scopus database, answered nine restoration questions of scientific and popular interest, and recommended next steps for consequential lake restoration. We find: (1) research on diverse topics is fragmented and needs more integration. (2) Ecological and limnological studies have mostly focused on salinity, Artemia, and Flamingos. (3) Dust from the dry lakebed and nearby regions has negatively impacted human health. (4) Most research seeks to restore the lake to a single, uniform level of 1274.1 m thought to recover Artemia. (5) The lakeβs north and south arms have different chemical and physical properties but researchers disagree on how newly breaching the causeway that separates the arms will impact salinities, evaporation, and ecosystems. (6) Expanding irrigated agriculture, dam construction, and mismanagement had a larger impact on lake decline than temperature increases and precipitation decreases. (7) The Iranian governmentβs 5-year recovery effort helped raise lake level about 1 m and immobilize lakebed dust. (8) Only one study publicly shared data, and only three studies described engagement with stakeholders or managers. (9) Numerous suggestions to improve economic conditions, work with farmers, or change farmer-government processes require varying effort and most still require implementation. We see next steps for lake recovery to monitor ungauged or poorly characterized water flows throughout the basin; develop alternative livelihoods beyond agriculture; describe the entire food web that supports migratory birds; manage for diverse ecosystem objectives and their associated lake levels; adapt basin water management to available water and lake evaporation; build capacity to share data, models, and code; train researchers in data-sharing tools and best practices; and better connect research topics, researchers, stakeholders, and managers. All of our findings and next steps encourage Lake Urmia managers to extend restoration efforts beyond five years and cultivate more public support
Using remote sensing to assess ecosystem resilience
Vegetation ecosystems are increasingly under pressure from both direct human influence and indirect anthropogenically-driven climate change. Increasing amounts of data are made available from satellite systems which can image these ecosystems from afar. The work in this thesis provides several examples of the utility of remotely sensed data from satellites to assess the resilience of ecosystems. This notion of resilience is measured by considering the return rate following a perturbation, with statistical metrics such as AR(1) and variance providing an indication of system resilience and the proximity to a potential tipping point. The first focus of this work is on direct human environmental intervention through community-based agroforestry groups in Kenya. These results show that the efforts of these groups can be detected with satellite data as a greening trend which occurs both within designated tree planting groves and in the surrounding landscape. These groups provide a case study for the power of positive social tipping points to achieve environmental improvement. Following this, the potential of high-resolution satellite data from Sentinel-2 to quantify patterned vegetation in the Sahel is explored. These striking patterns have often been associated with vegetation resilience in drylands. No correlation is found between pattern morphology and resilience, contrary to a previously held hypothesis from the literature. Precipitation is also identified as a key driver of these patterns. Moving beyond drylands, satellite data is utilised at a global scale to assess the link between vegetation resilience and climatic variables across the world. There is a clear relationship between average resilience, as measured by AR(1), and precipitation, which is evident at three spatial scales; the local (pixel), ecoregion and biome. There is also a temperature component, with hotter, drier locations displaying lower levels of resilience. This thesis finishes with a discussion of the potential for a resilience sensing framework constructed by combining remote sensing data with new cloud computing technologies. This will enable the monitoring of resilience change across the world and the identification of regions which require further investigation and intervention.Leverhulme Trus
1990λ λ λΆν λκΈ°κ·Όμ μ€μ¬μΌλ‘
The North Korean Famine, which is occurred in the mid-1990s, resulted in hundreds of thousands of peopleβs death or the agony of starvation. More than 20 years later, the famine has left many adverse sequelae on the North Korean economy, society, and public health. The series of natural disasters in the early 1990s, economic isolation caused by the soviet bloc collapses, and inefficiency of the agro-economic system are commonly acknowledged as causes of this famine. However, land degradation, the decline in land productivity resulted from these factors, and declining food production, caused by land degradation, are the fundamental causes of this famine. The land degradation and declining food production issues are crucial not only for North Koreaβs well-being but also for humankind in terms of about a billion people experiences the risk of hunger stemming from these issues.
The land degradation issue considers a social-ecological system(SES), which consists of biophysical components of the soil ecosystem and dynamics of socio-economical land-use change. The social-ecological system is a typical example of the complex adaptive system(CAS) resulting from a complex interaction among components. Moreover, the relationship between land degradation and declining food production is a feedback loop regarding their circular causality. The feedback loop of these issues can amplify the nonlinearity of unpredictable effects. Besides, a lack of data and ignorance of the interrelationship of the social-ecological system in North Korea make their problem more complex and uncertain. Consequently, the process of land degradation and declining food production in North Korea could interpret as a complex adaptive social-ecological system.
