Дисертації з теми "Monitoring of Land cover"
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1
Skelsey, Chris. "A system for monitoring land cover." Thesis, University of Aberdeen, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.361785.
Повний текст джерелаАнотація:
Underlying the majority of remotely-sensed data analysis is the assumption that geographical phenomena, such as rivers, heather-moors and the dynamics associated with such objects, can be adequately detected and identified through the use of spectral and other visual information alone. There is a common misconception that any major deficiencies of quantitative analyses are "hardware problems": that by increasing the spectral, spatial, radiometric and temporal resolutions of sensors, geographical phenomena will be identified with similarly increasing accuracy and reliability. This, however, is an unrealistic viewpoint. This thesis has developed a prototype of an automated system based on the principle that by considering the "real-world" properties of the land, a more effective and robust analysis of its dynamic nature can ensue. SYMOLAC is an automated SYstem for MOnitoring LAnd Cover based upon theories of artificial intelligence. It has been developed within a specifically designed hybrid software environment called ETORA, an Environment for Task-Orientated Analysis. This prototype environment allows SYMOLAC to utilise disparate sources of spatial data, to reason with both quantitative and qualitative knowledge, to model disparate domain uncertainties, and to exploit the functionality of third-party software components. Unlike standard approaches, it allows an automated analysis to focus on each particular domain task and how it may best be performed with the available data, knowledge and software resources. The detection of forest felling and the subsequent update of the Land Cover of Scotland (1988) dataset forms the initial application of SYMOLAC. It is concluded that the system's approach is flexible, extensible and adaptable, and demonstrates one way in which satellite imagery can offer potential to the future monitoring of complex land cover change without the need for human intervention.
2
Maus, Victor Wegner. "Land use and land cover monitoring using remote sensing image time series." Instituto Nacional de Pesquisas Espaciais (INPE), 2016. http://urlib.net/sid.inpe.br/mtc-m21b/2016/06.01.14.07.
Повний текст джерелаАнотація:
Land system change has a wide range of impacts on Earth system components. Tropical forests in particular have been identified as crucial ecosystems for climate regulation, global biodiversity, and hydrological cycling. The Brazilian Amazon has experienced a high rate of deforestation in the last decade and it is the main source of Brazils anthropogenic CO$_{2}$ emissions. The growing global population will further increase the demand for food and therefore increase the pressure on agricultural systems. High quality, fine resolution, and near-real time land use and land cover monitoring systems play a crucial role in generating information to advance our understanding of human impact on land cover. Earth Observation satellites are the only source that provides a continuous and consistent set of information about the Earth${'}$s land. The current large-scale classification systems such as MODIS Land Cover and GLC 2000 have limitations and their accuracy is not sufficient for land change modeling. Therefore, new techniques for improving land system products are urgently needed. The contribution of this thesis to Earth System Science is threefold. Firstly, the thesis presents a new method for analysis of remote-sensed image time series that improves spatio-temporal land cover data sets and has a substantial potential for contributing to land system change modeling. The developed Time- Weighted Dynamic Time Warping (TWDTW) method is a time-constraint variation of the well-known Dynamic Time Warping (DTW) method, which has in the extensive literature proved to be a robust time series data mining. Secondly, this thesis contributed to open and reproducible science by making the algorithms available for larger audience. TWDTW is implemented in an open source R package called dtwSat available in the Comprehensive R Archive Network (CRAN). Thirdly, this thesis presents an analysis of land cover changes in the Amazon, focusing on the Brazilian state of Mato Grosso that has gone through high rate of deforestation and cropland expansion in the last decade. This study identified and estimated the land cover change using MODIS image time series, contributing to better understand the land dynamics in the Brazilian Amazon. In the study area the pasture is the dominant land use after deforestation, whereas most of the single cropping area comes from pasture, and the cropping system is undergoing intensification from single to double cropping. Moreover, the regenerative secondary forest comes mainly from pasture. The study showed the potential of the TWDTW method for large-scale remote sensing data analysis, which could be extended to other Brazilian biomes to help understand land change in the whole Brazilian territory.Mudanças na superfície da terra têm uma ampla gama de impactos sobre o sistema terrestre. Florestas tropicais, em particular, são ecossistemas cruciais para regulação climática, manutenção da biodiversidade, a ciclo hidrológico. Na última década a Amazônia brasileira tem experimentado uma alta taxa de desmatamento, sendo a principal fonte de emissões antropogênicas de CO$_{2}$ no Brasil. O crescimento da população mundial vai aumentar ainda mais a demanda por alimentos e, portanto, aumentar a pressão sobre agrícultura e pecuária. Dados com alta qualidade, melhor resolução espacial e temporal, e o desenvolvimento de sistemas de monitoramento desempenham um papel crucial na geração de informações para avançar nossa compreensão sobre os impactos humanos na cobertura da terra. Os satélites de observação da Terra são a única fonte que fornece um conjunto contínuo e consistente de informações sobre nosso planeta. Sistemas de classificação em grande escala, como MODIS Land Cover e GLC 2000 têm limitações e sua acurácia não é suficiente para a modelagem de mudanças de use da terra. Portanto, são necessárias novas técnicas para melhoramento dos dados de use e cobertura da terra. Esta tese traz três contribuições para a Ciência do Sistema Terrestre. Primeiramente, esta tese apresenta um novo método para análise de séries temporais de imagens satélite que melhora a classificação de cobertura da terra. O método tem grande potencial para contribuir para a modelagem de mudanças do sistema terrestre. O método desenvolvido, Time-Weigted Dynamic Time Warping (TWDTW), é uma adaptação ponderada por tempo do método clássico Dynamic Time Warping (DTW), que tem em uma extensa literatura provando ser um método robusto para mineração de dados em séries temporais. Em segundo lugar, esta tese contribuiu para a ciência aberta e reprodutível, tornando algoritmos disponíveis para o público. TWDTW está implementado em um pacote R de código aberto chamado dtwSat disponível no Comprehensive R Archive Network (CRAN). Em terceiro lugar, esta tese apresenta uma análise as mudanças do uso e cobertura da terra na Amazônia, com foco no estado do Mato Grosso, que passou por alta taxa de desmatamento e expansão agrícola na última década. Este estudo identificou e estimou mudanças de cobertura da terra com séries temporais de imagens MODIS, contribuindo para melhor compreender a dinâmica de ocupação da terra na Amazônia brasileira. Na área de estudo, a pastagem é o uso dominante após o desmatamento, ao passo que a maior parte da área de cultivo com um ciclo anual provem da área de pasto, com o sistema de cultivo passando por intensificação, mudando de cultivo simples para cultivo duplo. Além disso, áreas de regeneração vêm, principalmente, de áreas de pastagem. O estudo mostrou o potencial do método de TWDTW para análise de dados de sensoriamento remoto em grande escala, que poderia ser estendido a outros biomas brasileiros para ajudar a entender as mudanças da terra em todo o território brasileiro.
3
Ek, Edgar. "Monitoring Land Use and Land Cover Changes in Belize, 1993-2003: A Digital Change Detection Approach." Ohio University / OhioLINK, 2004. http://www.ohiolink.edu/etd/view.cgi?ohiou1102520727.
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4
Hohlmann, Glen. "Monitoring land-cover change, an example of forest change in Peninsular Malaysia." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0018/MQ48392.pdf.
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Hohlmann, Glen Carleton University Dissertation Geography. "Monitoring land-cover change; an example of forest change in Peninsular Malaysia." Ottawa, 1999.
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6
Qi, Zhixin, and 齐志新. "Short-interval monitoring of land use and land cover change using RADARSAT-2 polarimetric SAR images." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hdl.handle.net/10722/194623.
