1,568 research outputs found

    Mapping forests in monsoon Asia with ALOS PALSAR 50-m mosaic images and MODIS imagery in 2010.

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    Extensive forest changes have occurred in monsoon Asia, substantially affecting climate, carbon cycle and biodiversity. Accurate forest cover maps at fine spatial resolutions are required to qualify and quantify these effects. In this study, an algorithm was developed to map forests in 2010, with the use of structure and biomass information from the Advanced Land Observation System (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) mosaic dataset and the phenological information from MODerate Resolution Imaging Spectroradiometer (MOD13Q1 and MOD09A1) products. Our forest map (PALSARMOD50 m F/NF) was assessed through randomly selected ground truth samples from high spatial resolution images and had an overall accuracy of 95%. Total area of forests in monsoon Asia in 2010 was estimated to be ~6.3 × 10(6 )km(2). The distribution of evergreen and deciduous forests agreed reasonably well with the median Normalized Difference Vegetation Index (NDVI) in winter. PALSARMOD50 m F/NF map showed good spatial and areal agreements with selected forest maps generated by the Japan Aerospace Exploration Agency (JAXA F/NF), European Space Agency (ESA F/NF), Boston University (MCD12Q1 F/NF), Food and Agricultural Organization (FAO FRA), and University of Maryland (Landsat forests), but relatively large differences and uncertainties in tropical forests and evergreen and deciduous forests

    Mapping annual forest cover by fusing PALSAR/PALSAR-2 and MODIS NDVI during 2007–2016

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    Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) HH and HV polarization data were used previously to produce annual, global 25 m forest maps between 2007 and 2010, and the latest global forest maps of 2015 and 2016 were produced by using the ALOS-2 PALSAR-2 data. However, annual 25 m spatial resolution forest maps during 2011–2014 are missing because of the gap in operation between ALOS and ALOS-2, preventing the construction of a continuous, fine resolution time-series dataset on the world's forests. In contrast, the MODerate Resolution Imaging Spectroradiometer (MODIS) NDVI images were available globally since 2000. This research developed a novel method to produce annual 25 m forest maps during 2007–2016 by fusing the fine spatial resolution, but asynchronous PALSAR/PALSAR-2 with coarse spatial resolution, but synchronous MODIS NDVI data, thus, filling the four-year gap in the ALOS and ALOS-2 time-series, as well as enhancing the existing mapping activity. The method was developed concentrating on two key objectives: 1) producing more accurate 25 m forest maps by integrating PALSAR/PALSAR-2 and MODIS NDVI data during 2007–2010 and 2015–2016; 2) reconstructing annual 25 m forest maps from time-series MODIS NDVI images during 2011–2014. Specifically, a decision tree classification was developed for forest mapping based on both the PALSAR/PALSAR-2 and MODIS NDVI data, and a new spatial-temporal super-resolution mapping was proposed to reconstruct the 25 m forest maps from time-series MODIS NDVI images. Three study sites including Paraguay, the USA and Russia were chosen, as they represent the world's three main forest types: tropical forest, temperate broadleaf and mixed forest, and boreal conifer forest, respectively. Compared with traditional methods, the proposed approach produced the most accurate continuous time-series of fine spatial resolution forest maps both visually and quantitatively. For the forest maps during 2007–2010 and 2015–2016, the results had greater overall accuracy values (>98%) than those of the original JAXA forest product. For the reconstructed 25 m forest maps during 2011–2014, the increases in classifications accuracy relative to three benchmark methods were statistically significant, and the overall accuracy values of the three study sites were almost universally >92%. The proposed approach, therefore, has great potential to support the production of annual 25 m forest maps by fusing PALSAR/PALSAR-2 and MODIS NDVI during 2007–2016

    Estimation and Mapping the Rubber Trees Growth Distribution using Multi Sensor Imagery With Remote Sensing and GIS Analysis

