本文選題：自然災(zāi)害 + 火災(zāi)�。� 參考：《中國科學(xué)院大學(xué)(中國科學(xué)院遙感與數(shù)字地球研究所)》2017年博士論文

【摘要】：火災(zāi)是全球性常發(fā)自然災(zāi)害之一,每年發(fā)生的火災(zāi)都會破壞大量地表植被系統(tǒng),極大地影響了農(nóng)林生產(chǎn)和經(jīng)濟發(fā)展。中國是一個火災(zāi)多發(fā)國家,是全球受火災(zāi)影響最為嚴重的前20個國家之一。特別是,在全球變化背景下,中國需要考慮科學(xué)有效的降低火災(zāi)風(fēng)險策略,應(yīng)對不斷增多的火災(zāi)風(fēng)險。遙感數(shù)據(jù)作為研究大氣與地表過程的一種重要信息源,具有許多無可比擬的特點和優(yōu)勢。目前,不同空間分辨率、光譜分辨率、時間分辨率的遙感數(shù)據(jù)提供了豐富的火情信息,利用星載或機載傳感器,既可以對區(qū)域性的突發(fā)火情快速高精度地觀測,也可以實現(xiàn)全球性的過火區(qū)域監(jiān)測。許多學(xué)者開始利用不同遙感數(shù)據(jù)及其產(chǎn)品,如Suomi-NPP VIIRS、MODIS、ATSR、SPOTVEGETATION、ASTER、AVHRR、風(fēng)云等對過火事件進行研究,并在火點識別、過火面積提取等方面取得了長足進展。本文重點關(guān)注火災(zāi)風(fēng)險的遙感評估問題,主要從兩個方面展開:1)討論和研究影響中國南部火災(zāi)風(fēng)險的關(guān)鍵氣候和環(huán)境參量,2)選取關(guān)鍵氣候和環(huán)境參量,實現(xiàn)火災(zāi)風(fēng)險的遙感建模評估。在第一方面研究中,本文重點研究了降水量(PPT)、蒸散發(fā)(ET)、潛在蒸散發(fā)(pET)等多氣候和環(huán)境參量對火災(zāi)風(fēng)險的貢獻機制,主要包括選取了與火災(zāi)誘發(fā)相關(guān)的系列氣候和環(huán)境參量,開展火災(zāi)密度與各參量之間的網(wǎng)格相關(guān)分析。其中,基于網(wǎng)格的相關(guān)分析從三個時間尺度進行展開,即年度、冬季(12月-次年2月)和春季(3月-5月),后兩個時間尺度的選擇主要考慮到春季和冬季是中國南部地區(qū)易發(fā)生火災(zāi)的季節(jié)。本文研究表明,降水量、蒸散發(fā)等氣候和環(huán)境參量同火災(zāi)風(fēng)險在季節(jié)性尺度上存在相關(guān)性,但這種相關(guān)性還同時與時間、空間條件密切相關(guān),即中國東南部地區(qū)氣候和環(huán)境參量同火災(zāi)風(fēng)險在冬季的相關(guān)性較大,而西南部地區(qū)則在春季呈現(xiàn)強相關(guān)。同時發(fā)現(xiàn),與中國南部地區(qū)火災(zāi)活動最顯著相關(guān)的參量是潛在蒸散發(fā)和蒸散發(fā)與潛在蒸散發(fā)的比值(ET/pET),其他參量如水分平衡和降水量等也與火災(zāi)活動相關(guān),但這些參量主要影響了華東南部冬季的火災(zāi)風(fēng)險。綜合來看,氣候和環(huán)境參量與火災(zāi)活動緊密相關(guān),可以作為中國南部地區(qū)火災(zāi)風(fēng)險評估的指示性指標。在第二方面的研究中,本文提出應(yīng)用半監(jiān)督機器學(xué)習(xí)方法來進行火災(zāi)風(fēng)險的建模評估。該機器學(xué)習(xí)方法是一種在僅有正例和未標注樣本的訓(xùn)練數(shù)據(jù)集下進行機器學(xué)習(xí)的特殊半監(jiān)督學(xué)習(xí)方法。在本部分的研究中,考慮到網(wǎng)格的空間分辨率得到提高,為了方便處理大量數(shù)據(jù),本文將研究區(qū)域縮小至中國東南部地區(qū),并主要考慮常綠闊葉林、混合林和多樹草地三種不同的地類。對于上述三類不同的地表植被覆蓋類型,本文對中國東南部地區(qū)的火災(zāi)風(fēng)險進行了評估。研究發(fā)現(xiàn),與常綠闊葉林和多樹草地相比,基于半監(jiān)督機器學(xué)習(xí)方法的火災(zāi)風(fēng)險評估模型在混合林區(qū)域的準確性更好。綜上,本文所提出的半監(jiān)督機器學(xué)習(xí)方法為理解火災(zāi)風(fēng)險問題提供了新的方法貢獻。當(dāng)前,本文主要采取了針對不同地類分別構(gòu)建單一性評估模型的方法,在未來的改進方面,還需考慮研究一種可用于整個地區(qū)、適用于多類地物的火災(zāi)風(fēng)險評價模型。同時,本文提出的方法在精度上仍有提升空間,如可考慮選取一些與火災(zāi)活動具有較高相關(guān)性并表現(xiàn)出高空間變異性的氣候和環(huán)境特征來構(gòu)建模型�？傮w而言,本文提出的半監(jiān)督機器學(xué)習(xí)方法是利用遙感數(shù)據(jù)進行火災(zāi)風(fēng)險評估的有效手段。
[Abstract]:Fire is one of the most common natural disasters in the world. Every year's fires will destroy a large number of surface vegetation systems, which greatly affect the production and economic development of agriculture and forestry. China is a fire prone country and one of the most serious fire affected countries in the world. In particular, China needs to consider the section under the background of global change. It is effective to reduce fire risk strategies and respond to increasing fire risk. Remote sensing data, as an important source of information for the study of atmospheric and surface processes, has many unparalleled features and advantages. At present, remote sensing data with different spatial resolution, spectral resolution and time resolution provide rich information and use of stars. A load or airborne sensor can be used for rapid and high precision observation of a regional burst of fire and a global monitoring area. Many scholars have begun to use different remote sensing data and their products, such as Suomi-NPP VIIRS, MODIS, ATSR, SPOTVEGETATION, ASTER, AVHRR, and wind clouds to study the fire events and identify the fire points, This paper focuses on the remote sensing assessment of fire risk, mainly