Повний текст джерелаАнотація:
Land use and land cover (LULC) change information is essential in urban planning and management. With the rapid urbanization in China, many illegal land developments have emerged in some rapidly developing regions and have caused irreversible environmental problems, posing a threat to sustainable urban development. Short-interval monitoring of LULC change therefore is necessary in these regions to control and prevent illegal land developments at an early stage.
Conventional optical remote sensing is limited by weather conditions and has difficulties collecting timely data in tropical regions characterized by frequent cloud cover. Radar remote sensing, not affected by clouds, is therefore a potential tool for collecting timely LULC information in these regions. Polarimetric SAR (PolSAR) is more suitable than single-polarization SAR for monitoring LULC change because it can discriminate different types of scattering mechanisms. The overall objective of this study is to conduct short-interval monitoring of LULC change using RADARSAT-2 PolSAR images.
Classification methods that achieve high accuracy for PolSAR images are essential in monitoring LULC change. In this study, a new method, based on the integration of polarimetric decomposition, PolSAR interferometry, object-oriented image analysis, and decision tree algorithms, is proposed for LULC classification using RADARSAT-2 PolSAR data. A comparison between the proposed classification method and Wishart supervised classification which is commonly used for the classification of PolSAR data showed that the proposed method can significantly improve LULC classification accuracy. Polarimetric decomposition, PolSAR interferometry, object-oriented image analysis, and decision tree algorithms have been determined to contribute to the improvement achieved by the proposed classification method.
Selection of appropriate incidence angle is important in LULC classification using PolSAR images because incidence angle influences the intensity and patterns of radar return. Based on the proposed classification method, the present study further investigates the influence of incidence angle on LULC classification using RADARSAT-2 PolSAR images. LULC classifications using incidence angles of 31.50 and 37.56° were conducted separately. The influence of incidence angle on the classification was investigated by comparing the results of the two independent classifications. The comparison showed that large incidence angle performs much better than small incidence angle in the classification of different vegetation types, whereas small incidence angle outperforms large incidence angle in reducing the confusion between urban/built-up areas and vegetation, that between vegetable and barren land, and that among barren land, water, and lawn. Considering that the detection of urban/built-up areas and barren land is important in monitoring illegal land developments, small incidence angle is more suitable than large incidence angle in monitoring illegal land developments.
Change detection methods that achieve high accuracy for PolSAR data are also essential in monitoring LULC change. The current study proposes a new method for LULC change detection using RADARSAT-2 PolSAR images. The proposed change detection method combines change vector analysis (CVA) and post-classification comparison (PCC) to detect LULC changes using object-oriented image analysis. The classification of PolSAR images is based on the proposed classification method. Compared with the PCC based on Wishart supervised classification, the proposed change detection method can achieve much higher accuracy for LULC change detection. Further investigation indicated that CVA, PCC, and object-oriented image analysis all contribute to the higher accuracy achieved by the proposed change detection method.
Short-interval monitoring of LULC change was carried out using a time series of RADARSAT-2 PolSAR images. The monitoring was based on monthly LULC change detection using the proposed change detection method and appropriate incidence angle. The influence of environmental factors on short-interval monitoring of LULC change was investigated by analyzing the monthly change detection results. Paddy harvesting and planting, seasonal crop growth, and change in soil moisture and surface roughness were found to exert significant influence on the short-interval monitoring of LULC change. High accuracy can be achieved for short-interval monitoring of construction sites and bulldozed land using RADARSAT-2 PolSAR images. However, paddy harvesting and growth still cause false alarms on the monitoring of these two LULC classes.
The study indicated that conducting short-interval monitoring of LULC change using RADARSAT-2 PolSAR images is effective. High accuracy can be achieved for short-interval monitoring of construction sites and bulldozed land using the proposed change detection and classification methods, which can provide important information for the control and prevention of illegal land developments at an early stage.published_or_final_version
Urban Planning and Design
Doctoral
Doctor of Philosophy
7
Roberts, Gareth James. "Monitoring land cover dynamics using linear kernel-driven BRDF model parameter temporal trajectories." Thesis, University College London (University of London), 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.407145.
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Sluiter, Raymond. "Mediterranean land cover change : modelling and monitoring natural vegetation using GIS and remote sensing /." Utrecht : Koninklijk Nederlands Aardrijkskundig Genootschap [u.a.], 2005. http://www.loc.gov/catdir/toc/fy0614/2006436726.html.
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Liu, Qingling, and Fanting Gong. "Monitoring land use and land cover change: a combining approach of change detection to analyze urbanization in Shijiazhuang, China." Thesis, Högskolan i Gävle, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-13715.
Повний текст джерелаАнотація:
Detecting the changes of land use and land cover of the earth’s surface is extremely important to achieve continual and precise information about study area for any kinds of planning of the development. Geographic information system and remote sensing technologies have shown their great capabilities to solve the study issues like land use and land cover changes. The aim of this thesis is to produce maps of land use and land cover of Shijiazhuang on year 1993, 2000 and 2009 to monitor the possible changes that may occur particularly in agricultural land and urban or built-up land, and detect the process of urbanization in this city. Three multi-temporal satellite image data, Thematic Mapper image data from year 1993, Enhanced Thematic Mapper image data from 2000 and China Brazil Earth Resource Satellite image data from 2009 were used in this thesis. In this study, supervised classification was the major classification approach to provide classified maps, and five land use and land cover categories were identified and mapped. Post-classification approach was used to improve the qualities of the classified map. The noises in the classified maps will be removed after post-classification process. Normalized difference vegetation index was used to detect the changes of vegetated land and non-vegetated land. Change detection function in Erdas Imagine was used to detect the urban growth and the intensity of changes surrounding the urban areas. Cellular automata Markov was used to simulate the trends of land use and cover change during the period of 1993 to 2000 and 2000 to 2009, and a future land use map was simulated based on the land use maps of year 2000 and 2009. From this performance, the cross-tabulation matrices between different periods were produced to analyze the trends of land use and cover changes, and these statistic data directly expressed the change of land use and land cover. The results show that the agricultural land and urban or built-up land were changed a lot, approximately half of agricultural land was converted into urban or built-up land. This indicates that the loss of agricultural land is associated with the growth of urban or built-up land. Thus, the urbanization took place in Shijiazhuang, and the results of this urban expansion lead to the loss of agricultural land and environmental problems. During the process of detecting the land use and cover change, obtaining of high-precision classified maps was the main problem.
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Qader, Sarchil Hama. "Monitoring decadal land cover and crop production in Iraq using time series remote sensing data." Thesis, University of Southampton, 2016. https://eprints.soton.ac.uk/411281/.
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Almutairi, Abdullah. "Monitoring land-cover change detection in an arid urban environment a comparison of change detection techniques /." Morgantown, W. Va. : [West Virginia University Libraries], 2000. http://etd.wvu.edu/templates/showETD.cfm?recnum=1410.
Повний текст джерелаАнотація:
Thesis (M.A.)--West Virginia University, 2000.Title from document title page. Document formatted into pages; contains xi, 77 p. : ill. (some col.), maps (some col.) Includes abstract. Includes bibliographical references (p. 74-77).
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Kliman, Douglas Hartley. "Rule-based classification of hyper-temporal, multi-spectral satellite imagery for land-cover mapping and monitoring." Diss., The University of Arizona, 1996. http://hdl.handle.net/10150/187473.