This study aims to develop a precautionary approach to mitigate the risk of land degradation and food shortage in North Korea, applying the social-ecological system perspective and complex adaptive systems methodologies. Chapter 2 attempts to reinterpret previous studies and data on these issues in a social-ecological system perspective and reconstruct these issues as a social-ecological process model. Chapter 3 apply the methods for detecting early warning signal of critical transition(EWS) of the complex adaptive system to find the threshold from land degradation to famine in North Korea. Finally, chapter 4 sets a conceptual model representing the agricultural social-ecological system of North Korea and builds a multi-agent system for land-use and cover change(MAS-LUCC) for emulating conceptual models and the 1990βs North Korean famine.
The results of this study are summarized as follows :
Firstly, Social-ecological reinterpreting and reconstructing the land degradation and declining food production in North Korea can reveal relations between social-ecological components and distinguish Whitebox-like and Blackbox-like features. By reassessing the studies of North Korea in a social-ecological context, it was possible to understand the gap among disciplinaries of land degradation and declining food production research. The lack of information on the interrelation between these issues could be partially recognized by analogy in literatureβs historical and political background. However, the statistical analysis was not easy to interpret for quantifying these interrelations, especially the human decision-making process and Blackbox-like natural processes. It implies that translating this social-ecological system needs a complex adaptive system approach.
Secondly, I discovered a wide range of evidence of early warning signals from many spatio-temporal data before the North Korea famine occurred. These spatio-temporal data consists of land degradation indices (e.g. NDVI), food production statistics, etc. In the early 1980s, patterns of these data, mainly land degradation data, show critical slowing down patterns inferred to decrease in resilience gradually. In the late 1980s, another early warning signal pattern was added to existing signals that imply flickering or approaching bifurcation and thresholds. In the early 1990s, food production data dramatically started to show early warning signals. These processes Increasing early warnings from the 1980s imply that the transition of North Korean famine had been complex adaptive emergence. And These results provide the reasoning for the North Korean famine process, such as feedback loops among factors, to interpret the simulation data of chapter 4. Moreover, about five yearsβ time gaps between detecting early warnings imply that North Korean authorities could have time for preventing this disaster. Some papers reviewed in chapter 2 pointed out their perception of risk. Despite evidence of their awareness and action, it is not enough time or strategy to avoid the risk of famine.
Thirdly, the conceptual model reflects the social-ecological system of North Korean agriculture, defined as an abstract (artificial) cooperative farm. And I made this conceptual model build an MAS-LUCC model to imitate the 1990s famine in North Korea in terms of complexity perspective. The simulation results of the test model, which set AD 1960 to the initial year, indicates that the famine could occur about 35 years later, AD 1995 in the real world. Moreover, these results show the tipping point suggests critical transition(20 and 30 years later from the initial), and different tipping point patterns of model data imply processing like a feedback loop. The simulation result, which assumes the change of amount food import or aid from foreign countries, shows that increasing the external food supply could delay the famineβs time. However, these scenarios could not remove the risk of famine and land degradation, which suggests the need for another essential and alternative solution such as mitigation of ecological pressure.
This study could provide some lessons for the sustainability of North Korea, the Korean Peninsula, and the world. First, for making strategy to the resilience of the social-ecological system of North Korea, decision-makers should be aware of the time gap of early warning or process and surprising due to complexity. Therefore, they should build a long-term holistic approach to deal with problems. Second, decision-makers and researchers should use MAS-LUCC based decision support systems to handle complex adaptive components in the social-ecological system. Third, North Korea always has to aim βopen systemβ to delay famine risk. Fourth, We all have to reduce ecological pressure so far as our ability to eliminate land degradation and risk of hunger worldwide.
As this study finally aims to a precautionary approach to the worldwide risk of land degradation and food shortage risk, this study may apply to other regions or other social-ecological issues. Moreover, as this study may be one of the first studies on North Korea deal with the complexity, this study could be one of the models of interdisciplinary research of North Korea issues. Additional research is needed to expand for sustainability North Korea, Korena Peninsula, and the world.1990λ
λ μ€λ°μ λΆν λκΈ°κ·Όμ λΉμ μμλ§μ μ¬λμ κΈ°μμ κ³ ν΅μ λΉ νΈλ ΈμΌλ©°, 20μ¬ λ
μ΄ μ§λ νμ¬κΉμ§λ λΆνμ λκΈ°κ·ΌμΌλ‘ μΈν κ²½μ Β·μ¬ν·보건 μ λ°μ κ±ΈμΉ νμ μ¦μ κ²ͺκ³ μλ€. μ΄ λκΈ°κ·Όμ μμΈμΌλ‘ μμ°μ¬ν΄μ κ³ λ¦½, 체μ μ λΉν¨μ¨μ± λ±μ΄ μ κΈ°λλ€. κ·Έλ¬λ κ·Όλ³Έμ μΈ λΆν λκΈ°κ·Όμ μμΈμ κΈ°μ‘΄ μ κΈ°λ μμΈμ΄ 볡ν©μ μΌλ‘ μμ©νμ¬ ν μ§μ μμ°μ±μ λ¨μ΄νΈλ¦¬λ ν μ§ν©νν(land degradation) λ¬Έμ μ μ΄λ‘ μΈν μλμμ° κ°μ λ¬Έμ λ€. ν μ§ν©ννμ μλμμ° κ°μ λ¬Έμ λ λΆνμ ν¬ν¨ν΄ μ μΈκ³ μΈκ΅¬ μ€ 10μ΅ λͺ
μ΄μμ΄ κ²½ννλ λ¬Έμ λ‘, λΆνλ§μ λ¬Έμ κ° μλ μΈλ₯μ μ§μκ°λ₯μ±μ μννλ μ£Όμ λ¬Έμ μ€ νλλΌ ν μ μλ€.
