6 research outputs found

    Remotely-Sensed Early Warning Signals of a Critical Transition in a Wetland Ecosystem

    Get PDF
    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.

    Get PDF
    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

    Get PDF
    Lake Urmia’s 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 lake’s 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 government’s 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

    Get PDF
    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…„λŒ€ λΆν•œ λŒ€κΈ°κ·Όμ„ μ€‘μ‹¬μœΌλ‘œ

    Get PDF
    The North Korean Famine, which is occurred in the mid-1990s, resulted in hundreds of thousands of people’s 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 Korea’s 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 1990’s 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 literature’s 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 years’ 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 famine’s 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 β€œopen system” 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…„λŒ€ μ€‘λ°˜μ˜ λΆν•œ λŒ€κΈ°κ·Όμ€ λ‹Ήμ‹œ μˆ˜μ‹­λ§Œμ˜ μ‚¬λžŒμ„ κΈ°μ•„μ˜ 고톡에 빠트렸으며, 20μ—¬ 년이 μ§€λ‚œ ν˜„μž¬κΉŒμ§€λ„ λΆν•œμ€ λŒ€κΈ°κ·ΌμœΌλ‘œ μΈν•œ κ²½μ œΒ·μ‚¬νšŒΒ·λ³΄κ±΄ μ „λ°˜μ— 걸친 ν›„μœ μ¦μ„ κ²ͺκ³  μžˆλ‹€. 이 λŒ€κΈ°κ·Όμ˜ μ›μΈμœΌλ‘œ μžμ—°μž¬ν•΄μ™€ 고립, 체제의 λΉ„νš¨μœ¨μ„± 등이 μ œκΈ°λœλ‹€. κ·ΈλŸ¬λ‚˜ 근본적인 λΆν•œ λŒ€κΈ°κ·Όμ˜ 원인은 κΈ°μ‘΄ 제기된 원인이 λ³΅ν•©μ μœΌλ‘œ μž‘μš©ν•˜μ—¬ ν† μ§€μ˜ 생산성을 λ–¨μ–΄νŠΈλ¦¬λŠ” 토지황폐화(land degradation) λ¬Έμ œμ™€ 이둜 μΈν•œ μ‹λŸ‰μƒμ‚° κ°μ†Œ λ¬Έμ œλ‹€. 