Повний текст джерелаАнотація:
A rule-based classification model was developed to derive land-cover information from a large set of hyper-temporal, multi-spectral satellite imagery encompassing the state of Arizona. The model uses Advanced Very High Resolution Radiometer (AVHRR) imagery and the 30-minute digital elevation model (DEM) from the EROS Data Center (EDC) Conterminous U.S. AVHRR Biweekly Composites. Sixty one images from 1990, 1991 and 1992 were analyzed using the Brown & Lowe (1973) Natural Vegetative Communities of Arizona map to identify temporal patterns of Normalized Difference Vegetation Index (NDVI) and thermal measurements for 13 land-cover classes. Fifteen characteristic layers were created to represent the spectral, thermal and temporal properties of the data set. These layers were inputs for the rule-based classification model. The model was run on three years of data, creating three single year land-cover maps. The modeling effort showed that NDVI, thermal and DEM characteristics are useful for discerning land-cover classes. The single year land-cover maps showed that the rule-based model could not detect land-cover change between years. The single year maps were combined to create a summary land-cover map. This map differs from the Brown and Lowe map in the shape, proportional size and spatial distribution of land-cover polygons. The rule-based model can discern more land-cover classes than spectral cluster classification. Ground observations and an aerial video was used to assess map accuracy. The same proportion of agreement was observed between the ground observations, the Brown and Lowe map, and the summary land-cover map. Agreement was higher between video and the summary map than between video and the Brown and Lowe map. With further refinements to the input data set, classification model rules and field accuracy assessment, higher levels of agreement can be expected. Overall results show that rule-based classification of hyper-temporal, multi-spectral satellite imagery is a desirable method for mapping global land-cover.
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Hunt, John W. "Monitoring and resource management : streams, land cover, and the use of water quality information in public policy /." Diss., Digital Dissertations Database. Restricted to UC campuses, 2009. http://uclibs.org/PID/11984.
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Mattikalli, Nandish M. "An integrated GIS for monitoring land cover dynamics, water quantity and quality in the River Glen catchment, U.K." Thesis, University of Cambridge, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.261553.
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Toukiloglou, Pericles. "Comparison of AVHRR, MODIS and VEGETATION for land cover mapping and drought monitoring at 1 km spatial resolution." Thesis, Cranfield University, 2007. http://dspace.lib.cranfield.ac.uk/handle/1826/2903.
Повний текст джерелаАнотація:
Low spatial resolution remote sensors are one of the best data sources for large area land cover mapping and drought monitoring. This study was concerned with identifying which of the three most operational such sensors (AVHRR, MODIS, and VEGETATION), were likely to help produce the best results within the mentioned applications. A rigorous review of the sensors’ characteristics led to the hypothesis that in land cover mapping and drought monitoring applications MODIS is most likely to achieve the best results followed by VEGETATION and lastly by AVHRR. This hypothesis was tested against experimental results generated within this study. A methodology was developed allowing for unbiased relative comparison of the capacity of the sensors’ Solar Reflective Bands (SRBs) to map land cover, and was applied to data collected over the UK and Greece, for which maps were produced using data collected by each sensor over the same dates and sites, and accuracy estimated using reference data. In the majority of cases the most accurate maps were produced by MODIS data; however, there were cases when maps produced by AVHRR and particularly VEGETATION data were more accurate. Drought monitoring methodologies for low resolution data require historical Normalised Difference Vegetation Index (NDVI) records extending longer than MODIS and VEGETATION operational times. Towards solving this limitation, the relationships between the sensors’ NDVI measurements over the same targets were investigated. It was found that NDVI data for one sensor could be predicted from NDVI data collected by another sensor with considerable accuracy. Consequently, MODIS and VEGETATION historical NDVI records could be extended based on past AVHRR data, and applications could be benefited by interchanging sensors for provision of NDVI data in the event of a sensor failure. These extended datasets were used to assess drought conditions over Ethiopia with the aim of using the Vegetation Productivity Indicator (VPI) methodology. The sensors’ NDVI data responsiveness to rainfall was assessed, finding MODIS NDVI data to best reflect rainfall conditions, and likely to produce more accurate VPI results. Overall the experimental results generated in this study supported the initial hypothesis.
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Huang, Shengli. "The potential of multi-sensor satellite data for applications in environmental monitoring with special emphasis on land cover mapping, desertification monitoring and fire detection." Diss., lmu, 2005. http://nbn-resolving.de/urn:nbn:de:bvb:19-34836.
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Wegner, Maus Victor, Gilberto Camara, Marius Appel, and Edzer Pebesma. "dtwSat: Time-Weighted Dynamic Time Warping for Satellite Image Time Series Analysis in R." Foundation for Open Access Statistics, 2019. http://epub.wu.ac.at/6808/1/v88i05.pdf.
Повний текст джерелаАнотація:
The opening of large archives of satellite data such as LANDSAT, MODIS and the SENTINELs has given researchers unprecedented access to data, allowing them to better quantify and understand local and global land change. The need to analyze such large data sets has led to the development of automated and semi-automated methods for satellite image time series analysis. However, few of the proposed methods for remote sensing time series analysis are available as open source software. In this paper we present the R package dtwSat. This package provides an implementation of the time-weighted dynamic time warping method for land cover mapping using sequence of multi-band satellite images. Methods based on dynamic time warping are flexible to handle irregular sampling and out-of-phase time series, and they have achieved significant results in time series analysis. Package dtwSat is available from the Comprehensive R Archive Network (CRAN) and contributes to making methods for satellite time series analysis available to a larger audience. The package supports the full cycle of land cover classification using image time series, ranging from selecting temporal patterns to visualizing and assessing the results.
18
Vos, Elizmay. "Land cover change and its effect on landscape function in the Koue Bokkeveld." Thesis, Stellenbosch : Stellenbosch University, 2014. http://hdl.handle.net/10019.1/95941.
Повний текст джерелаАнотація:
Thesis (MSc)--Stellenbosch University, 2014.ENGLISH ABSTRACT: Natural vegetation and wetlands in regions of the Western Cape are being replaced by agricultural development. Previous studies on one such region - the Koue Bokkeveld, a high mountainous region at the source of three major drainage basins, demarcated for study purposes to cover nearly 2500 km2, have failed to provide a comprehensive overview of such change and its landscape outcomes. This research aimed to detect, capture, record and classify the spatial nature, extent and change dynamics of various landscape elements and functions due to change in the land cover of this region. It assembled a comprehensive spatial database by digitising existing maps, aerial photograph mosaics and satellite imagery. Land cover maps were created for three historical and land cover change analyses were performed for the interim periods. Morphological landscape images were derived from a DEM and used to explain and interpret the location of land cover types and trends in change patterns. The effects on three landscape functions were determined, namely modelled run-off production, biodiversity deduced from landscape pattern structure and SANBI ratings, and carbon storage potential based on published figures. The research found that the regional landscape has undergone substantive land cover change , since the reference state. Increased intensity and productivity of agriculture and its related infrastructure increased its coverage to nearly 45 000 ha or 20 % of the total area. Perennial agriculture (nearly 10 000 ha of orchards and plantations) and annual (intensive vegetable growing and diminishing cereal crops) agriculture, accompanied by improved enabling infrastructure, such as irrigation technology (large storage dams, pipelines, micro delivery modes), transportation, roads, product cooling and packing plants, have extensively replaced natural vegetation. Located in valley bottoms and along toe-slope locations, where they compete for space directly with expanding and constantly intensifying agricultural activities, wetlands, shale fynbos and renosterveld face complete replacement. Land use and land cover changes have profoundly affected landscape functioning. Modelled rainfall run-off has increased (2% overall) in most subregions, opening possibilities for sedimentation and erosion. A high degree of fragmentation of the vulnerable and affected vegetation types threatens biodiversity. Increased carbon storage in perennial agriculture offers a benefit of change, as opposed to the negative outcomes on biodiversity of change in the Koue Bokkeveld. The research recommends improved institutional provision of the data required for system and regional modelling of processes like run-off in developing communities and for meeting the requirements of more sophisticated and accurate landscape models. Improved availability and appropriateness of GIT software solutions to conduct regional research and the use of more economical open software for GIS applications are to be encouraged. Ongoing and improved management and control are advocated for the expanding and intensified agriculture in a sensitive fynbos setting and for the maintenance of healthy landscape functioning. Concerning the discipline of geography, the exploitation of landscape functioning as a transdisciplinary focus inherent to a new regional geography is encouraged to arrest disciplinary drift. Specifically, future research should intensify the examination of the linkages between land use, land cover, change and ecological landscape functioning.