ν μ§ν©νν λ¬Έμ λ ν μ§μ μν νκ²½μ μμμ ν μ§μ΄μ©μ μ¬νκ²½μ μ λμΈμ΄ 볡ν©μ μΌλ‘ μνΈ μμ©νλ μ¬νμνμμ€ν
(SocialβEcological System) λ¬Έμ λ€. μ¬νμνμμ€ν
ꡬμ±μμμ λ€μν μνΈμμ©μ λ¬Έμ μ 볡μ‘μ±μ λμ΄κ³ μ΄ν΄λ₯Ό μ΄λ ΅κ² νλ€. λν ν μ§ν©ννμ μλμμ° κ°μ λ¬Έμ λ μλ‘κ° μμΈμ΄μ κ²°κ³Όλ‘ μμ©νλ λλ¨Ήμ ꡬ쑰μ λ¬Έμ λ‘, λ¬Έμ κ° λΉμ νμ μΌλ‘ μ¦νλκ³ μ΄λ ν λ°©ν₯μΌλ‘ λ³νν μ§λ₯Ό μμΈ‘νκΈ° μ΄λ ΅κ² λ§λλ μμΈμΌλ‘ μμ©νλ€. λ°λΌμ λΆνμ ν μ§ν©ννμ μλμμ° κ°μ λ¬Έμ λ μ¬νμνμμ€ν
μμ κΈ°μΈνλ 볡μ‘μ μκ³(complex adaptive system) ννλΌκ³ λ³Ό μ μμΌλ©°, μ΄ λλ¬Έμ ν μ§ν©νν λ¬Έμ λ νλ‘μΈμ€μ λν μ΄ν΄μ μμΈ‘μ΄ μ΄λ ΅λ€κ³ μλ €μ Έ μλ€. νΉν λΆνμ ν μ§ν©νν λ¬Έμ λ λΆν μ 보μ λΆμ‘±κ³Ό νμ κ° λΆμ λ μ°κ΅¬ νν λ‘ λ κ°μ€λ 볡μ‘μ±μ λ¬Έμ μ μλ¬λ¦¬κ³ μλ€.
μ΄ μ°κ΅¬μμλ λΆνμ ν μ§ν©ννμ μλμμ° κ°μ λ¬Έμ λ₯Ό μ¬νμνμμ€ν
κ³Ό 볡μ‘μ μκ³μ λ°©λ²λ‘ μ ν΅ν΄ μ κ·Όν¨μΌλ‘μ¨ ν μ§ν©νν λκΈ°κ·Όμ λν μ¬μ μλ°© μ κ·Ό(precautionary approach)μ ν΅ν΄ μνμ μ κ°νλ λ°©μμ λͺ¨μνκ³ μ νλ€. μ΄λ₯Ό μν΄ μ¬νμνμμ€ν
μ κΈ°λ°μΌλ‘ κΈ°μ‘΄ λΆνμ ν μ§ν©ννμ μλμμ° μ°κ΅¬μ μλ£λ₯Ό μ¬κ΅¬μ±νμλ€(2μ₯). λν 볡μ‘μ μκ³ λ°©λ² μ€ μ‘°κΈ°κ²½λ³΄μ νΈ νμ§ κΈ°λ²(EWS)μ μ μ©ν¨μΌλ‘μ¨ μμΈ‘μ΄ μ΄λ €μ΄ λΆν ν μ§ν©ννμ μλμμ° κ°μ μνμ μνμ Β·ν΅κ³μ μ§νλ₯Ό λͺ¨μνμλ€(3μ₯). κ·Έλ¦¬κ³ λΆνμ ν μ§ν©ννμ μλμμ° κ°μ λ¬Έμ λ₯Ό λννλ λͺ¨νμ μ€κ³νκ³ , 볡μ‘μ μκ³ κΈ°λ° λͺ¨νν κΈ°λ²μΈ λ€νμμμμ€ν
(MAS-LUCC)μ μ΄μ©ν΄ λͺ¨νμ ꡬνν¨μΌλ‘μ¨ 1990λ
λ λΆν λκΈ°κ·Όμ μ¬ν κ°λ₯μ±μ νμΈνμ¬ μ¬μ μλ°©μ λμμ λν΄μ νκ°νκ³ μ νμλ€(4μ₯). κ²°κ³Όλ₯Ό μμ½νλ©΄ λ€μκ³Ό κ°λ€.
