Deep Learning Methods for Remote Sensing

Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing

Mapping land degradation using remote sensing data and an unsupervised clustering algorithm in the eThekwini Metropolitan Area.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Land degradation is a major environmental problem facing South Africa and many other countries around the world. For proper management and adoption of best rehabilitation strategies, a compendious regional-scale assessment approach is needed to attain the full extent of the impairment. The aim of this study was to assess the spatial extent of land degradation with the use of GIS and remote sensing techniques in the eThekwini Metropolitan Area (EMA), KwaZulu-Natal, South Africa. The first objective was to review the status of land degradation in South Africa, as well as tracking of emerging trends in remote sensing and Geographic Information Systems research. Historically, in South Africa, land degradation has been associated with poverty and rurality. While conducting studies was also a challenge, demanding high human and economic resources. Although these studies were accurate and invaluable, most of them were too localized and highly difficult to replicate. The introduction of remote sensing has bought a new dimension with a timely spatial mapping of land degradation at regional scales. As a result, there thus been a sharp increase in remote sensing-based land degradation studies, this is also accompanied by the recent improvements in capabilities of remote sensors and associated GIS platforms. However, there is still a challenge of accessibility, especially for financial constricted regions such as the sub-Sahara of Africa. Most of the cutting-edge remote sensing data such as the hyperspectral and high spatial resolution imagery are highly expensive and therefore inaccessible to those not affording. However, the use of new-age medium resolution sensors is a potential solution. The second objection of this study was to detect and map the spatial distribution of land degradation in the EMA through use of Sentinel-2 derived vegetation indices (VIs) in conjunction with a hierarchical clustering algorithm. Data from Sentinel-2 was used to derive VIs used in this study, these are namely; NDVI, RVI, SAVI; and SARVI. The framework using Ward’s hierarchical clustering performed relatively good to produce 6 clusters that achieved an overall classification accuracy (OA) of 88.81% when mapping land-cover including land degradation. In this regard, land degradation achieved the highest classification accuracy of up to 100%, while water achieved the lowest at 63.33%. Although there was quite a significant difference in accuracies between different land-cover classes, overall, the results were still reasonably good with an error rate of 0.14 and Kappa Coefficient of 0.86. The results from this study, therefore, suggest that Ward’s unsupervised clustering approach is a suitable tool for mapping of complex land-cover classes, particularly land degradation

Performance evaluation of object based greenhouse detection from Sentinel-2 MSI and Landsat 8 OLI data: A case study from Almería (Spain)

tThis paper shows the first comparison between data from Sentinel-2 (S2) Multi Spectral Instrument (MSI)and Landsat 8 (L8) Operational Land Imager (OLI) headed up to greenhouse detection. Two closely relatedin time scenes, one for each sensor, were classified by using Object Based Image Analysis and RandomForest (RF). The RF input consisted of several object-based features computed from spectral bands andincluding mean values, spectral indices and textural features. S2 and L8 data comparisons were alsoextended using a common segmentation dataset extracted form VHR World-View 2 (WV2) imagery totest differences only due to their specific spectral contribution. The best band combinations to performsegmentation were found through a modified version of the Euclidian Distance 2 index. Four differentRF classifications schemes were considered achieving 89.1%, 91.3%, 90.9% and 93.4% as the best overallaccuracies respectively, evaluated over the whole study area

Land use change and climate variation in the Three Gorges Reservoir Catchment from 2000 to 2015 based on the Google Earth Engine

Possible environmental change and ecosystem degradation have received increasing attention since the construction of Three Gorges Reservoir Catchment (TGRC) in China. The advanced Google Earth Engine (GEE) cloud-based platform and the large number of Geosciences and Remote Sensing datasets archived in GEE were used to analyze the land use and land cover change (LULCC) and climate variation in TGRC. GlobeLand30 data were used to evaluate the spatial land dynamics from 2000 to 2010 and Landsat 8 Operational Land Imager (OLI) images were applied for land use in 2015. The interannual variations in the Land Surface Temperature (LST) and seasonally integrated normalized difference vegetation index (SINDVI) were estimated using Moderate Resolution Imaging Spectroradiometer (MODIS) products. The climate factors including air temperature, precipitation and evapotranspiration were investigated based on the data from the Global Land Data Assimilation System (GLDAS). The results indicated that from 2000 to 2015, the cultivated land and grassland decreased by 2.05% and 6.02%, while the forest, wetland, artificial surface, shrub land and waterbody increased by 3.64%, 0.94%, 0.87%, 1.17% and 1.45%, respectively. The SINDVI increased by 3.209 in the period of 2000-2015, while the LST decreased by 0.253 °C from 2001 to 2015. The LST showed an increasing trend primarily in urbanized area, with a decreasing trend mainly in forest area. In particular, Chongqing City had the highest LST during the research period. A marked decrease in SINDVI occurred primarily in urbanized areas. Good vegetation areas were primarily located in the eastern part of the TGRC, such as Wuxi County, Wushan County, and Xingshan County. During the 2000–2015 period, the air temperature, precipitation and evapotranspiration rose by 0.0678 °C/a, 1.0844 mm/a, and 0.4105 mm/a, respectively. The climate change in the TGRC was influenced by LULCC, but the effect was limited. What is more, the climate change was affected by regional climate change in Southwest China. Marked changes in land use have occurred in the TGRC, and they have resulted in changes in the LST and SINDVI. There was a significantly negative relationship between LST and SINDVI in most parts of the TGRC, especially in expanding urban areas and growing forest areas. Our study highlighted the importance of environmental protection, particularly proper management of land use, for sustainable development in the catchment