AFRIKAANSE OPSOMMING: Natuurlike plantegroei en vleilande in substreke van die Wes-Kaap word voortdurend vervang deur landbou-ontwikkeling. Vorige studies oor een so 'n streek - die Koue Bokkeveld, 'n hoogliggende bergagtige streek op die oorsprong van drie groot dreineerbekkens, wat afgebaken vir studie-doeleindes byna 2500 km2 dek bied geen omvattende oorsig van sodanige verandering en die landskapsuitkomste daarvan nie. Hierdie navorsing was daarop gemik om die ruimtelike aard, omvang en veranderingsdinamika van verskillende landskapselemente en -funksies as gevolg van die verandering van grondbedekking van hierdie streek na te spoor, op te teken en te klassifiseer. 'n Omvattende ruimtelike databasis is saamgestel uit versyferde bestaande kaarte, lugfoto-mosaïeke en satellietbeelde. Grondbedekkingskaarte vir drie historiese tydsnitte is geskep en verandering oor die tussentydse periode is ontleed. Morfologiese landskapselemente is uit 'n DEM onttrek en gebruik om die patroon van grondbedekking en tendense in veranderingspatrone te verklaar en te verduidelik. Die nagevolge op drie landskapsfunksies, naamlik reënval-afloop produksie, biodiversiteit soos afgelei van die landskap se patroonstruktuur en SANBI graderings, en koolstof-opgaringspotensiaal gebaseer op gepubliseerde syfers, is bepaal. Die navorsing het bevind dat die streekslandskap sedert die vroegste verwysingstaat wesenlike grondbedekkingsverandering ondergaan het. Verhoogde intensiteit en produktiwiteit van die landbou en sy verwante infrastruktuur het die dekking daarvan vergroot tot byna 45 000 ha of 20% van die totale oppervlakte. Permanente (nagenoeg 10 000 ha boorde en plantasies) en jaarlikse (intensiewe groente en dalende graangewas verbouing) landbou, saam met verbeterde infrastruktuur, soos besproeiingstegnologie (groot opgaardamme, pypleidings, mikroleweringstoerusting), vervoer, paaie, produkverkoeling en -verpakkingsgeriewe, het natuurlike plantegroei grootskaals vervang. Geleë in valleivloere en aanliggende heuwelhange, waar hulle direk om ruimte meeding met die uitbreidende en voortdurend intensiferende landbou-aktiwiteite, is vleilande, skalie-fynbos en renosterveld onderhewig aan volledige vervanging. Grondgebruik- en bedekkingsverandering raak landskapsfunksionering wesenlik. Gemodelleerde reënvalafloop het toegeneem (2% in totaal) in die meeste substreke en vergroot so die moontlikhede vir sedimentasie en erosie. ‘n Hoë vlak van landskapsfragmentasie in die kwesbare en geaffekteerde plantegroeitipes bedreig biodiversiteit. Verhoogde koolstofopgaring in meerjarige landbougewasse impliseer wel 'n positiewe opbrengs van grondbedekkingsverandering, in teenstelling tot die negatiewe biodiversiteitsuitkomste van verandering in die Koue Bokkeveld. Die navorsing beveel aan dat institusionele voorsiening van data wat benodig word vir stelsel- en prosesmodellering (bv. reënvalafloop) in ontwikkelende gemeenskappe, en die vereistes van meer gesofistikeerde en akkurate landskapsmodelle, verbeter word. Die beskikbaarheid en geskiktheid van GIT sagteware-oplossings vir streeksnavorsing, en die gebruik van meer ekonomiese oop-programmatuur vir GIS-toepassings, word aangemoedig. Vir praktiese doeleindes word deurlopend-verbeterde bestuur en beheer oor die uitbreiding en intensifering van van landbou in 'n sensitiewe fynbosomgewing en die instandhouding van gesonde landskapsfunksionering bepleit. Met betrekking tot die dissipline van geografie, moedig die navorsing die ontginning van die landskap se funksionering as 'n transdissiplinêre fokus inherent aan 'n nuwe tipe streeksgeografie aan, om dissiplinêre uiteenloping te stuit. Toekomstige navorsing kan spesifiek die ondersoek van die skakels tussen grondgebruik, grondbedekking, verandering en ekologiese landskapsfunksionering verskerp.
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Weidemann, Bonnie Diane. "Monitoring Land-Cover Change in the Las Vegas Valley: A Study of Five Change Detection Methods in an Urban Environment." BYU ScholarsArchive, 2012. https://scholarsarchive.byu.edu/etd/3539.
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Change detection is currently a topic of great interest to theoretic geographic researchers. The necessity to map, monitor, and model land cover change is also important to a variety of applied fields as varied as urban planning and military intelligence. This research compares five algorithms to map urban land cover change in the greater Las Vegas, Nevada metropolitan area. Landsat Thematic Mapper imagery acquired on May 1990 and May 2000 was used as the primary data. The change detection methods yielded simple maps of change vs. no change. These algorithms included image differencing, image ratioing, image regression, vegetation index differencing, and principal components analysis. Each of these techniques accurately identified areas of land cover with moderate levels of accuracy and produced overall change detection accuracy values between 60% and 76% depending on the method. The highest accuracy was obtained by the image ratioing method using the red spectral band (76%). As expected, the determination of change detection thresholds for each technique was critical to the accuracy produced by the algorithm. Moreover, the type of statistic used in optimizing that threshold was also a significant impacting the final accuracy. The approach of using a set of ground points to calibrate the change detection threshold proved to have significant merit.
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Villarreal, Miguel Luis. "Land Use and Disturbance Interactions in Dynamic Arid Systems: Multiscale Remote Sensing Approaches for Monitoring and Analyzing Riparian Vegetation Change." Diss., The University of Arizona, 2009. http://hdl.handle.net/10150/195061.
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Riparian systems are comprised of interacting aquatic and terrestrial elements that contribute distinctively to the natural capital of arid landscapes. Riparian vegetation is a major component of riparian systems, providing the ecosystem services required to support watershed health. The spatial and temporal distributions of riparian vegetation are influenced by hydrologic and disturbance processes operating at scales from local to regional. I believe both these processes are well suited to monitoring using synoptic and multitemporal approaches.The research in this dissertation is presented as 3 related studies. The first study focused on historical riparian dynamics related to natural disturbance and land use. Using current and historical riparian vegetation maps, we examined vegetation change within catchments of varying land use intensity. Results suggest that land use activities and wastewater subsidy affect the rate of development and diversity of riparian community typesThe second study used moderate resolution satellite imagery to monitor changes in riparian structure and pattern within a land cover change framework. We classified Landsat Thematic Mapper satellite imagery of the Upper Santa Cruz River watershed using Classification and Regression Tree (CART) models. We tested the ability of our models to capture change at landscape, floodplain, and catchment scales, centering our change detection efforts on a riparian tree die-off episode and found they can be used to describe both general landscape dynamics and disturbance-related riparian change.The third study examined historical and environmental factors contributing to spatial patterns of vegetation following two riparian tree die-offs. We used high resolution aerial imagery to map locations of individual live and dead trees and collected a suite of environmental variables and historical variables related directly and indirectly to land use and disturbance history. We tested for differences between groups of live and dead trees using Multi-response Permutation Procedures and found strong relationships between historical factors and mortality incidence.The results from these studies demonstrate the importance of examining historical information and spatial linkages across scales when monitoring riparian vegetation. From a land management perspective, the results identify the need for landscape-level, ecosystem-based management programs to maintain functioning and spatially connected riparian systems.