첫째, μ¬νμνμμ€ν
κΈ°λ°μΌλ‘ λΆνμ ν μ§ν©ννμ μλμμ° κ°μμ κΈ°μ‘΄ μ°κ΅¬μ μ§μμ μ¬κ΅¬μ±ν¨μΌλ‘μ¨, κΈ°μ‘΄ μ°κ΅¬μ μλ£κ° κ°μ§κ³ μλ μ¬νμνμ κ°μΉλ₯Ό νμΈνκ³ νκ³λ₯Ό μΈμν μ μμλ€. λΆνμ ν μ§ν©ννμ μλμμ° κ°μμ λν κ°κ°μ μ°κ΅¬μ±κ³Όλ₯Ό νμΈν¨μΌλ‘μ¨ νΈν₯κ³Ό μΈμ μ°¨μ΄λ₯Ό νμΈνμλ€. λν, λΆνμ κ΄ν μμ¬μ μ μΉλ¬Έν μ λ§₯λ½μ λ°νμΌλ‘ μ΄λ€ μ¬μ΄μ μμΈμ μΆμΆνκ³ μ¬νμνμμ€ν
κΈ°λ°μ κ°λ
λͺ¨νμΌλ‘ ꡬμΆν μ μμλ€. κ·Έλ¬λ ꡬ체μ μΈ κ΄κ³λ₯Ό μμ λλ μλ£κΈ°λ°μΌλ‘ 보μνλ κ²μ μλ£ μ체μ λΆμ‘±κ³Ό κΈ°λ² λ±μ νκ³λ‘ λ€μ λΆμ‘±ν κ²μ΄ νμ€μ΄μλ€. νΉν μΈκ°μ μμ¬κ²°μ κ³Ό ν μ§ν©ννμ ν΅μ¬μ νλ‘μΈμ€λ λΆμμ μΌλ‘ μ κ·Όνλ λ° νκ³κ° μμμΌλ©°, λμμ λͺ¨μμ΄ νμν¨μ νμΈν μ μμλ€.
λμ§Έ, 1990λ
λ λΆν λκΈ°κ·Όκ³Ό ν μ§ν©νν νμμλ μ¬λ¬ μ°¨μμ μ¬μ μ§νλ₯Ό νμΈν μ μμλ€. 1960-1990λ
λ λΆνμ μλκ°μ λ¬Έμ μ ν μ§ν©νν λ¬Έμ μ μ°κ΄λ λ€μν μκ³΅κ° μ§νμ μκ³μ΄ μ§νλ₯Ό λμμΌλ‘ 쑰기경보μ νΈλ₯Ό ν¬μ°©νλ μ°κ΅¬λ₯Ό μ§ννμλ€. κ·Έ κ²°κ³Ό 1980λ
λ μ΄μλ μ μ§μ μΈ λ³νλ₯Ό λνλ΄λ 쑰기경보μ νΈκ° ν μ§ν©νν κ΄λ ¨ μ§νλ₯Ό μ€μ¬μΌλ‘ λνλ¬μΌλ©°, 1980λ
λ νλ°μλ ν μ§ν©νν κ΄λ ¨ μ§νλ₯Ό μ€μ¬μΌλ‘ λ ν° μνμ λνλ΄λ 쑰기경보μ νΈκ° λνλ¬κ³ , 1990λ
λ μ΄μλ μλμμ° κ΄λ ¨ μ§νμμ 쑰기경보μ νΈκ° λνλ¬λ€. μ΄λ₯Ό ν΅ν΄ λ¨μν 쑰기경보μ νΈ λμΆ λ° μμ μ νμ
ν μ μμμ λΏ μλλΌ, λλ¨Ήμ ꡬ쑰μ μ°½λ°νμ κ°μ 볡μ‘μ μκ³ νλ‘μΈμ€λ₯Ό μ μΆν μ μμμΌλ©° μ΄λ 4μ₯μ λͺ¨ν κ²°κ³Ό ν΄μμλ νμ©ν μ μμλ€. λν ν μ§ν©νν μ§νμ μλμμ° μ§ν κ°μ μ½ 5λ
κ°μ μμ°¨λ κΈ°μ‘΄ μ°κ΅¬μμ νμΈν μ μλ― 1990λ
λ λκΈ°κ·Όμ μ¬μ μ§νκ° μ μ΄λ 1980λ
λ νλ°μλ νλ©΄νλμλ€λ κ²μ μμνλ κ²°κ³Όλ€. κ·Έλ¬λ λμνκΈ°μλ μΆ©λΆν μκ°μ΄ λΆμ‘±νμκ±°λ μλͺ»λ λμμ΄μμ κ²μ΄λΌλ κ² λν λ¬Έν μ°κ΅¬λ₯Ό ν΅ν΄ νμΈν μ μμλ€. μ΄λ μλΉ μμ κΈ°μ‘΄ μ°κ΅¬μ μλ£μμ κ΅μ°¨κ²μ¦ν μ μμλ€.