토지황폐화와 μ‹λŸ‰μƒμ‚° κ°μ†Œ λ¬Έμ œλŠ” λΆν•œμ„ 포함해 μ „ 세계 인ꡬ 쀑 10μ–΅ λͺ… 이상이 κ²½ν—˜ν•˜λŠ” 문제둜, λΆν•œλ§Œμ˜ λ¬Έμ œκ°€ μ•„λ‹Œ 인λ₯˜μ˜ 지속가λŠ₯성을 μœ„ν˜‘ν•˜λŠ” μ£Όμš” 문제 쀑 ν•˜λ‚˜λΌ ν•  수 μžˆλ‹€. 토지황폐화 λ¬Έμ œλŠ” ν† μ§€μ˜ μƒνƒœ ν™˜κ²½μ  μš”μ†Œμ™€ ν† μ§€μ΄μš©μ˜ μ‚¬νšŒκ²½μ œμ  동인이 λ³΅ν•©μ μœΌλ‘œ μƒν˜Έ μž‘μš©ν•˜λŠ” μ‚¬νšŒμƒνƒœμ‹œμŠ€ν…œ(Social–Ecological System) λ¬Έμ œλ‹€. μ‚¬νšŒμƒνƒœμ‹œμŠ€ν…œ κ΅¬μ„±μš”μ†Œμ˜ λ‹€μ–‘ν•œ μƒν˜Έμž‘μš©μ€ 문제의 λ³΅μž‘μ„±μ„ 높이고 이해λ₯Ό μ–΄λ ΅κ²Œ ν•œλ‹€. λ˜ν•œ 토지황폐화와 μ‹λŸ‰μƒμ‚° κ°μ†Œ λ¬Έμ œλŠ” μ„œλ‘œκ°€ μ›μΈμ΄μž 결과둜 μž‘μš©ν•˜λŠ” λ˜λ¨Ήμž„ ꡬ쑰의 문제둜, λ¬Έμ œκ°€ λΉ„μ„ ν˜•μ μœΌλ‘œ 증폭되고 μ–΄λ– ν•œ λ°©ν–₯으둜 변화할지λ₯Ό μ˜ˆμΈ‘ν•˜κΈ° μ–΄λ ΅κ²Œ λ§Œλ“œλŠ” μ›μΈμœΌλ‘œ μž‘μš©ν•œλ‹€. λ”°λΌμ„œ λΆν•œμ˜ 토지황폐화와 μ‹λŸ‰μƒμ‚° κ°μ†Œ λ¬Έμ œλŠ” μ‚¬νšŒμƒνƒœμ‹œμŠ€ν…œμ—μ„œ κΈ°μΈν•˜λŠ” λ³΅μž‘μ μ‘κ³„(complex adaptive system) ν˜•νƒœλΌκ³  λ³Ό 수 있으며, 이 λ•Œλ¬Έμ— 토지황폐화 λ¬Έμ œλŠ” ν”„λ‘œμ„ΈμŠ€μ— λŒ€ν•œ 이해와 예츑이 μ–΄λ ΅λ‹€κ³  μ•Œλ €μ Έ μžˆλ‹€. 특히 λΆν•œμ˜ 토지황폐화 λ¬Έμ œλŠ” λΆν•œ μ •λ³΄μ˜ λΆ€μ‘±κ³Ό ν•™μ œ κ°„ λΆ„μ ˆλœ 연ꡬ ν’ν† λ‘œ 더 κ°€μ€‘λœ λ³΅μž‘μ„±μ˜ λ¬Έμ œμ— μ‹œλ‹¬λ¦¬κ³  μžˆλ‹€. 이 μ—°κ΅¬μ—μ„œλŠ” λΆν•œμ˜ 토지황폐화와 μ‹λŸ‰μƒμ‚° κ°μ†Œ 문제λ₯Ό μ‚¬νšŒμƒνƒœμ‹œμŠ€ν…œκ³Ό λ³΅μž‘μ μ‘κ³„μ  방법둠을 톡해 μ ‘κ·Όν•¨μœΌλ‘œμ¨ 토지황폐화 λŒ€κΈ°κ·Όμ— λŒ€ν•œ μ‚¬μ „μ˜ˆλ°© μ ‘κ·Ό(precautionary approach)을 톡해 μœ„ν—˜μ„ μ €κ°ν•˜λŠ” λ°©μ•ˆμ„ λͺ¨μƒ‰ν•˜κ³ μž ν•œλ‹€. 이λ₯Ό μœ„ν•΄ μ‚¬νšŒμƒνƒœμ‹œμŠ€ν…œμ„ 기반으둜 κΈ°μ‘΄ λΆν•œμ˜ 토지황폐화와 μ‹λŸ‰μƒμ‚° 연ꡬ와 자료λ₯Ό μž¬κ΅¬μ„±ν•˜μ˜€λ‹€(2μž₯). λ˜ν•œ λ³΅μž‘μ μ‘κ³„ 방법 쀑 μ‘°κΈ°κ²½λ³΄μ‹ ν˜Έ 탐지 기법(EWS)을 μ μš©ν•¨μœΌλ‘œμ¨ 예츑이 μ–΄λ €μš΄ λΆν•œ 토지황폐화와 μ‹λŸ‰μƒμ‚° κ°μ†Œ μœ„ν—˜μ˜ μˆ˜ν•™μ Β·ν†΅κ³„μ  징후λ₯Ό λͺ¨μƒ‰ν•˜μ˜€λ‹€(3μž₯). 