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James, Tosin. "Changes in Land Use Land Cover (LULC), Surface Water Quality and Modelling Surface Discharge in Beaver Creek Watershed, Northeast Tennessee and Southwest Virginia." Digital Commons @ East Tennessee State University, 2020. https://dc.etsu.edu/etd/3747.
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Beaver Creek is an impaired streams that is not supporting its designated use for recreation due to Escherichia coli (E.coli), and sediment. To address this problem, this thesis was divided into two studies.
The first study explored changes in Land Use Land Cover (LULC), and its impact on surface water quality. Changes in E.coli load between 1997-2001 and 2014-2018 were analyzed. Also, Landsat data of 2001, and 2018 were examined in Terrset 18.31. Mann-Whitney test only showed a significant reduction in E.coli for one site. Negative correlation was established between E.coli load, and Developed LULC, Forest LULC, and Cultivated LULC.
The second study modelled discharge for Beaver Creek watershed using HEC-HMS. This study simulated discharge in an upstream sub-watershed of Beaver Creek, and the full Beaver Creek with a Nash-Sutcliffe of 0.007, and R2 0.20. Sub-basins with high discharge were identified for further examination for possible high sediment load.
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Leeper, Ronnie. "Near-surface Atmospheric Response to Simulated Changes in Land-cover Vegetation Fraction, and Soil Moisture over Western Kentucky." TopSCHOLAR®, 2009. http://digitalcommons.wku.edu/theses/108.
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A series of land-use-land-cover-change (LULCC) based sensitivity experiments, including changes in vegetation type, fractional vegetation (FV), and soil moisture (SM), over Western Kentucky were conducted to investigate atmospheric response to land-use. The choice of land-use for this study was chosen in the context of Western Kentucky’s historical LULCC. For this study, vegetation types considered were grassland, forest, and bare soil with further variations in FV for grassland and forest at 25, 50, 75, and 100 % and systematic increases and decreases in volumetric SM of 0.05, 0.10, and 0.15 m3 m-3. To the author’s knowledge, this is the first assessment of its kind that incorporates these types of LULCC in a single study. In addition, typical anthropogenic land-use change often incorporates several types of LULCC. Moreover, this assessment provides a robust analysis of the impacts LULCC has on atmospheric processes over Western Kentucky.
To simulate the importance of land-use on atmospheric processes, a well known meso-scale model developed by the National Center for Atmospheric Research (NCAR) and the Pennsylvania State University (PSU) MM5 coupled with an intermediately complex land surface model (LSM) Noah was used. The purpose of this research is to investigate the impact of multiple types of LULCC on planetary boundary layer (PBL) evolution, PBL stability, near surface 3D-wind fields, temperature, and moisture. Furthermore, it is anticipated that multiple types of LULCC will provide more insight into the complex nonlinear land-atmosphere interactions from atmospheric, air quality, and climatology perspectives.
Modeling analysis revealed the importance of land-use on atmospheric processes. Changes in all three types of LULCC (land-cover, FV, and SM) altered the distribution of surface energy and moisture, PBL structure, 3D-wind fields, and PBL stability. In general, it was found that LULCC that enhanced (diminished) ET rates reduced (increased) sensible heat flux, atmospheric temperature and, and PBL heights below (above) control (CTRL). For instance, the conversion of land-cover from CTRL to grassland reduced 2 m temperature and PBL heights by 0.60 °C and 228 m respectively compared to CTRL due to an evaporative advantage (lower stomata resistance). Multiple types of land-use change were found to either offset or enhance overall modeled response to LULCC. A reduction in FV to 25 % over grassland diminished ET despite the evaporation advantage of grassland and increased 2 m temperature and PBL heights with respect to CTRL by 3.3 °C and 504 m. These results significantly altered horizontal and vertical wind fields, affecting moisture advection and the development of meso-scale circulations. Compared to CTRL, these differences were enhanced over drier soils, but muted over moist soils. Moreover, the impact of LULCC on atmosphere evolution was not only dependent on the type of LULCC, but also on the current state of other unaltered land surface features such as vegetation type, FV, and SM.
Alterations to modeled PBL development, as a result of LULCC, can have important impacts on a region’s climatology and air quality. Simulated changes in typical PBL moisture and temperature through time can affect local and regional climatology. Depending on the type of LULCC, these alterations in climate may lead to localized cooling. In addition, it was further hypothesized that changes in PBL height can affect air quality. Given the capping inversion layer at the top of the PBL, changes in PBL heights can significantly affect air quality with lower (higher) PBL heights diminishing (enhancing) air quality. Moreover, this research prescribes the importance of considering LULCC in atmospheric assessments of climatology and air quality, including pollutant dispersion and trajectory modeling.
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Mlotha, McArd Joseph. "Analysis of Land Use/Land Cover Change Impacts Upon Ecosystem Services in Montane Tropical Forest of Rwanda: Forest Carbon Assessment and REDD+ Preparedness." Antioch University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=antioch1527773591460797.
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Qi, Zhiming. "Monitoring and modeling of subsurface drainage and nitrate leaching under various land covers." [Ames, Iowa : Iowa State University], 2009. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3389138.
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Abdalla, Jacob Dzarma. "The evaluation of satellite imagery in a geographic information system (GIS) for monitoring land cover changes in a semi-arid area of north-eastern Nigeria." Thesis, Cranfield University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.410441.
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Mahmoud, El-Abbas Mustafa Mustafa. "Assessing, monitoring and mapping forest resources in the Blue Nile Region of Sudan using an object-based image analysis approach." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-161511.