μ
μ§Έ, λΆνλμ
μ μ¬νμνμμ€ν
μ λνν μ μλ κ°μμ νλλμ₯ κ°λ
λͺ¨νμ ꡬμΆνκ³ , μ΄λ₯Ό 1990λ
λ λΆν λκΈ°κ·Όμ μ¬νν μ μλ 볡μ‘μ μκ³ κΈ°λ° λ€νμμμμ€ν
μΌλ‘ ꡬνν μ μμλ€. λͺ¨μ κ²°κ³Ό νμ€μμ λνλ¬λ κΈκ²©ν 1990λ
λ λΆν λκΈ°κ·Όμ ννκ° μ μ¬νκ² μ¬νλμλ€. λν μ΄ κ²°κ³Όμμλ λͺ¨μ ν 20λ
ν(1980λ
μ ν)μ 30λ
ν(1990λ
μ ν)μ λ³κ³‘μ μ΄ λνλλλ°, μ΄λ λλ¨Ήμμ ꡬ쑰μ κ°μ ν μ§ν©ννμ μλμμ° κ°μμ 볡μ‘μ μκ³μ νλ‘μΈμ€ λ³νλ₯Ό λνλΈλ€. κ·Έλ¦¬κ³ μΈλΆ μλμ§μ μλ리μ€λ₯Ό ν΅ν΄ μΈλΆ μμ μ κ³΅μ΄ μλΉ λΆλΆ λΆνμ ν μ§ν©ννμ μλμμ° μ ν μνμ μ§μ°μν¬ μ μλ€λ κ²°λ‘ μ λ΄λ¦΄ μ μμλ€. κ·Έλ¬λ λͺ¨νμμλ λκΈ°κ·Όμ λ°μ μ체λ λ§μ μ μμμΌλ©°, μΈκ΅¬μ μνμλ ₯μ μ€μ΄λ λ± λ€λ₯Έ ꡬ체μ μΈ λμμ΄ νμν¨μ νμΈν μ μμλ€.
μ΄ μ°κ΅¬μ κ²°κ³Όλ λΆνκ³Ό νλ°λμ μ§μκ°λ₯μ± μ μ±
μ립μ ν¨μλ₯Ό κ°μ§λ€, ν μ§ν©ννμ μλμμ° λΆμ‘± λ¬Έμ λ₯Ό ν¬ν¨ν λΆνμ μ¬νμνμμ€ν
μ λν μ μ±
μ립μ μν΄μλ μμΈμ±κ³Ό μμ°¨λ₯Ό κ³ λ €ν μ₯κΈ° κ΄μ°°κ³Ό, μ¬νκ²½μ μ μμ°μνμ μμλ₯Ό ν¬κ΄νλ μ’
ν©μ μΈ μ κ·Όμ΄ νμνλ€. λν 볡μ‘μ μκ³ λ°©λ²λ‘ μ μ μ©ν¨μΌλ‘μ¨ μ μ±
μμ¬κ²°μ μ΄ μ΄λ ν νκΈν¨κ³Όλ₯Ό λΆλ¬μΌμΌν¬μ§λ₯Ό νμΈνκ³ μμ¬κ²°μ μ μνν νμκ° μλ€. ꡬ체μ μΈ κΈ°κ·Ό λ°©μ§ λμ±
μΌλ‘ λ¨κΈ°μ μΌλ‘λ λΆνμ νμκ³μμ ννΌνλλ‘ ν μ μ±
μ μΈ λμμ λ§λ ¨ν νμκ° μκ³ , μ₯κΈ°μ μΌλ‘λ λΆνμ ν¬ν¨ν΄ μ μΈκ³μ μΌλ‘ μΈκ΅¬μ μνμλ ₯μ μ€μ΄λ λ°©μμ λͺ¨μν νμκ° μλ€.