그리고 λΆν•œμ˜ 토지황폐화와 μ‹λŸ‰μƒμ‚° κ°μ†Œ 문제λ₯Ό λŒ€ν‘œν•˜λŠ” λͺ¨ν˜•μ„ μ„€κ³„ν•˜κ³ , λ³΅μž‘μ μ‘κ³„ 기반 λͺ¨ν˜•ν™” 기법인 λ‹€ν–‰μœ„μžμ‹œμŠ€ν…œ(MAS-LUCC)을 μ΄μš©ν•΄ λͺ¨ν˜•μ„ κ΅¬ν˜„ν•¨μœΌλ‘œμ¨ 1990λ…„λŒ€ λΆν•œ λŒ€κΈ°κ·Όμ˜ μž¬ν˜„ κ°€λŠ₯성을 ν™•μΈν•˜μ—¬ μ‚¬μ „μ˜ˆλ°©μ  λŒ€μ•ˆμ— λŒ€ν•΄μ„œ ν‰κ°€ν•˜κ³ μž ν•˜μ˜€λ‹€(4μž₯). κ²°κ³Όλ₯Ό μš”μ•½ν•˜λ©΄ λ‹€μŒκ³Ό κ°™λ‹€. 첫째, μ‚¬νšŒμƒνƒœμ‹œμŠ€ν…œ 기반으둜 λΆν•œμ˜ 토지황폐화와 μ‹λŸ‰μƒμ‚° κ°μ†Œμ˜ κΈ°μ‘΄ 연ꡬ와 지식을 μž¬κ΅¬μ„±ν•¨μœΌλ‘œμ¨, κΈ°μ‘΄ 연ꡬ와 μžλ£Œκ°€ 가지고 μžˆλŠ” μ‚¬νšŒμƒνƒœμ  κ°€μΉ˜λ₯Ό ν™•μΈν•˜κ³  ν•œκ³„λ₯Ό 인식할 수 μžˆμ—ˆλ‹€. λΆν•œμ˜ 토지황폐화와 μ‹λŸ‰μƒμ‚° κ°μ†Œμ— λŒ€ν•œ 각각의 연ꡬ성과λ₯Ό ν™•μΈν•¨μœΌλ‘œμ¨ 편ν–₯κ³Ό 인식 차이λ₯Ό ν™•μΈν•˜μ˜€λ‹€. λ˜ν•œ, λΆν•œμ— κ΄€ν•œ 역사와 μ •μΉ˜λ¬Έν—Œ 속 λ§₯락을 λ°”νƒ•μœΌλ‘œ 이듀 μ‚¬μ΄μ˜ μš”μΈμ„ μΆ”μΆœν•˜κ³  μ‚¬νšŒμƒνƒœμ‹œμŠ€ν…œ 기반의 κ°œλ…λͺ¨ν˜•μœΌλ‘œ ꡬ좕할 수 μžˆμ—ˆλ‹€. κ·ΈλŸ¬λ‚˜ ꡬ체적인 관계λ₯Ό μˆ˜μ‹ λ˜λŠ” 자료기반으둜 λ³΄μ™„ν•˜λŠ” 것은 자료 자체의 λΆ€μ‘±κ³Ό 기법 λ“±μ˜ ν•œκ³„λ‘œ λ‹€μ†Œ λΆ€μ‘±ν•œ 것이 ν˜„μ‹€μ΄μ—ˆλ‹€. 특히 μΈκ°„μ˜ μ˜μ‚¬κ²°μ •κ³Ό ν† μ§€ν™©νν™”μ˜ 핡심적 ν”„λ‘œμ„ΈμŠ€λŠ” λΆ„μ„μ μœΌλ‘œ μ ‘κ·Όν•˜λŠ” 데 ν•œκ³„κ°€ μžˆμ—ˆμœΌλ©°, λŒ€μ•ˆμ  λͺ¨μƒ‰μ΄ ν•„μš”ν•¨μ„ 확인할 수 μžˆμ—ˆλ‹€. λ‘˜μ§Έ, 1990λ…„λŒ€ λΆν•œ λŒ€κΈ°κ·Όκ³Ό 토지황폐화 ν˜„μƒμ—λŠ” μ—¬λŸ¬ μ°¨μ›μ˜ 사전 징후λ₯Ό 확인할 수 μžˆμ—ˆλ‹€. 1960-1990λ…„λŒ€ λΆν•œμ˜ μ‹λŸ‰κ°μ†Œ λ¬Έμ œμ™€ 토지황폐화 λ¬Έμ œμ™€ μ—°κ΄€λœ λ‹€μ–‘ν•œ μ‹œκ³΅κ°„ μ§€ν‘œμ™€ μ‹œκ³„μ—΄ μ§€ν‘œλ₯Ό λŒ€μƒμœΌλ‘œ μ‘°κΈ°κ²½λ³΄μ‹ ν˜Έλ₯Ό ν¬μ°©ν•˜λŠ” 연ꡬλ₯Ό μ§„ν–‰ν•˜μ˜€λ‹€. κ·Έ κ²°κ³Ό 1980λ…„λŒ€ μ΄ˆμ—λŠ” 점진적인 λ³€ν™”λ₯Ό λ‚˜νƒ€λ‚΄λŠ” μ‘°κΈ°κ²½λ³΄μ‹ ν˜Έκ°€ 토지황폐화 κ΄€λ ¨ μ§€ν‘œλ₯Ό μ€‘μ‹¬μœΌλ‘œ λ‚˜νƒ€λ‚¬μœΌλ©°, 1980λ…„λŒ€ ν›„λ°˜μ—λŠ” 토지황폐화 κ΄€λ ¨ μ§€ν‘œλ₯Ό μ€‘μ‹¬μœΌλ‘œ 더 큰 μœ„ν—˜μ„ λ‚˜νƒ€λ‚΄λŠ” μ‘°κΈ°κ²½λ³΄μ‹ ν˜Έκ°€ λ‚˜νƒ€λ‚¬κ³ , 1990λ…„λŒ€ μ΄ˆμ—λŠ” μ‹λŸ‰μƒμ‚° κ΄€λ ¨ μ§€ν‘œμ—μ„œ μ‘°κΈ°κ²½λ³΄μ‹ ν˜Έκ°€ λ‚˜νƒ€λ‚¬λ‹€. 