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    The plantation of rubber tree in different countries throughout the world are expanded rapidly in areas that are not known before in planting such as these vegetation species. Estimating and mapping the distribution of rubber trees stand ages in these regions is very necessary to get better understanding of the effects of the changes of land cover on the Carbon and Water Cycle and also the productivity of the latex in different ages. Many remote sensing techniques that have been used to estimate the land cover / land use for mapping and monitoring the distribution of rubber trees growth based on different remote sensing classification algorithms (Maximum likelihood, SAM classification, Decision Tree and Mahalanobis Distance) with different types of data (Multispectral, Hyperspectral or statistical) by using many sensor

    Burnt area mapping in insular Southeast Asia using medium resolution satellite imagery

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    Burnt area mapping in humid tropical insular Southeast Asia using medium resolution (250-500m) satellite imagery is characterized by persisting cloud cover, wide range of land cover types, vast amount of wetland areas and highly varying fire regimes. The objective of this study was to deepen understanding of three major aspects affecting the implementation and limits of medium resolution burnt area mapping in insular Southeast Asia: 1) fire-induced spectral changes, 2) most suitable multitemporal compositing methods and 3) burn scars patterns and size distribution. The results revealed a high variation in fire-induced spectral changes depending on the pre-fire greenness of burnt area. It was concluded that this variation needs to be taken into account in change detection based burnt area mapping algorithms in order to maximize the potential of medium resolution satellite data. Minimum near infrared (MODIS band 2, 0.86μm) compositing method was found to be the most suitable for burnt area mapping purposes using Moderate Resolution Imaging Spectroradiometer (MODIS) data. In general, medium resolution burnt area mapping was found to be usable in the wetlands of insular Southeast Asia, whereas in other areas the usability was seriously jeopardized by the small size of burn scars. The suitability of medium resolution data for burnt area mapping in wetlands is important since recently Southeast Asian wetlands have become a major point of interest in many fields of science due to yearly occurring wild fires that not only degrade these unique ecosystems but also create regional haze problem and release globally significant amounts of carbon into the atmosphere due to burning peat. Finally, super-resolution MODIS images were tested but the test failed to improve the detection of small scars. Therefore, super-resolution technique was not considered to be applicable to regional level burnt area mapping in insular Southeast Asia.Laaja valikoima erilaisia maankäyttöluokkia, pilvisyys ja kosteikkoalueiden suuri määrä luovat erityispiirteet paloalueiden kartoitukselle Kaakkois-Aasian saariston kostean troppisissa olosuhteissa keskiresoluutioisilla (250m-500m) satelliittikuva-aineistoilla. Tämän tutkimuksen tavoitteena oli syventää ymmärrystä keskiresoluutioisen paloaluekartoituksen toteutukseen ja rajoituksiin Kaakkois-Aasian saaristossa vaikuttavista tekijöistä. Tutkimuksen tulokset paljastivat suurta vaihtelua tulipalojen aiheuttamissa heijastussäteilyn muutoksissa riippuen palaneen alueen vehreydestä ennen tulipaloa. Johtopäätöksenä todettiin että keskiresoluutioisten satelliittikuvien koko potentiaalin hyödyntämiseksi paloalueiden kartoituksessa tämä vaihtelu tulisi ottaa huomioon paloalueiden havainnointialgoritmeissa jotka perustuvat heijastussäteilyn muutosten seurantaan. Tähän ajatukseen perustuvaa paloalueiden kartoitusta myös kokeiltiin aineistoilla jotka oli tutkimuksissa todettu parhaiten tarkoitukseen sopiviksi. Paloalueiden muoto- ja kokojakauman analyysiin sekä käytännön testeihin perustuen keskiresoluutioinen paloalueiden kartoitus todettiin käyttökelpoiseksi Kaakkois-Aasian saariston kosteikkoalueilla. Muilla alueilla sen sijaan paloalueiden pieni koko uhkasi vakavasti sen käyttökelpoisuutta. Keskiresoluutioisten satelliittikuva-aineistojen käyttökelpoisuus paloalueiden kartoitukseen kosteikkoalueilla on kuitenkin merkittävää sillä viime aikoina Kaakkois-Aasian kosteikkoalueet ovat monilla tieteenaloilla nousseet kiinnostuksen kohteeksi vuosittain esiintyvien tulipalojen takia. Vuosittaiset tulipalot eivät ainoastaan heikennä näitä ainutlaatuisia ekosysteemejä vaan lähinnä palavan turpeen johdosta myös aiheuttavat pahoja alueellisia savusumuongelmia ja vapauttavat maailmanlaajuisesti merkittäviä määriä hiilidioksidia ilmakehään. Tämän tutkimuksen tulokset osaltaan luovat pohjaa yhä tarkempien alueellisten paloalueiden kartoitusmenetelmien kehittämiselle. Näillä menetelmillä kerättävä tieto paloalueiden laajuudesta ja sijainneista antaa muiden alojen tutkijoille yhä paremmat mahdollisuudet arvioida Kaakkois-Aasian saariston kosteikkoalueiden tulipalojen paikallisia, alueellisia ja maailmanlaajuisia vaikutuksia