Повний текст джерелаАнотація:
Following the hierarchical nature of forest resource management, the present work focuses on the natural forest cover at various abstraction levels of details, i.e. categorical land use/land cover (LU/LC) level and a continuous empirical estimation of local operational level. As no single sensor presently covers absolutely all the requirements of the entire levels of forest resource assessment, multisource imagery (i.e. RapidEye, TERRA ASTER and LANDSAT TM), in addition to other data and knowledge have been examined. To deal with this structure, an object-based image analysis (OBIA) approach has been assessed in the destabilized Blue Nile region of Sudan as a potential solution to gather the required information for future forest planning and decision making. Moreover, the spatial heterogeneity as well as the rapid changes observed in the region motivates the inspection for more efficient, flexible and accurate methods to update the desired information. An OBIA approach has been proposed as an alternative analysis framework that can mitigate the deficiency associated with the pixel-based approach. In this sense, the study examines the most popular pixel-based maximum likelihood classifier, as an example of the behavior of spectral classifier toward respective data and regional specifics. In contrast, the OBIA approach analyzes remotely sensed data by incorporating expert analyst knowledge and complimentary ancillary data in a way that somehow simulates human intelligence for image interpretation based on the real-world representation of the features. As the segment is the basic processing unit, various combinations of segmentation criteria were tested to separate similar spectral values into groups of relatively homogeneous pixels. At the categorical subtraction level, rules were developed and optimum features were extracted for each particular class. Two methods were allocated (i.e. Rule Based (RB) and Nearest Neighbour (NN) Classifier) to assign segmented objects to their corresponding classes. Moreover, the study attempts to answer the questions whether OBIA is inherently more precise at fine spatial resolution than at coarser resolution, and how both pixel-based and OBIA approaches can be compared regarding relative accuracy in function of spatial resolution. As anticipated, this work emphasizes that the OBIA approach is can be proposed as an advanced solution particulary for high resolution imagery, since the accuracies were improved at the different scales applied compare with those of pixel-based approach. Meanwhile, the results achieved by the two approaches are consistently high at a finer RapidEye spatial resolution, and much significantly enhanced with OBIA. Since the change in LU/LC is rapid and the region is heterogeneous as well as the data vary regarding the date of acquisition and data source, this motivated the implementation of post-classification change detection rather than radiometric transformation methods. Based on thematic LU/LC maps, series of optimized algorithms have been developed to depict the dynamics in LU/LC entities. Therefore, detailed change “from-to” information classes as well as changes statistics were produced. Furthermore, the produced change maps were assessed, which reveals that the accuracy of the change maps is consistently high. Aggregated to the community-level, social survey of household data provides a comprehensive perspective additionally to EO data. The predetermined hot spots of degraded and successfully recovered areas were investigated. Thus, the study utilized a well-designed questionnaire to address the factors affecting land-cover dynamics and the possible solutions based on local community's perception. At the operational structural forest stand level, the rationale for incorporating these analyses are to offer a semi-automatic OBIA metrics estimates from which forest attribute is acquired through automated segmentation algorithms at the level of delineated tree crowns or clusters of crowns. Correlation and regression analyses were applied to identify the relations between a wide range of spectral and textural metrics and the field derived forest attributes. The acquired results from the OBIA framework reveal strong relationships and precise estimates. Furthermore, the best fitted models were cross-validated with an independent set of field samples, which revealed a high degree of precision. An important question is how the spatial resolution and spectral range used affect the quality of the developed model this was also discussed based on the different sensors examined. To conclude, the study reveals that the OBIA has proven capability as an efficient and accurate approach for gaining knowledge about the land features, whether at the operational forest structural attributes or categorical LU/LC level. Moreover, the methodological framework exhibits a potential solution to attain precise facts and figures about the change dynamics and its driving forcesDa das Waldressourcenmanagement hierarchisch strukturiert ist, beschäftigt sich die vorliegende Arbeit mit der natürlichen Waldbedeckung auf verschiedenen Abstraktionsebenen, das heißt insbesondere mit der Ebene der kategorischen Landnutzung / Landbedeckung (LU/LC) sowie mit der kontinuierlichen empirischen Abschätzung auf lokaler operativer Ebene. Da zurzeit kein Sensor die Anforderungen aller Ebenen der Bewertung von Waldressourcen und von Multisource-Bildmaterialien (d.h. RapidEye, TERRA ASTER und LANDSAT TM) erfüllen kann, wurden zusätzlich andere Formen von Daten und Wissen untersucht und in die Arbeit mit eingebracht. Es wurde eine objekt-basierte Bildanalyse (OBIA) in einer destabilisierten Region des Blauen Nils im Sudan eingesetzt, um nach möglichen Lösungen zu suchen, erforderliche Informationen für die zukünftigen Waldplanung und die Entscheidungsfindung zu sammeln. Außerdem wurden die räumliche Heterogenität, sowie die sehr schnellen Änderungen in der Region untersucht. Dies motiviert nach effizienteren, flexibleren und genaueren Methoden zu suchen, um die gewünschten aktuellen Informationen zu erhalten. Das Konzept von OBIA wurde als Substitution-Analyse-Rahmen vorgeschlagen, um die Mängel vom früheren pixel-basierten Konzept abzumildern. In diesem Sinne untersucht die Studie die beliebtesten Maximum-Likelihood-Klassifikatoren des pixel-basierten Konzeptes als Beispiel für das Verhalten der spektralen Klassifikatoren in dem jeweiligen Datenbereich und der Region. Im Gegensatz dazu analysiert OBIA Fernerkundungsdaten durch den Einbau von Wissen des Analytikers sowie kostenlose Zusatzdaten in einer Art und Weise, die menschliche Intelligenz für die Bildinterpretation als eine reale Darstellung der Funktion simuliert. Als ein Segment einer Basisverarbeitungseinheit wurden verschiedene Kombinationen von Segmentierungskriterien getestet um ähnliche spektrale Werte in Gruppen von relativ homogenen Pixeln zu trennen. An der kategorische Subtraktionsebene wurden Regeln entwickelt und optimale Eigenschaften für jede besondere Klasse extrahiert. Zwei Verfahren (Rule Based (RB) und Nearest Neighbour (NN) Classifier) wurden zugeteilt um die segmentierten Objekte der entsprechenden Klasse zuzuweisen. Außerdem versucht die Studie die Fragen zu beantworten, ob OBIA in feiner räumlicher Auflösung grundsätzlich genauer ist als eine gröbere Auflösung, und wie beide, das pixel-basierte und das OBIA Konzept sich in einer relativen Genauigkeit als eine Funktion der räumlichen Auflösung vergleichen lassen. Diese Arbeit zeigt insbesondere, dass das OBIA Konzept eine fortschrittliche Lösung für die Bildanalyse ist, da die Genauigkeiten - an den verschiedenen Skalen angewandt - im Vergleich mit denen der Pixel-basierten Konzept verbessert wurden. Unterdessen waren die berichteten Ergebnisse der feineren räumlichen Auflösung nicht nur für die beiden Ansätze konsequent hoch, sondern durch das OBIA Konzept deutlich verbessert. Die schnellen Veränderungen und die Heterogenität der Region sowie die unterschiedliche Datenherkunft haben dazu geführt, dass die Umsetzung von Post-Klassifizierungs- Änderungserkennung besser geeignet ist als radiometrische Transformationsmethoden. Basierend auf thematische LU/LC Karten wurden Serien von optimierten Algorithmen entwickelt, um die Dynamik in LU/LC Einheiten darzustellen. Deshalb wurden für Detailänderung "von-bis"-Informationsklassen sowie Veränderungsstatistiken erstellt. Ferner wurden die erzeugten Änderungskarten bewertet, was zeigte, dass die Genauigkeit der Änderungskarten konstant hoch ist. Aggregiert auf die Gemeinde-Ebene bieten Sozialerhebungen der Haushaltsdaten eine umfassende zusätzliche Sichtweise auf die Fernerkundungsdaten. Die vorher festgelegten degradierten und erfolgreich wiederhergestellten Hot Spots wurden untersucht. Die Studie verwendet einen gut gestalteten Fragebogen um Faktoren die die Dynamik der Änderung der Landbedeckung und mögliche Lösungen, die auf der Wahrnehmung der Gemeinden basieren, anzusprechen. Auf der Ebene des operativen strukturellen Waldbestandes wird die Begründung für die Einbeziehung dieser Analysen angegeben um semi-automatische OBIA Metriken zu schätzen, die aus dem Wald-Attribut durch automatisierte Segmentierungsalgorithmen in den Baumkronen abgegrenzt oder Cluster von Kronen Ebenen erworben wird. Korrelations- und Regressionsanalysen wurden angewandt, um die Beziehungen zwischen einer Vielzahl von spektralen und strukturellen Metriken und den aus den Untersuchungsgebieten abgeleiteten Waldattributen zu identifizieren. Die Ergebnisse des OBIA Rahmens zeigen starke Beziehungen und präzise Schätzungen. Die besten Modelle waren mit einem unabhängigen Satz von kreuz-validierten Feldproben ausgestattet, welche hohe Genauigkeiten ergaben. Eine wichtige Frage ist, wie die räumliche Auflösung und die verwendete Bandbreite die Qualität der entwickelten Modelle auch auf der Grundlage der verschiedenen untersuchten Sensoren beeinflussen. Schließlich zeigt die Studie, dass OBIA in der Lage ist, als ein effizienter und genauer Ansatz Kenntnisse über die Landfunktionen zu erlangen, sei es bei operativen Attributen der Waldstruktur oder auch auf der kategorischen LU/LC Ebene. Außerdem zeigt der methodischen Rahmen eine mögliche Lösung um präzise Fakten und Zahlen über die Veränderungsdynamik und ihre Antriebskräfte zu ermitteln
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Shipigina, Ekaterina. "Remote sensing methods for environmental monitoring of human impact on sub-Arctic ecosystems in Europe." Thesis, University of Cambridge, 2013. https://www.repository.cam.ac.uk/handle/1810/268066.