이λ₯Ό 톡해 λ‹¨μˆœνžˆ μ‘°κΈ°κ²½λ³΄μ‹ ν˜Έ λ„μΆœ 및 μ‹œμ μ„ νŒŒμ•…ν•  수 μžˆμ—ˆμ„ 뿐 μ•„λ‹ˆλΌ, λ˜λ¨Ήμž„ ꡬ쑰와 μ°½λ°œν˜„μƒ 같은 λ³΅μž‘μ μ‘κ³„ ν”„λ‘œμ„ΈμŠ€λ₯Ό μœ μΆ”ν•  수 μžˆμ—ˆμœΌλ©° μ΄λŠ” 4μž₯의 λͺ¨ν˜• κ²°κ³Ό 해석에도 ν™œμš©ν•  수 μžˆμ—ˆλ‹€. λ˜ν•œ 토지황폐화 μ§€ν‘œμ™€ μ‹λŸ‰μƒμ‚° μ§€ν‘œ κ°„μ˜ μ•½ 5λ…„κ°„μ˜ μ‹œμ°¨λŠ” κΈ°μ‘΄ μ—°κ΅¬μ—μ„œ 확인할 수 μžˆλ“― 1990λ…„λŒ€ λŒ€κΈ°κ·Όμ˜ 사전 징후가 적어도 1980λ…„λŒ€ ν›„λ°˜μ—λŠ” ν‘œλ©΄ν™”λ˜μ—ˆλ‹€λŠ” 것을 μ•”μ‹œν•˜λŠ” κ²°κ³Όλ‹€. κ·ΈλŸ¬λ‚˜ λŒ€μ‘ν•˜κΈ°μ—λŠ” μΆ©λΆ„ν•œ μ‹œκ°„μ΄ λΆ€μ‘±ν•˜μ˜€κ±°λ‚˜ 잘λͺ»λœ λŒ€μ‘μ΄μ—ˆμ„ κ²ƒμ΄λΌλŠ” 것 λ˜ν•œ λ¬Έν—Œ 연ꡬλ₯Ό 톡해 확인할 수 μžˆμ—ˆλ‹€. μ΄λŠ” 상당 수의 κΈ°μ‘΄ 연ꡬ와 μžλ£Œμ—μ„œ ꡐ차검증할 수 μžˆμ—ˆλ‹€. μ…‹μ§Έ, λΆν•œλ†μ—…μ˜ μ‚¬νšŒμƒνƒœμ‹œμŠ€ν…œμ„ λŒ€ν‘œν•  수 μžˆλŠ” κ°€μƒμ˜ ν˜‘λ™λ†μž₯ κ°œλ…λͺ¨ν˜•μ„ κ΅¬μΆ•ν•˜κ³ , 이λ₯Ό 1990λ…„λŒ€ λΆν•œ λŒ€κΈ°κ·Όμ„ μž¬ν˜„ν•  수 μžˆλŠ” λ³΅μž‘μ μ‘κ³„ 기반 λ‹€ν–‰μœ„μžμ‹œμŠ€ν…œμœΌλ‘œ κ΅¬ν˜„ν•  수 μžˆμ—ˆλ‹€. λͺ¨μ˜ κ²°κ³Ό ν˜„μ‹€μ—μ„œ λ‚˜νƒ€λ‚¬λ˜ κΈ‰κ²©ν•œ 1990λ…„λŒ€ λΆν•œ λŒ€κΈ°κ·Όμ˜ ν˜•νƒœκ°€ μœ μ‚¬ν•˜κ²Œ μž¬ν˜„λ˜μ—ˆλ‹€. λ˜ν•œ 이 κ²°κ³Όμ—μ„œλŠ” λͺ¨μ˜ ν›„ 20λ…„ ν›„(1980λ…„ μ „ν›„)와 30λ…„ ν›„(1990λ…„ μ „ν›„)에 변곑점이 λ‚˜νƒ€λ‚˜λŠ”λ°, μ΄λŠ” λ˜λ¨Ήμž„μ˜ ꡬ쑰와 같은 토지황폐화와 μ‹λŸ‰μƒμ‚° κ°μ†Œμ˜ λ³΅μž‘μ μ‘κ³„μ  ν”„λ‘œμ„ΈμŠ€ λ³€ν™”λ₯Ό λ‚˜νƒ€λ‚Έλ‹€. 그리고 μ™ΈλΆ€ μ‹λŸ‰μ§€μ› μ‹œλ‚˜λ¦¬μ˜€λ₯Ό 톡해 μ™ΈλΆ€ μžμ› 제곡이 상당 λΆ€λΆ„ λΆν•œμ˜ 토지황폐화와 μ‹λŸ‰μƒμ‚° μ €ν•˜ μœ„ν—˜μ„ μ§€μ—°μ‹œν‚¬ 수 μžˆλ‹€λŠ” 결둠을 내릴 수 μžˆμ—ˆλ‹€. κ·ΈλŸ¬λ‚˜ λͺ¨ν˜•μ—μ„œλŠ” λŒ€κΈ°κ·Όμ˜ λ°œμƒ μžμ²΄λŠ” 막을 수 μ—†μ—ˆμœΌλ©°, 인ꡬ와 μƒνƒœμ••λ ₯을 μ€„μ΄λŠ” λ“± λ‹€λ₯Έ ꡬ체적인 λŒ€μ•ˆμ΄ ν•„μš”ν•¨μ„ 확인할 수 μžˆμ—ˆλ‹€. 