    Identifying and Forecasting Potential Biophysical Risk Areas within a Tropical Mangrove Ecosystem Using Multi-Sensor Data

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    Mangroves are one of the most productive ecosystems known for provisioning of various ecosystem goods and services. They help in sequestering large amounts of carbon, protecting coastline against erosion, and reducing impacts of natural disasters such as hurricanes. Bhitarkanika Wildlife Sanctuary in Odisha harbors the second largest mangrove ecosystem in India. This study used Terra, Landsat and Sentinel-1 satellite data for spatio-temporal monitoring of mangrove forest within Bhitarkanika Wildlife Sanctuary between 2000 and 2016. Three biophysical parameters were used to assess mangrove ecosystem health: leaf chlorophyll (CHL), Leaf Area Index (LAI), and Gross Primary Productivity (GPP). A long-term analysis of meteorological data such as precipitation and temperature was performed to determine an association between these parameters and mangrove biophysical characteristics. The correlation between meteorological parameters and mangrove biophysical characteristics enabled forecasting of mangrove health and productivity for year 2050 by incorporating IPCC projected climate data. A historical analysis of land cover maps was also performed using Landsat 5 and 8 data to determine changes in mangrove area estimates in years 1995, 2004 and 2017. There was a decrease in dense mangrove extent with an increase in open mangroves and agricultural area. Despite conservation efforts, the current extent of dense mangrove is projected to decrease up to 10% by the year 2050. All three biophysical characteristics including GPP, LAI and CHL, are projected to experience a net decrease of 7.7%, 20.83% and 25.96% respectively by 2050 compared to the mean annual value in 2016. This study will help the Forest Department, Government of Odisha in managing and taking appropriate decisions for conserving and sustaining the remaining mangrove forest under the changing climate and developmental activities

    Classification and mapping of paddy rice by combining Landsat and SAR time series data

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    Rice is an important food resource, and the demand for rice has increased as population has expanded. Therefore, accurate paddy rice classification and monitoring are necessary to identify and forecast rice production. Satellite data have been often used to produce paddy rice maps with more frequent update cycle (e.g., every year) than field surveys. Many satellite data, including both optical and SAR sensor data (e.g., Landsat, MODIS, and ALOS PALSAR), have been employed to classify paddy rice. In the present study, time series data from Landsat, RADARSAT-1, and ALOS PALSAR satellite sensors were synergistically used to classify paddy rice through machine learning approaches over two different climate regions (sites A and B). Six schemes considering the composition of various combinations of input data by sensor and collection date were evaluated. Scheme 6 that fused optical and SAR sensor time series data at the decision level yielded the highest accuracy (98.67% for site A and 93.87% for site B). Performance of paddy rice classification was better in site A than site B, which consists of heterogeneous land cover and has low data availability due to a high cloud cover rate. This study also proposed Paddy Rice Mapping Index (PMI) considering spectral and phenological characteristics of paddy rice. PMI represented well the spatial distribution of paddy rice in both regions. Google Earth Engine was adopted to produce paddy rice maps over larger areas using the proposed PMI-based approach

    Mapping Forest Cover in Northeast China from Chinese HJ-1 Satellite Data Using an Object-Based Algorithm