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The role and scale of human impact on the global environment is a question of special importance to the scientific community and the world as a whole. This impact has dramatically increased since the beginning of industrialisation, yet its understanding remains patchy. The sub-Arctic plays a central role in forming the global environment due to the vast territory of boreal forest and tundra. Severe climatic conditions make its ecosystems highly sensitive to any natural and human disturbances. In this context, the dynamics of boreal vegetation, and of the forest/tundra interface (the treeline), is the most representative indicator of environmental changes in the sub-Arctic. For some time now, monitoring land cover and vegetation changes using remote sensing techniques have been a powerful method for studying human impact on environment from landscape to global scales. It is particularly efficient when applied to the sub-Arctic ecosystems. Remote sensing gives access to accurate and specific information about distant and hard-to-reach areas across forest and tundra. Despite all the e orts, there is a lack of uniformity in studying human impact, a shortage of mapping of impact over large territories and a lack of understanding of the relation between human activity and environmental response. This dissertation develops a systematic approach to monitoring land cover and vegetation changes under human impact over northern Fennoscandia. The study area extends north and south of the treeline and covers around 400,000km2 reaching from Finnmark in Norway, through Norrbotten in Sweden, Lapland in Finland up to the Murmansk region in Russia. This is the most populated and industrially developed region of the whole sub-Arctic and, therefore, suffering most from human impact. This dissertation identifies industrial atmospheric pollution, reindeer herding, forest logging, forest fires and infrastructure development as the primary types of human impact close to the treeline. For each type characteristic hotspots are identified and human impact is analysed in the context of physical environment as well as cultural, economical and political development of the area. This dissertation presents an automated workflow enabling large-scale land cover mapping in northern Fennoscandia with high throughput. It starts with automated image pre-processing using image metadata and ends with automated mapping of classification results. A single classifier for multispectral Landsat data is trained on extensive field data collected across the whole region. Open source tools are used extensively to set up the processing scripts enabling rapid and reproducible analysis. Using the developed advanced remote sensing methodology land cover maps are constructed for all identified hotspots and types of human impact. Changes in vegetation are analysed using three or four historical land cover maps for each hotspot. More than 35 Landsat TM and ETM+ images covering the period from the 1980s until 2011 are processed in an automated manner. A strong correlation between the level of impact and the scale of vegetation change is confirmed and analysed. The structure and dynamics of the local treeline and the quality of environment are analysed and assessed in the context of changing levels of impact at each hotspot and regionally.
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Rocha, Genival Fernandes. "Trajetória e contexto espacial dos desmatamentos no bioma cerrado." Universidade Federal de Goiás, 2014. http://repositorio.bc.ufg.br/tede/handle/tede/4986.
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The Cerrado biome, occupying approximately 25% of the Brazilian territory, is distinguished for its rich biodiversity and for being a major water producer for the the most important South American basins (e.g. Amazonian, São Francisco, Tocantins-Araguaia and Paraná). With vast flat areas, it is a major industrial food producer and the main agricultural frontier in the country. On the other hand, the large-scale land conversion made the Cerrado one of the 34 biodiversity hotspots in the world, i.e. regions under severe environmental pression due to the reduction and fragmentation of the natural landscapes. The remote sensing monitoring of the Cerrado is very recent, with only two systems currently in operation: the warning deforestation system (SIAD Cerrado), developed by the Image Processing and GIS Lab of the Federal University of Goiás (LAPIG – UFG) and the Satellite Deforestation Monitoring of the Brazilian Biomes Project (PMDBBS), an initiative of the Brazilian Institute of the Environment and Renewable Natural Resources (MMA – IBAMA). Specifically, this doctoral thesis pursued the following goals: 1) comparative evaluation of these two monitoring initiatives; 2) analysis of ten years of SIAD deforestation data, the only Cerrado deforestation time-series available; 3) assessment of possible alternatives aiming at the improvement of SIAD-like semi-automated monitoring systems. Our results demonstrate that both the SIAD and PMDBBS deforestation spatial distribution patterns are very similar and complementary. The analysis of SIAD data for the 2002 – 2012 period indicates that the remnant decreased from 61.2% to 58.5% relatively to the biome area (~ 2 million km2), with about 80% of the total cleared area constrained to terrains with mild slopes (< 3%) and concentrated in only 100 of the 1,384 municipalities. It is worth mentioning that the states with highest deforestation rates are also the ones with the largest native vegetation remnants, suggesting that key agricultural frontiers are still very active. Potential improvements for making systems like SIAD more robust and reliable include the use of time-series and spatial heuristics criteria for filtering the automated generated deforestation warnings, which demand very time-consuming visual inspection approaches. We estimate that about 506,906 km2 of remnant vegetative cover are highly vulnerable and under risk of being converted over the years. Thus, improving the current systems and monitoring models, in order to make them more reliable and capable of operating at multiple scales of observation, is fundamental for the effective territorial governance of the Cerrado biome.