μ΄ μ°κ΅¬λ λΆν, νλ°λ, κ·Έλ¦¬κ³ μΈκ³μ μΌλ‘ μ¬ν κ°λ₯μ±μ΄ ν° κΈ°κ·Όκ³Ό ν μ§ν©νν λ¬Έμ μ μν μ κ°μ μν κ΅νκ³Ό λμμ μ μνλ€λ μ μμ κ·Έ μμκ° μλ€. νΉν ν μ§ν©νν λ¬Έμ λΏ μλλΌ λ€λ°©λ©΄μ νκ²½λ¬Έμ μ μ¬νμνμμ€ν
μ μ μ©ν μ μλ 쑰기경보μ νΈ ν¬μ°© μ°κ΅¬μ κ°μ μ΅μ μ°κ΅¬κΈ°λ²μ μ μ©νκ³ , νμ κ° μΈμμ°¨μ μ§μ μ°¨μ΄λ₯Ό 극볡νλ λμμ μ μνμλ€λ μ μμ νμ μ μΈ μλ―Έκ° μλ€. λν μ§μ μ μΈ λΆν μ μ±
μλ¦½λΏ μλλΌ μ μ±
μ μ λ°μ μΈ κΈ°μ‘° μ€μ κ³Ό νκ°μκΉμ§ μμλ₯Ό μ 곡νμλ€λ μ μμ μλ―Έκ° ν¬λ€κ³ ν μ μλ€.μ 1 μ₯ μ λ‘ 1
μ 1 μ μ°κ΅¬λ°°κ²½κ³Ό λͺ©μ 1
1. μ°κ΅¬μ νμμ± 1
2. κ΄λ ¨ μ°κ΅¬λν₯κ³Ό κ³Όμ 5
1) μ μΈκ³μ μ°¨μμ ν μ§ν©νν μ°κ΅¬λν₯ 5
2) 1990λ
λ λΆν λκΈ°κ·Όκ³Ό λΆν ν μ§ν©νν μ°κ΅¬λν₯κ³Ό κ³Όμ 9
3) μ¬νμνμ μ κ·Όλ²κ³Ό 볡μ‘μ μκ³μ μΈμ체κ³μ νμμ± 11
3. μ κ·Όλ°©λ²κ³Ό μ°κ΅¬μ§λ¬Έ 15
4. μ°κ΅¬λͺ©μ 17
μ 2 μ μ°κ΅¬λμκ³Ό λ²μ 18
1. μ°κ΅¬μ μ곡κ°μ λ²μ 18
2. μ£Όμ κ°λ
μ μ μμ λ²μ μ€μ 21
μ 3 μ μ°κ΅¬μ κ΅¬μ± 24
μ 2 μ₯ λΆν ν μ§ν©ννμ μλμμ° κ°μμ λν μ¬νμνμ μ κ·Ό 26
μ 1 μ λΆν ν μ§ν©νν νν©κ³Ό μ°κ΅¬κ²½ν₯ 27
1. ν΅κ³ λ° μ λΆλ°κ° μλ£ μ€μ¬μ ν μ§ν©νν μ°κ΅¬ 29
2. ν μ§ν©ννμ λν μμ±μμ κΈ°λ° μ°κ΅¬ 33
3. ν μ§ν©ννμ λν μ°κ΅¬ μ’
ν© 37
μ 2 μ λΆνμ μλμμ° κ°μκ²½ν₯κ³Ό κ΄λ ¨ μ°κ΅¬λν₯ 39
1. ν΅κ³λ¬ΈνκΈ°λ° λΆνμ μλμμ° κ²½ν₯ 42
2. μλμμ°μ λ³ν μμΈκ³Ό νκΈν¨κ³Όμ κ΄ν ν΅κ³μ λ¬Έν 49
1) λμ
μμ°λμ νκΈν¨κ³Ό(κΈ°κ·Ό)μ κ΄λ ¨λ ν΅κ³μ λ¬Έν 49
2) λμ
μμ°λ λ³νμ μ¬νκ²½μ μ μμΈκ³Ό κ΄λ ¨λ ν΅κ³μ λ¬Έν 52
3) μλμμ° λ³νμ μμ°νκ²½ μμΈ μ€ κΈ°νμ κ΄λ ¨λ ν΅κ³μ λ¬Έν 54
3. λΆνμ μλμμ° μλ£μ μμΈλ€ κ°μ μ°κ΄μ± μ°κ΅¬λν₯ 57
1) μΈλ¬Έμ¬νμλ£μ μλμμ°λκ³Όμ κ΄κ³ 57
2) μμμ§μμ μλμμ°λκ³Όμ κ΄κ³μ± 59
3) μλμμ°λ-μμμ§μ-νκ²½μ 보μμ κ΄κ³ 63
4. λΆνμ μλμμ° λ³νμ κ΄ν μμ½κ³Ό νκ³ 65
μ 3 μ λΆν ν μ§ν©ννμ μλμμ° κ°μμ λν μ μμ μ κ·Ό 66
1. λΆν ν μ§ν©ννμ μλμμ° κ°μμ μμ¬μ λ§₯λ½ 67
2. 1990λ