이 μ—°κ΅¬μ˜ κ²°κ³ΌλŠ” λΆν•œκ³Ό ν•œλ°˜λ„μ˜ 지속가λŠ₯μ„± μ •μ±… μˆ˜λ¦½μ— ν•¨μ˜λ₯Ό 가진닀, 토지황폐화와 μ‹λŸ‰μƒμ‚° λΆ€μ‘± 문제λ₯Ό ν¬ν•¨ν•œ λΆν•œμ˜ μ‚¬νšŒμƒνƒœμ‹œμŠ€ν…œμ— λŒ€ν•œ μ •μ±… μˆ˜λ¦½μ„ μœ„ν•΄μ„œλŠ” μ˜μ™Έμ„±κ³Ό μ‹œμ°¨λ₯Ό κ³ λ €ν•œ μž₯κΈ° κ΄€μ°°κ³Ό, μ‚¬νšŒκ²½μ œμ™€ μžμ—°μƒνƒœμ  μš”μ†Œλ₯Ό ν¬κ΄„ν•˜λŠ” 쒅합적인 접근이 ν•„μš”ν•˜λ‹€. λ˜ν•œ λ³΅μž‘μ μ‘κ³„ 방법둠을 μ μš©ν•¨μœΌλ‘œμ¨ μ •μ±…μ˜μ‚¬κ²°μ •μ΄ μ–΄λ– ν•œ νŒŒκΈ‰νš¨κ³Όλ₯Ό λΆˆλŸ¬μΌμœΌν‚¬μ§€λ₯Ό ν™•μΈν•˜κ³  μ˜μ‚¬κ²°μ •μ„ μˆ˜ν–‰ν•  ν•„μš”κ°€ μžˆλ‹€. ꡬ체적인 κΈ°κ·Ό 방지 λŒ€μ±…μœΌλ‘œ λ‹¨κΈ°μ μœΌλ‘œλŠ” λΆν•œμ„ νμ‡„κ³„μ—μ„œ νƒˆν”Όν•˜λ„λ‘ ν•  정책적인 λŒ€μ•ˆμ„ λ§ˆλ ¨ν•  ν•„μš”κ°€ 있고, μž₯κΈ°μ μœΌλ‘œλŠ” λΆν•œμ„ 포함해 μ „ μ„Έκ³„μ μœΌλ‘œ 인ꡬ와 μƒνƒœμ••λ ₯을 μ€„μ΄λŠ” λ°©μ•ˆμ„ λͺ¨μƒ‰ν•  ν•„μš”κ°€ μžˆλ‹€. 이 μ—°κ΅¬λŠ” λΆν•œ, ν•œλ°˜λ„, 그리고 μ„Έκ³„μ μœΌλ‘œ μž¬ν˜„ κ°€λŠ₯성이 큰 κΈ°κ·Όκ³Ό 토지황폐화 문제의 μœ„ν—˜ 저감을 μœ„ν•œ κ΅ν›ˆκ³Ό λŒ€μ•ˆμ„ μ œμ‹œν–ˆλ‹€λŠ” μ μ—μ„œ κ·Έ μ˜μ˜κ°€ μžˆλ‹€. 특히 토지황폐화 문제뿐 μ•„λ‹ˆλΌ λ‹€λ°©λ©΄μ˜ ν™˜κ²½λ¬Έμ œμ™€ μ‚¬νšŒμƒνƒœμ‹œμŠ€ν…œμ— μ μš©ν•  수 μžˆλŠ” μ‘°κΈ°κ²½λ³΄μ‹ ν˜Έ 포착 연ꡬ와 같은 μ΅œμ‹  연ꡬ기법을 μ μš©ν•˜κ³ , ν•™μ œ κ°„ 인식차와 지식 차이λ₯Ό κ·Ήλ³΅ν•˜λŠ” λŒ€μ•ˆμ„ μ œμ‹œν•˜μ˜€λ‹€λŠ” μ μ—μ„œ ν•™μˆ μ μΈ μ˜λ―Έκ°€ μžˆλ‹€. λ˜ν•œ 직접적인 λΆν•œ μ •μ±… 수립뿐 μ•„λ‹ˆλΌ μ •μ±…μ˜ μ „λ°˜μ μΈ κΈ°μ‘° μ„€μ •κ³Ό ν‰κ°€μ—κΉŒμ§€ 의의λ₯Ό μ œκ³΅ν•˜μ˜€λ‹€λŠ” μ μ—μ„œ μ˜λ―Έκ°€ 크닀고 ν•  수 μžˆλ‹€.제 1 μž₯ μ„œ λ‘  1 제 1 절 연ꡬ배경과 λͺ©μ  1 1. μ—°κ΅¬μ˜ ν•„μš”μ„± 1 2. κ΄€λ ¨ 연ꡬ동ν–₯κ³Ό 과제 5 1) μ „ 세계적 μ°¨μ›μ˜ 토지황폐화 연ꡬ동ν–₯ 5 2) 1990λ…„λŒ€ λΆν•œ λŒ€κΈ°κ·Όκ³Ό λΆν•œ 토지황폐화 연ꡬ동ν–₯κ³Ό 과제 9 3) μ‚¬νšŒμƒνƒœμ  접근법과 λ³΅μž‘μ μ‘κ³„μ  μΈμ‹μ²΄κ³„μ˜ ν•„μš”μ„± 11 3. 접근방법과 μ—°κ΅¬μ§ˆλ¬Έ 15 4. 연ꡬλͺ©μ  17 제 2 절 μ—°κ΅¬λŒ€μƒκ³Ό λ²”μœ„ 18 1. μ—°κ΅¬μ˜ μ‹œκ³΅κ°„μ  λ²”μœ„ 18 2. μ£Όμš” κ°œλ…μ˜ μ •μ˜μ™€ λ²”μœ„ μ„€μ • 21 제 3 절 μ—°κ΅¬μ˜ ꡬ성 24 제 2 μž₯ λΆν•œ 토지황폐화와 μ‹λŸ‰μƒμ‚° κ°μ†Œμ— λŒ€ν•œ μ‚¬νšŒμƒνƒœμ  μ ‘κ·Ό 26 제 1 절 λΆν•œ 토지황폐화 ν˜„ν™©κ³Ό 연ꡬ경ν–₯ 27 1. 