The use of satellite remote sensing for flood risk management

Over the last decades the impact of natural disasters to the global environment is becoming more and more severe. The number of disasters has dramatically increased, as well as the cost to the global economy and the number of people affected. Among the natural disaster, flood catastrophes are considered to be the most costly, devastating, broad extent and frequent, because of the tremendous fatalities, injuries, property damage, economic and social disruption they cause to the humankind. In the last thirty years, the World has suffered from severe flooding and the huge impact of floods has caused hundreds of thousands of deaths, destruction of infrastructures, disruption of economic activity and the loss of property for worth billions of dollars. In this context, satellite remote sensing, along with Geographic Information Systems (GIS), has become a key tool in flood risk management analysis. Remote sensing for supporting various aspects of flood risk management was investigated in the present thesis. In particular, the research focused on the use of satellite images for flood mapping and monitoring, damage assessment and risk assessment. The contribution of satellite remote sensing for the delineation of flood prone zones, the identification of damaged areas and the development of hazard maps was explored referring to selected cases of study

Evolution of Ecological Security in the Tableland Region of the Chinese Loess Plateau Using a Remote-Sensing-Based Index

Maintaining optimal ecological security is a serious issue in the Chinese Loess Plateau (CLP). Remote sensing ecological indexes (RSEI) of three main tableland regions of the CLP were calculated based on spectral information provided by remote sensing imaging satellites between 2000 and 2018. We were able to use RSEI values to systematically evaluate the temporal and spatial variation in the regional ecological environment and determine the influential factors that mainly associated with these changes. The results showed that between 2000 and 2018, the ecological environment improved, remained stable, and deteriorated, respectively, in the Gansu, Shaanxi, and Shanxi tablelands. Regions with poor or fair RSEIs were concentrated around the main river basins, while regions with moderate RSEIs were associated with poor ecological conditions and poor areas. The significant spatiotemporal variation in RSEI indicates that the ecological system in this region is relatively fragile. We also observed that natural factors such as the temperature, potential evapotranspiration, and precipitation had the greatest influence on the overall ecological quality. The rapid increase in the regional population and human activity played an important role in the variation in the regional RSEI. This research will provide important information on controlling regional soil erosion and ecological restoration in the CLP

Using CORONA and Landsat Data for Evaluating and Mapping Long-term LULC Changes in Iraqi Kurdistan

This research implements a new approach to classifying CORONA images. A case study demonstrated that information can be extracted from CORONA images using an automated classification process instead of on-screen manual digitising. This study, of the urban growth in Iraqi Kurdistan, has shown that the timeline of a change detection analysis can be extended to include the period before Landsat missions started in 1972. Urban growth was caused by economic growth and population increase