from two aspects: 1) discuss and study the key climate and environment parameters affecting the fire risk in southern China, 2) select the key climate and environmental parameters, and realize the evaluation of remote sensing modeling for fire risk. In this study, this paper focuses on the contribution mechanism of precipitation (PPT), evapotranspiration (ET), potential evapotranspiration (pET) and other climatic and environmental parameters to the fire risk, including the selection of a series of climate and environmental parameters related to the fire induced, and the grid correlation analysis between the fire density and the parameters. The analysis is carried out from three time scales, namely, annual, winter (December - February) and spring (March -5 months). The first two time scales are selected mainly considering that spring and winter are the prone fire seasons in southern China. This paper shows that the climate and environmental parameters such as precipitation, evapotranspiration and other climate are on the seasonal scale. There is a correlation, but the correlation is closely related to time and space conditions, that is, the climate and environment parameters in the southeast of China are related to the fire risk in winter, while the southwest region is strongly correlated in the spring, and the most significant parameter related to the fire activity in the southern part of China is the potential evapotranspiration. The ratio of hair and Evapotranspiration to potential evapotranspiration (ET/pET), other parameters such as water balance and precipitation are also related to fire activities, but these parameters mainly affect the risk of fire in winter in the south of East China. In the second aspect of the study, this paper proposes a semi supervised machine learning method for the modeling and evaluation of fire risk. The machine learning method is a special semi supervised learning method for machine learning under a training data set with only positive and unlabeled samples. In order to improve the spatial resolution, in order to facilitate the processing of a large number of data, this paper narrowed the research area to the southeast of China, and mainly considered the evergreen broad-leaved forest, mixed forest and multi tree grassland, three different types of land. For the above three types of surface vegetation cover types, this article reviews the fire risk in the southeast of China. The study found that the accuracy of the fire risk assessment model based on semi supervised machine learning method is better than that of evergreen broad-leaved forest and multi tree meadow. To sum up, the semi supervised machine learning method proposed in this paper provides a new method to understand the problem of fire risk. In the future improvement, we also need to consider a fire risk assessment model which can be used in the whole area and suitable for multi class objects in the future. At the same time, the method proposed in this paper still has the space to improve the accuracy, for example, it can be considered to have a higher correlation with the fire activity. In general, the semi supervised machine learning method proposed in this paper is an effective means of using remote sensing data to assess the risk of fire.

【學(xué)位授予單位】：中國科學(xué)院大學(xué)(中國科學(xué)院遙感與數(shù)字地球研究所)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2017
【分類號】：X932;TP79

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本文編號：1883940