톡계 및 μ •λΆ€λ°œκ°„ 자료 μ€‘μ‹¬μ˜ 토지황폐화 연ꡬ 29 2. 토지황폐화에 λŒ€ν•œ μœ„μ„±μ˜μƒ 기반 연ꡬ 33 3. 토지황폐화에 λŒ€ν•œ 연ꡬ μ’…ν•© 37 제 2 절 λΆν•œμ˜ μ‹λŸ‰μƒμ‚° κ°μ†Œκ²½ν–₯κ³Ό κ΄€λ ¨ 연ꡬ동ν–₯ 39 1. ν†΅κ³„λ¬Έν—ŒκΈ°λ°˜ λΆν•œμ˜ μ‹λŸ‰μƒμ‚° κ²½ν–₯ 42 2. μ‹λŸ‰μƒμ‚°μ˜ λ³€ν™” 원인과 νŒŒκΈ‰νš¨κ³Όμ— κ΄€ν•œ 톡계와 λ¬Έν—Œ 49 1) λ†μ—…μƒμ‚°λŸ‰μ˜ νŒŒκΈ‰νš¨κ³Ό(κΈ°κ·Ό)와 κ΄€λ ¨λœ 톡계와 λ¬Έν—Œ 49 2) λ†μ—…μƒμ‚°λŸ‰ λ³€ν™”μ˜ μ‚¬νšŒκ²½μ œμ  μš”μΈκ³Ό κ΄€λ ¨λœ 톡계와 λ¬Έν—Œ 52 3) μ‹λŸ‰μƒμ‚° λ³€ν™”μ˜ μžμ—°ν™˜κ²½ μš”μΈ 쀑 기후와 κ΄€λ ¨λœ 톡계와 λ¬Έν—Œ 54 3. λΆν•œμ˜ μ‹λŸ‰μƒμ‚° μžλ£Œμ™€ μš”μΈλ“€ κ°„μ˜ μ—°κ΄€μ„± 연ꡬ동ν–₯ 57 1) μΈλ¬Έμ‚¬νšŒμžλ£Œμ™€ μ‹λŸ‰μƒμ‚°λŸ‰κ³Όμ˜ 관계 57 2) μ‹μƒμ§€μˆ˜μ™€ μ‹λŸ‰μƒμ‚°λŸ‰κ³Όμ˜ 관계성 59 3) μ‹λŸ‰μƒμ‚°λŸ‰-μ‹μƒμ§€μˆ˜-ν™˜κ²½μ •λ³΄μ™€μ˜ 관계 63 4. λΆν•œμ˜ μ‹λŸ‰μƒμ‚° 변화에 κ΄€ν•œ μš”μ•½κ³Ό ν•œκ³„ 65 제 3 절 λΆν•œ 토지황폐화와 μ‹λŸ‰μƒμ‚° κ°μ†Œμ— λŒ€ν•œ 전역적 μ ‘κ·Ό 66 1. λΆν•œ 토지황폐화와 μ‹λŸ‰μƒμ‚° κ°μ†Œμ˜ 역사적 λ§₯락 67 2. 1990λ…„λŒ€ λΆν•œ λŒ€κΈ°κ·Όκ³Ό 토지황폐화 μš”μΈ λ„μΆœ 76 1) μ‚¬νšŒκ²½μ œμ μ œλ„μ  μš”μΈ 77 2) μžμ—°ν™˜κ²½μ  μš”μΈ 79 3. μ‚¬νšŒμƒνƒœμ‹œμŠ€ν…œ λ§₯λ½μ—μ„œμ˜ κ°œλ…λͺ¨ν˜• λ„μΆœ 81 제 4 절 λΆν•œ 토지황폐화와 μ‹λŸ‰μƒμ‚° κ°μ†Œλ¬Έμ œ μš”μΈλ“€ κ°„μ˜ 관계 84 1. κ΄€λ ¨ μžλ£Œμ™€ 뢄석방법 84 2. μ‹œκ°„μžλ£Œ κ°„μ˜ 관계성을 ν†΅ν•œ μ‹λŸ‰μƒμ‚°λŸ‰ μΆ”μ •λͺ¨ν˜• 개발 90 3. μ‹œκ³΅κ°„μžλ£Œ κ°„μ˜ 관계성 νŒŒμ•…κ³Ό μ‹μƒμ§€μˆ˜ 예츑λͺ¨ν˜• 탐색 96 4. λ―Έμ‹œμ  관계성 νŒŒμ•…μ„ ν†΅ν•œ κ°œλ…λͺ¨ν˜• 평가 110 1) μ‹λŸ‰μƒμ‚°λŸ‰μ‹μƒμ§€μˆ˜ 예츑λͺ¨ν˜•κ³Ό κ°œλ…λͺ¨ν˜•κ³Όμ˜ 비ꡐ와 ν•œκ³„ 110 2) μ‚¬νšŒμ‹œμŠ€ν…œμ— λŒ€ν•œ 평가와 λ³΄μ™„μ˜ 어렀움 112 제 5 절 μ†Œ κ²° 113 제 3 μž₯ 1990λ…„λŒ€ λΆν•œ λŒ€κΈ°κ·Όκ³Ό 토지황폐화 λ¬Έμ œμ— λŒ€ν•œ μ‘°κΈ°κ²½λ³΄μ‹ ν˜Έ 115 제 1 절 λ¬Έν—Œμ—°κ΅¬ 117 1. 