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    Forest plays a significant role in the global carbon budget and ecological processes. The precise mapping of forest cover can help significantly reduce uncertainties in the estimation of terrestrial carbon balance. A reliable and operational method is necessary for a rapid regional forest mapping. In this study, the goal relies on mapping forest and subcategories in Northeast China through the use of high spatio-temporal resolution HJ-1 imagery and time series vegetation indices within the context of an object-based image analysis and decision tree classification. Multi-temporal HJ-1 images obtained in a single year provide an opportunity to acquire phenology information. By analyzing the difference of spectral and phenology information between forest and non-forest, forest subcategories, decision trees using threshold values were finally proposed. The resultant forest map has a high overall accuracy of 0.91 ± 0.01 with a 95% confidence interval, based on the validation using ground truth data from field surveys. The forest map extracted from HJ-1 imagery was compared with two existing global land cover datasets: GlobCover 2009 and MCD12Q1 2009. The HJ-1-based forest area is larger than that of MCD12Q1 and GlobCover and more closely resembles the national statistics data on forest area, which accounts for more than 40% of the total area of the Northeast China. The spatial disagreement primarily occurs in the northern part of the Daxing'an Mountains, Sanjiang Plain and the southwestern part of the Songliao Plain. The compared result also indicated that the forest subcategories information from global land cover products may introduce large uncertainties for ecological modeling and these should be cautiously used in various ecological models. Given the higher spatial and temporal resolution, HJ-1-based forest products could be very useful as input to biogeochemical models (particularly carbon cycle models) that require accurate and updated estimates of forest area and type

    접근불가지역인 북한의 시계열 토지피복도 매핑 및 산림 변화 동향 분석

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    학위논문(석사) -- 서울대학교대학원 : 농업생명과학대학 생태조경·지역시스템공학부(생태조경학), 2021.8. 이동근.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% ± 0.9%, and the Kappa coefficient was 0.94 ± 0.02. Using LULC change detection, our research effectively explains the change trajectory of North Korea’s current LULC, providing new insights into the change characteristics of North Korea’s croplands and forests. Further, our results show that North Korea’s urban area has increased significantly, its forest cover has increased slightly, and its cropland cover has decreased. We determined that North Korea’s Forest protection policies have led to the forest restoration. Thus, as agriculture is one of North Korea’s main economic contributors, croplands have been forced to relocate, expanding to other regions to compensate for the land loss caused by forest restoration.북한은 세계에서 가장 심각하게 황폐화된 산림 중 하나를 포함하고 있지만 최근에는 산림 복원을 강조하고 있다. 산림 복원이 일어나는 정도를 이해하기 위해서는 토지 이용과 토지 피복 변화 경향 (LULCC)을 이해해야 한다. 따라서 본 연구는 30m 해상도의 현대 시계열 토지 이용 토지 피복 (LULC)지도를 구성 및 분석하여 북한 전역의 장기 LULCC를 식별하고 산림 변화 추세를 이해하는 것을 목표로 한다. 2001 - 2018 년 기간 동안 국가의 LULC지도는 30m 해상도 위성 이미지 시계열 데이터를 기반으로 반영구적 포인트 분류 및 기계 학습을 사용하여 구성되었으며, 이는 GEE (Google Earth Engine)에서 수집 한 현상 학적 정보와 함께 사용되고 있다. 또한 LULCC 탐지기 법과 경작지 변화와 고도의 관계를 고려하여 2001 - 2018 년 북한의 산림 변화를 평가하였다. LULC 맵 결과의 전체 분류 정확도는 97.5 % ± 0.9 %이고, Kappa 계수는 0.94 ± 0.02 이다. LULCC 탐지는 또한 2001 - 2018 년에 북한의 산림 면적이 약간 증가한 것으로 나타났다. 일반적으로 산림 피복 면적은 크게 변하지 않았으나 남부와 중부 지역의 산림 복원과 북부와 서부의 경작지 상대적 증가 측면에서 뚜렷한 공간적 변화가 관찰되었다. 북한의 특성과 산림 정책 문서를 검토 한 결과 북한 근대 산림의 일부 지역이 복원되고 있음을 확인하였다.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 51석
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