λ λΆν λκΈ°κ·Όκ³Ό ν μ§ν©νν μμΈ λμΆ 76
1) μ¬νκ²½μ μ μ λμ μμΈ 77
2) μμ°νκ²½μ μμΈ 79
3. μ¬νμνμμ€ν
λ§₯λ½μμμ κ°λ
λͺ¨ν λμΆ 81
μ 4 μ λΆν ν μ§ν©ννμ μλμμ° κ°μλ¬Έμ μμΈλ€ κ°μ κ΄κ³ 84
1. κ΄λ ¨ μλ£μ λΆμλ°©λ² 84
2. μκ°μλ£ κ°μ κ΄κ³μ±μ ν΅ν μλμμ°λ μΆμ λͺ¨ν κ°λ° 90
3. μ곡κ°μλ£ κ°μ κ΄κ³μ± νμ
κ³Ό μμμ§μ μμΈ‘λͺ¨ν νμ 96
4. λ―Έμμ κ΄κ³μ± νμ
μ ν΅ν κ°λ
λͺ¨ν νκ° 110
1) μλμμ°λμμμ§μ μμΈ‘λͺ¨νκ³Ό κ°λ
λͺ¨νκ³Όμ λΉκ΅μ νκ³ 110
2) μ¬νμμ€ν
μ λν νκ°μ 보μμ μ΄λ €μ 112
μ 5 μ μ κ²° 113
μ 3 μ₯ 1990λ
λ λΆν λκΈ°κ·Όκ³Ό ν μ§ν©νν λ¬Έμ μ λν 쑰기경보μ νΈ 115
μ 1 μ λ¬Ένμ°κ΅¬ 117
1. μ΄λ‘ μ λ°°κ²½ 117
2. μκ³μ μ΄ μ‘°κΈ°κ²½λ³΄μ νΈ νμ§ κΈ°λ²κ³Ό μ¬λ‘ 125
1) μΈ‘μ μλ£ κΈ°λ° λ°©λ² 125
2) λͺ¨ν κΈ°λ° λ°©λ² 129
3) 곡κ°μ 쑰기경보μ νΈ μ§ν 132
μ 2 μ μ°κ΅¬λ°©λ² 137
1. μ°κ΅¬μλ£ 137
2. μκ³μ μ΄ μ‘°κΈ°κ²½λ³΄μ νΈ νμ§ λͺ¨ν μ€μ 140
1) μΈ‘μ μλ£ κΈ°λ° λ°©λ² 140
2) λͺ¨ν κΈ°λ° λ°©λ² 141
3) 곡κ°μ 쑰기경보μ νΈ λμΆλ°©λ² 142
μ 3 μ μΈ‘μ μλ£ κΈ°λ° λΆν λκΈ°κ·Όκ³Ό ν μ§ν©νν λ¬Έμ μ 쑰기경보μ νΈ μ¬λΆ νμΈ 144
1. μλ κ΄λ ¨ μλ£μ 쑰기경보μ νΈ 144
2. μμμ§μμ 쑰기경보μ νΈ 148
3. κΈ°νμλ£μ 쑰기경보μ νΈ 155
4. κΈ°ν μλ£μ 쑰기경보μ νΈ 163
5. λΆν λκΈ°κ·Όμ λν 쑰기경보μ νΈ μ¬λΆ 169
μ 4 μ λͺ¨ν곡κ°κΈ°λ° λΆν λκΈ°κ·Όκ³Ό ν μ§ν©νν λ¬Έμ μ 쑰기경보μ νΈ μμ λ° μ§μ νμΈ 171
1. λΉλͺ¨μμ μΆμΈ-νμ°-λμ½ λͺ¨ν(DDJ Model) 171
1) μλμμ°κ³Ό μκΈμ λ³ν 171
2) μμμ§μ 174
3) κΈ°νμλ£ 183
4) μ¬νκ²½μ μ§ν 188
2. μκ° κ°λ³μ± μκΈ°νκ· λͺ¨ν μ μ© κ²°κ³Ό 191
1) μλμμ°κ³Ό μκΈμ λ³ν 191
2) μμμ§μ 195
3) κΈ°νμλ£μ μ¬νκ²½μ μ§ν 199
3. κ³΅κ° κΈ°λ° μ‘°κΈ°κ²½λ³΄μ νΈ λΆμκ²°κ³Ό λ° ν΄μ 203
4. 쑰기경보μ νΈ μμ λ° μ·¨μ½μ§μ νμΈ 207
μ 5 μ μ κ²° 209
1. 쑰기경보μ νΈ ν¬μ°©κ²°κ³Όμ λν κΈ°μ΄μ λ
Όμ 209
2. μ£Όμ λ
Όμ μ§μ 211
μ 4 μ₯ λ€νμμμμ€ν
κΈ°λ° 1990λ
λ λΆν λκΈ°κ·Ό μ¬νλͺ¨ν κ°λ° 214
μ 1 μ λ¬Ένμ°κ΅¬ 216