이둠적 λ°°κ²½ 117 2. μž„κ³„μ „μ΄ μ‘°κΈ°κ²½λ³΄μ‹ ν˜Έ 탐지 기법과 사둀 125 1) μΈ‘μ •μžλ£Œ 기반 방법 125 2) λͺ¨ν˜• 기반 방법 129 3) 곡간적 μ‘°κΈ°κ²½λ³΄μ‹ ν˜Έ μ§€ν‘œ 132 제 2 절 연ꡬ방법 137 1. μ—°κ΅¬μžλ£Œ 137 2. μž„κ³„μ „μ΄ μ‘°κΈ°κ²½λ³΄μ‹ ν˜Έ 탐지 λͺ¨ν˜• μ„€μ • 140 1) μΈ‘μ •μžλ£Œ 기반 방법 140 2) λͺ¨ν˜• 기반 방법 141 3) 곡간적 μ‘°κΈ°κ²½λ³΄μ‹ ν˜Έ λ„μΆœλ°©λ²• 142 제 3 절 μΈ‘μ •μžλ£Œ 기반 λΆν•œ λŒ€κΈ°κ·Όκ³Ό 토지황폐화 문제의 μ‘°κΈ°κ²½λ³΄μ‹ ν˜Έ μ—¬λΆ€ 확인 144 1. μ‹λŸ‰ κ΄€λ ¨ 자료의 μ‘°κΈ°κ²½λ³΄μ‹ ν˜Έ 144 2. μ‹μƒμ§€μˆ˜μ˜ μ‘°κΈ°κ²½λ³΄μ‹ ν˜Έ 148 3. κΈ°ν›„μžλ£Œμ˜ μ‘°κΈ°κ²½λ³΄μ‹ ν˜Έ 155 4. 기타 자료의 μ‘°κΈ°κ²½λ³΄μ‹ ν˜Έ 163 5. λΆν•œ λŒ€κΈ°κ·Όμ— λŒ€ν•œ μ‘°κΈ°κ²½λ³΄μ‹ ν˜Έ μ—¬λΆ€ 169 제 4 절 λͺ¨ν˜•κ³΅κ°„κΈ°λ°˜ λΆν•œ λŒ€κΈ°κ·Όκ³Ό 토지황폐화 문제의 μ‘°κΈ°κ²½λ³΄μ‹ ν˜Έ μ‹œμ  및 지점 확인 171 1. λΉ„λͺ¨μˆ˜μ  μΆ”μ„Έ-ν™•μ‚°-도약 λͺ¨ν˜•(DDJ Model) 171 1) μ‹λŸ‰μƒμ‚°κ³Ό μˆ˜κΈ‰μ˜ λ³€ν™” 171 2) μ‹μƒμ§€μˆ˜ 174 3) κΈ°ν›„μžλ£Œ 183 4) μ‚¬νšŒκ²½μ œμ§€ν‘œ 188 2. μ‹œκ°„ κ°€λ³€μ„± μžκΈ°νšŒκ·€ λͺ¨ν˜• 적용 κ²°κ³Ό 191 1) μ‹λŸ‰μƒμ‚°κ³Ό μˆ˜κΈ‰μ˜ λ³€ν™” 191 2) μ‹μƒμ§€μˆ˜ 195 3) κΈ°ν›„μžλ£Œμ™€ μ‚¬νšŒκ²½μ œμ§€ν‘œ 199 3. 곡간 기반 μ‘°κΈ°κ²½λ³΄μ‹ ν˜Έ 뢄석결과 및 해석 203 4. μ‘°κΈ°κ²½λ³΄μ‹ ν˜Έ μ‹œμ  및 취약지점 확인 207 제 5 절 μ†Œ κ²° 209 1. μ‘°κΈ°κ²½λ³΄μ‹ ν˜Έ 포착결과에 λŒ€ν•œ 기초적 λ…Όμ˜ 209 2. μ£Όμš” λ…Όμ˜ 지점 211 제 4 μž₯ λ‹€ν–‰μœ„μžμ‹œμŠ€ν…œ 기반 1990λ…„λŒ€ λΆν•œ λŒ€κΈ°κ·Ό μž¬ν˜„λͺ¨ν˜• 개발 214 제 1 절 λ¬Έν—Œμ—°κ΅¬ 216 1. λ‹€ν–‰μœ„μžμ‹œμŠ€ν…œ 216 2. LUDAS Framework 219 3. λ‹€ν–‰μœ„μžμ‹œμŠ€ν…œκ³Ό LUDAS Framework의 ν•œλ°˜λ„ μ μš©μ‚¬λ‘€ 220 1) λ‹€ν–‰μœ„μžμ‹œμŠ€ν…œμ˜ ν•œκ΅­ μ μš©μ‚¬λ‘€ 220 2) LUDAS Framework의 ν•œλ°˜λ„ μƒνƒœκ³„ μ μš©μ‚¬λ‘€ 221 3) LUDAS Framework의 λΆν•œ μ μš©μ‚¬λ‘€ 224 4. λ‹€ν–‰μœ„μžμ‹œμŠ€ν…œμ˜ λΆν•œ λŒ€κΈ°κ·Όκ³Ό 토지황폐화 문제 μ μš©λ°©μ•ˆ 226 제 2 절 κ°€μƒμ˜ λΆν•œ ν˜‘λ™λ†μž₯ λͺ¨ν˜• 섀계 228 1. λΆν•œ 농업 