라오스 북부 지역의 토지이용과 가계 소득에 관한 연구 - 우돔싸이 푸히피 국가 생물다양성 보전 지역 인근 두 마을의사례 -

학위논문 (석사)-- 서울대학교 대학원 농업생명과학대학 산림과학부, 2017. 8. YOUN, Yeo-Chang.라오스 북부 우돔싸이주푸히피 국가 생물다양성 보전 지역(NBCA)에 거주하는 지역주민의 75~80%가량은 산림을 농지로 전용하여 농작물을 생산하는 일에 생계를 의존한다. 이 연구의 목적은 라오스 북부 산림 지역의 가구 소득과 토지이용 변화에 영향을 미치는 요인을 규명하고, 화전 경작으로 인한 라오스 산림 전용을 방지하기 위해 발생할 기회비용을 산정하는 것이다. 두 마을에서 74개 가구를 랜덤 추출한 뒤 빈곤환경네트워크(PEN) 가이드라인을 이용하여 인터뷰하였고, 현장조사와 ArcGIS 프로그램에서 제공하는 위성영상을 이용하여 토지이용 변화를 분석하였다. 가구 소득과 토지이용 변화에 영향을 미치는 요인을 분석하기 위해 다중회귀분석을 실시하였고, 산림 전용 방지의 기회비용을 산정한 뒤 이를 순현재가치(NPV)로 변환하였다. 이 연구에서 우돔싸이 지역의 가구 소득은 주로 비목재임산물, 목재, 땔감 등의 임산물과 쌀, 옥수수, 카르다몸 등의 농작물, 그리고 가축 축산물로부터의 소득으로 구성되었다. 전체 응답을 분석한 결과, 가족 수, 중간 상인을 이용한 시장 접근, 가축의 수가 이들의 가구 소득에 영향을 미치는 요인으로 나타났다. 후아이상(Huaysang) 마을에서는 가족 수(-), 시장 접근성(+), 교육 년 수(+), 중형 가축의 수(+)가 가구 소득에 영향을 미쳤으며, 나사이통(Naxaythong) 마을에서는 대형 가축의 수(+)만 가구 소득에 영향을 미쳤다. 임산물과 농작물, 축산물로부터 얻은 가구 소득이 높을수록 화전 경작 방지를 통한 온신가스 감축의 기회비용이 크다. 쌀과 다른 농작물 경작으로부터 얻게 될 소득은 라오스 북부 산촌 마을의 토지이용 변화로부터 온실가스를 감축하기 위한 기회비용의 주요 원천이다. 따라서 이 연구에서는 화전 경작과 관련된 정부의 정책이 농업과 농작물 거래로부터 가구 소득을 증가시키는 것보다 생물다양성 보전에 기반한 경제 활동을 장려할 것을 제안한다.About 75-80% of people in the rural community in the Phuhiphi-National Biodiversity Conservation Area (NBCA) of Oudomxay Province in Northern Laos depend on the conversion of forests to agriculture lands for crop productions to support their livelihoods. This study aims to identify the factors influencing the household income and land use change in the mountain areas of Northern Laos, and its goal is to estimate the opportunity cost for avoiding deforestation from shifting cultivation. The randomized collection method and the Poverty Environment Network (PEN) guideline was utilized for surveys. 74 households (85% of the population) in two villages were interviewed, and land use changes were interpreted using satellite imageries by ArcGIS application. Additionally, the multiple linear regression method was used for investigating the factors influencing household income and land use change, and the opportunity cost of forest conservation was estimated. This research showed that the household income was mainly obtained from forest products (NTFPs, timber and fuelwood), crop production (rice, corn and cardamom) and livestock husbandry. The income of households is influenced by the size of family, marketing by middlemen, as well as number of livestock animals. In Huaysang village, the family size negatively affected household income, while market access, years of education and number of medium-size animals contributed positively to household income. In Naxaythong village, only number of big-size animals positively affected household income. The higher household income obtained from forest products and agricultural practices, including crop productions and livestock, the higher opportunity cost of avoiding shifting cultivation. Thus, forgone income from shifting cultivation for rice and other crop cultivation is the main source of opportunity cost for greenhouse gas mitigation from land use change in the mountain villages of Northern Laos. Finally, this study suggests that government policies related shifting cultivation should promote economic activities based on biodiversity conservation, rather than promoting household income from agriculture and trade of commercial crops.Chapter 1 Introduction 1 1.1 Background 1 1.2 Objectives 3 Chapter 2 Literature review 4 2.1 Key definition 4 2.2 Opportunity cost of REDD+ 7 2.3 Livelihoods of indigenous communities 7 2.4 Factors influencing land use change and deforestation 8 2.5 Household activities and shifting cultivation in Lao PDR 10 2.6 Land use and deforestation and forest degradation in Lao PDR 11 Chapter 3 Materials and methodology 12 3.1 Study area 12 3.2 Materials 15 3.3 Methodology 19 3.4 Characteristics of the households 28 3.5 Household livelihood 35 3.6 Marketing method in two villages 35 3.7 Land use characteristics of two villages 35 Chapter 4 Results and discussion 37 4.1 Household income 37 4.2 Household revenues 40 4.3 Factors related to household incomes and land use changes 41 4.4 Land uses and land use change 53 4.5 Co2 emission from land use change 53 4.6 Opportunity cost of avoiding deforestation and forest degradation 54 4.7 Discussion 58 Chapter 5 Conclusion and recommendation 62 5.1 Conclusion 62 5.2 Recommendation 64 Bibliography 65 Appendix 72 Acknowledgement 79 Korean Abstract (초 록) 80Maste

Nighttime Lights as a Proxy for Economic Performance of Regions

Studying and managing regional economic development in the current globalization era demands prompt, reliable, and comparable estimates for a region’s economic performance. Night-time lights (NTL) emitted from residential areas, entertainment places, industrial facilities, etc., and captured by satellites have become an increasingly recognized proxy for on-ground human activities. Compared to traditional indicators supplied by statistical offices, NTLs may have several advantages. First, NTL data are available all over the world, providing researchers and official bodies with the opportunity to obtain estimates even for regions with extremely poor reporting practices. Second, in contrast to non-standardized traditional reporting procedures, the unified NTL data remove the problem of inter-regional comparability. Finally, NTL data are currently globally available on a daily basis, which makes it possible to obtain these estimates promptly. In this book, we provide the reader with the contributions demonstrating the potential and efficiency of using NTL data as a proxy for the performance of regions