1. λ€νμμμμ€ν
216
2. LUDAS Framework 219
3. λ€νμμμμ€ν
κ³Ό LUDAS Frameworkμ νλ°λ μ μ©μ¬λ‘ 220
1) λ€νμμμμ€ν
μ νκ΅ μ μ©μ¬λ‘ 220
2) LUDAS Frameworkμ νλ°λ μνκ³ μ μ©μ¬λ‘ 221
3) LUDAS Frameworkμ λΆν μ μ©μ¬λ‘ 224
4. λ€νμμμμ€ν
μ λΆν λκΈ°κ·Όκ³Ό ν μ§ν©νν λ¬Έμ μ μ©λ°©μ 226
μ 2 μ κ°μμ λΆν νλλμ₯ λͺ¨ν μ€κ³ 228
1. λΆν λμ
λ¬Έμ μμμ νλλμ₯μ λνμ± 228
2. κ°μμ λΆν νλλμ₯ λͺ¨ν μ μ 231
1) κ·λͺ¨μ ν¬κΈ°, νν 232
2) ν μ§νΌλ³΅κ³Ό μ§ν, κΈ°ν μμ± 232
3. κ°μμ λΆν νλλμ₯ λͺ¨ν κ΅¬μ± μμ 235
1) νκ²½ 235
2) μΈκ°νμμ 236
3) μΈλΆμμΈκ³Ό κ° μμλ€ κ°μ κ΄κ³ 237
μ 3 μ λ€νμμμμ€ν
λͺ¨νμ κ΅¬μΆ 238
1. λͺ¨νμ κ΅¬μ± 238
2. νκ²½ λΆμλͺ¨ν 241
1) νκ²½ λΆμλͺ¨νμ μμ±μ 보μ μΈ΅μ 241
2) 물리μ ν μνΉμ± μ°μΆλͺ¨ν 242
3) ννμ ν μνΉμ± μ°μΆλͺ¨ν 244
4) ν μ μ§ μ§νμ λμ
μμ°λ μ°μΆλͺ¨ν 244
3. μΈκ°νμμ λΆμλͺ¨ν 248
1) μΈκ°νμμμ μμ±μ 보 248
2) μλμ λΆλ°° 249
3) μμ¬κ²°μ κ³Ό ν μ§μ΄μ©μ λ³κ²½ 251
4. μΈλΆνκ²½κ³Ό κ²°κ³Ό λμΆ, λͺ¨νμ ꡬλ 255
1) μΈλΆνκ²½λ³μ 255
2) κ²°κ³Όκ° λμΆ 256
3) λͺ¨νμ ꡬλ 258
μ 4 μ λΆν νλλμ₯ λ€νμμμμ€ν
λͺ¨νμ 1990λ
λ λκΈ°κ·Ό μ¬νμ¬λΆ νμΈ 259
1. λͺ¨μ μ’
λ£ μμ 260
2. ν μ§μ΄μ©μ λ³ν 264
3. ν μ§ν©νν λ° μλμμ° μ νμ κ΄λ ¨λ λ΄λΆλ³μ 266
4. κ²°κ³Όμ λν 볡μ‘μ μκ³μ ν΄μ 270
μ 5 μ λΆν νλλμ₯ λ€νμμμμ€ν
λͺ¨νμ νκ²½λ³ν μλλ¦¬μ€ μ μ© κ²°κ³Ό 272
1. λͺ¨μ μ’
λ£ μμ 275
2. ν μ§μ΄μ©μ λ³ν 275
3. ν μ§ν©νν λ° μλμμ° μ νμ κ΄λ ¨λ λ΄λΆλ³μ 278
4. μλλ¦¬μ€ κ²°κ³Ό μ’
ν© 282
μ 6 μ μ κ²° 284
μ 5 μ₯ κ²° λ‘ 286
μ°Έκ³ λ¬Έν 293
λΆ λ‘ 312
λΆλ‘ 1. μ°κ΅¬μλ£ λͺ©λ‘κ³Ό μμ±μ 보 313
λΆλ‘ 2. AVHRR GIMMS μμμ§μλ₯Ό νμ©ν λΆν ν μ§ν©ννμ κ²½ν₯κ³Ό 곡κ°μ μ·¨μ½μ§ λμΆ 323
λΆλ‘ 3. λͺ¨νκΈ°λ° μ‘°κΈ°κ²½λ³΄μ νΈ νμ§ κΈ°λ²μ ν΅ν μ°λλ³ ν μ§ν©ννμ μλμμ° κ°μ 쑰기경보μ νΈ νμ§ 357
λΆλ‘ 4. λ€νμμμμ€ν
λͺ¨ν μΈν°νμ΄μ€μ μ½λ 362
Abstract 377λ°