λ¬Έμ œμ—μ„œμ˜ ν˜‘λ™λ†μž₯의 λŒ€ν‘œμ„± 228 2. κ°€μƒμ˜ λΆν•œ ν˜‘λ™λ†μž₯ λͺ¨ν˜• μ •μ˜ 231 1) 규λͺ¨μ™€ 크기, ν˜•νƒœ 232 2) 토지피볡과 μ§€ν˜•, 기타 속성 232 3. κ°€μƒμ˜ λΆν•œ ν˜‘λ™λ†μž₯ λͺ¨ν˜• ꡬ성 μš”μ†Œ 235 1) ν™˜κ²½ 235 2) μΈκ°„ν–‰μœ„μž 236 3) μ™ΈλΆ€μš”μΈκ³Ό 각 μš”μ†Œλ“€ κ°„μ˜ 관계 237 제 3 절 λ‹€ν–‰μœ„μžμ‹œμŠ€ν…œ λͺ¨ν˜•μ˜ ꡬ좕 238 1. λͺ¨ν˜•μ˜ ꡬ성 238 2. ν™˜κ²½ 뢀속λͺ¨ν˜• 241 1) ν™˜κ²½ 뢀속λͺ¨ν˜•μ˜ 속성정보와 μΈ΅μœ„ 241 2) 물리적 ν† μ–‘νŠΉμ„± μ‚°μΆœλͺ¨ν˜• 242 3) 화학적 ν† μ–‘νŠΉμ„± μ‚°μΆœλͺ¨ν˜• 244 4) ν† μ–‘ 질 μ§€ν‘œμ™€ λ†μ—…μƒμ‚°λŸ‰ μ‚°μΆœλͺ¨ν˜• 244 3. μΈκ°„ν–‰μœ„μž 뢀속λͺ¨ν˜• 248 1) μΈκ°„ν–‰μœ„μžμ˜ 속성정보 248 2) μ‹λŸ‰μ˜ λΆ„λ°° 249 3) μ˜μ‚¬κ²°μ •κ³Ό ν† μ§€μ΄μš©μ˜ λ³€κ²½ 251 4. μ™ΈλΆ€ν™˜κ²½κ³Ό κ²°κ³Ό λ„μΆœ, λͺ¨ν˜•μ˜ ꡬ동 255 1) μ™ΈλΆ€ν™˜κ²½λ³€μˆ˜ 255 2) κ²°κ³Όκ°’ λ„μΆœ 256 3) λͺ¨ν˜•μ˜ ꡬ동 258 제 4 절 λΆν•œ ν˜‘λ™λ†μž₯ λ‹€ν–‰μœ„μžμ‹œμŠ€ν…œ λͺ¨ν˜•μ˜ 1990λ…„λŒ€ λŒ€κΈ°κ·Ό μž¬ν˜„μ—¬λΆ€ 확인 259 1. λͺ¨μ˜ μ’…λ£Œ μ‹œμ  260 2. ν† μ§€μ΄μš©μ˜ λ³€ν™” 264 3. 토지황폐화 및 μ‹λŸ‰μƒμ‚° μ €ν•˜μ™€ κ΄€λ ¨λœ λ‚΄λΆ€λ³€μˆ˜ 266 4. 결과에 λŒ€ν•œ λ³΅μž‘μ μ‘κ³„μ  해석 270 제 5 절 λΆν•œ ν˜‘λ™λ†μž₯ λ‹€ν–‰μœ„μžμ‹œμŠ€ν…œ λͺ¨ν˜•μ˜ ν™˜κ²½λ³€ν™” μ‹œλ‚˜λ¦¬μ˜€ 적용 κ²°κ³Ό 272 1. λͺ¨μ˜ μ’…λ£Œ μ‹œμ  275 2. ν† μ§€μ΄μš©μ˜ λ³€ν™” 275 3. 토지황폐화 및 μ‹λŸ‰μƒμ‚° μ €ν•˜μ™€ κ΄€λ ¨λœ λ‚΄λΆ€λ³€μˆ˜ 278 4. μ‹œλ‚˜λ¦¬μ˜€ κ²°κ³Ό μ’…ν•© 282 제 6 절 μ†Œ κ²° 284 제 5 μž₯ κ²° λ‘  286 μ°Έκ³ λ¬Έν—Œ 293 λΆ€ 둝 312 뢀둝 1. μ—°κ΅¬μžλ£Œ λͺ©λ‘κ³Ό 속성정보 313 뢀둝 2. AVHRR GIMMS μ‹μƒμ§€μˆ˜λ₯Ό ν™œμš©ν•œ λΆν•œ ν† μ§€ν™©νν™”μ˜ κ²½ν–₯κ³Ό 곡간적 취약지 λ„μΆœ 323 뢀둝 3. λͺ¨ν˜•κΈ°λ°˜ μ‘°κΈ°κ²½λ³΄μ‹ ν˜Έ 탐지 기법을 ν†΅ν•œ 연도별 토지황폐화와 μ‹λŸ‰μƒμ‚° κ°μ†Œ μ‘°κΈ°κ²½λ³΄μ‹ ν˜Έ 탐지 357 뢀둝 4. λ‹€ν–‰μœ„μžμ‹œμŠ€ν…œ λͺ¨ν˜• μΈν„°νŽ˜μ΄μŠ€μ™€ μ½”λ“œ 362 Abstract 377λ°•
    corecore