O bioma Cerrado ocupa aproximadamente 25% do território nacional e se destaca por sua rica biodiversidade e como “produtor de água” para as bacias Amazônica, do São Francisco, do Tocantins-Araguaia e do Paraná. Com vastas áreas de terras predominantemente planas e de fácil manejo, destaca-se também pela produção de alimentos em escala industrial, sendo hoje a principal fronteira agropecuária do país. Por outro lado, e com aproximadamente 50% do seu território convertido em áreas de agricultura e pecuária, é também reconhecido como um dos 34 hotspots de biodiversidade, i.e. regiões do mundo severamente comprometidas do ponto de vista ambiental, por conta da redução e fragmentação das paisagens naturais. O monitoramento por sensoriamento remoto do bioma Cerrado é recente, sendo que até o momento há dois sistemas em operação: o Sistema Integrado de Alerta de Desmatamentos (SIAD Cerrado), desenvolvido pelo Laboratório de Processamento de Imagens e Geoprocessamento da Universidade Federal de Goiás (LAPIG – UFG) e o Projeto de Monitoramento do Desmatamento dos Biomas Brasileiros por Satélite (PMDBBS), uma iniciativa do Instituto Brasileiro do Meio Ambiente e dos Recursos Naturais Renováveis (MMA – IBAMA). Especificamente, esta tese buscou: 1) avaliar, comparativamente estas duas iniciativas; 2) analisar 10 anos de dados de desmatamento SIAD, única série temporal sobre desmatamentos no bioma Cerrado existente e disponível e 3) avaliar alternativas e melhorias possíveis, com vistas ao aprimoramento de sistemas de monitoramento semi-automatizados, a exemplo do SIAD. Os nossos resultados demonstram que os padrões de distribuição espacial dos desmatamentos detectados pelo SIAD e PMDBBS são muito semelhantes e complementares. Por sua vez, a análise de dados SIAD para o período entre 2002 e 2012, indica que a vegetação remanescente ao longo dos 2 milhões de km²do bioma diminuiu de 61,2% para 58,5%, sendo que aproximadamente 80% da área total desmatada, condicionada principalmente pelo relevo (i.e. declividades < 3%), concentrou-se em apenas 100 dos 1.384 municípios do Cerrado. Interessante observar que os estados com as maiores áreas de remanescentes de vegetação natural de Cerrado são,coincidentemente, os que mais desmataram nos últimos dez anos, o que demonstra a existência de fronteiras agropecuárias ativas. Possíveis melhorias para tornar sistemas aexemplo do SIAD mais robustos e confiáveis incluem o uso de séries temporais e associações heurísticas como critérios de filtragem de alertas de desmatamentos gerados automaticamente e sujeitos amorosos processos de inspeção visual. Estimamos que aproximadamente 506.906 km2 da cobertura vegetal remanescente encontram-se vulneráveis e sob risco de serem convertidas ao longo dos próximos anos. Assim, o aprimoramento dos atuais sistemas e modelos de monitoramento, com vistas atorná-los cada vez mais robustos, confiáveis e capazes de operar em múltiplas escalas de observação, é imprescindível à efetiva governança territorial e ambiental do bioma Cerrado.
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Marambe, Kodippili Arachchilage Yahampath Anuruddha Marambe. "Monitoring Crop Evapotranspiration in the Western Lake Erie Basin Using Optical Sensors." Bowling Green State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1535362877977252.
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Lam, Chi-Nguyen. "Méthodes de Machine Learning pour le suivi de l'occupation du sol des deltas du Viêt-Nam." Thesis, Brest, 2021. http://www.theses.fr/2021BRES0074.
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Le développement socio-économique au Vietnam est associé à l'existence des grands deltas fluviaux. En outre, les facteurs environnementaux tels que la sécheresse et l’inondation jouent un rôle important dans le changement de l’occupation du sol au niveau de ces deltas. Ces changements ne sont pas sans impact sur l’équilibre naturel et économique du pays. Dans cette optique, cette thèse a pour objectifs de proposer des méthodes de traitement des données satellites pour une cartographie et suivi efficaces de l’occupation du sol au niveau des deux principaux deltas du Viêt- Nam, fleuve rouge et du Mékong. En effet, un travail expérimental a été effectué en vérifiant et évaluant l’apport du traitement d’images multi-capteurs par de nombreuses approches de segmentation d’image et d’apprentissage automatique peu profond et profond. Ainsi, un modèle de Convolutional Neural Network (CNN) adapté au contexte de l’étude, a démontré sa robustesse pour la détection et la cartographie de l’occupation du sol afin de caractériser l’aléa de l’inondation et d’analyser les enjeuxSocio-economic development in Vietnam is associated with the existence of large fluvial deltas. Furthermore, environmental factors such as dryness and flooding have an important role in the change of land use/land cover within these deltas. These changes have an impact on the natural and economic balance of the country. In this perspective, the objectives of the present thesis are to suggest processing methods of satellite data for an efficient mapping and monitoring of land use in the two main deltas of Vietnam, the Red River and the Mekong Delta. Indeed, experimental work has been carried out by verifying and evaluating the contribution of multi-sensor image processing through various image segmentation approaches and machine/deep learning algorithms. Thus, a Convolutional Neural Network (CNN) model adapted to the context of the study demonstrated its robustness for the detection and mapping of land use in order to characterise the flood hazard and analyse the issues at risk
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Maupin, Travis Pritchard. "ASSESSMENT OF CONDUCTIVITY SENSORS PERFORMANCE FOR MONITORING MINED LAND DISCHARGED WATERS AND AN EVALUATION OF THE HYDROLOGIC PERFORMANCE OF THE GUY COVE STREAM RESTORATION PROJECT." UKnowledge, 2012. http://uknowledge.uky.edu/bae_etds/6.
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The surface mining method of mountaintop removal has been shown to adversely affect the water quality and hydrologic characteristics of downstream regions. Based on recent scientific literature, the U.S. EPA issued guidance on the specific conductivity (EC25ºC) of waters discharged from mined lands in the Appalachian Coal Belt Region stating that these waters should have an EC25ºC less than 300-500 μS cm-1. Hence, accurately measuring EC25ºC levels of mine discharged waters has significant implications. Furthermore, the development of reclamation techniques that positively impact the hydrological and water quality aspects of valley fill (VF) discharge is needed. To tackle these questions, a two-part study was conducted. First, a detailed study comparing sensor performance under controlled and field conditions was performed. Second, the hydrologic parameters (storm flow only) of a stream restoration project constructed atop a retrofitted valley fill were compared to a headwater stream with no mining as well as one influenced by mining with no restoration. Results indicated that significant differences were noted between four conductivity sensors with errors positively correlated with increases in EC25ºC. For storm events, the restored stream section atop the VF is performing similar to the unminded, forested watershed for some hydrologic parameters.
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Hannerz, Fredrik. "Making water information relevant on local to global scale : the role of information systems for integrated water management /." Doctoral thesis, Stockholm : Department of Physical Geography and Quaternary Geology, Stockholm university, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-7431.
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Karrasch, Pierre, Daniel Henzen, Sebastian Hunger, and Max Hörold. "Determination of water body structures for small rivers using remote sensing data." SPIE, 2015. https://tud.qucosa.de/id/qucosa%3A35055.
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The diversity of habitats in water bodies like rivers is characterised by the status of morphological and hydrological conditions. The good ecological status of water bodies is claimed in the EuropeanWater Framework Directive. For the assessment of this status the hydro-morphology is one of the most important supporting components for the classification of the ecological status of water bodies. Therefore the periodical monitoring is a mandatory measure in the scope of the European Water Framework Directive. Regarding the so called overview-method of the LAWA (German Working Group on water issues of the Federal States and the Federal Government represented by the Federal Environment Ministry) the use of remote sensing data and remote sensing methodologies becomes more important. Therefore remote sensing data on different scales (satellite, aerial photographs) as well as other topographic information (ATKIS) and a high resolution DTM are merged into an integrative process of analysis using remote sensing and GIS methodology. The analyses ared focused on two parameters. First, a detailed landuse classification based on LANDSAT satellite data is performed for whole catchment of a small river. The results show significant increase of urban areas close to the river. The second analyses deals with the determination of river curvature and introduces the use of a quasi-continously representation of the river. An additional challenge is the chosen study area of a low mountain range river. While large rivers are clear visible in remote sensing data, the usability and transformation of the well-established algorithms and work ows to small rivers need a further substantial research.
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Comber, Alexis. "Automated land cover change detection." Thesis, University of Aberdeen, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.248628.
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This thesis describes a generic approach for automated land cover monitoring. Knowledge about land cover is acquired through a knowledge acquisition exercise and used to augment image analysis in order to determine land cover change direction. It is demonstrated that taking a task-oriented approach to the change problem avoids the specificity of more traditional data-oriented approaches. The approach described here involves four key analyses that have contributed to the overall problem solution: Identifying the knowledge used to determine different land cover elements (elicitation and modelling); Land cover remote sensing characteristics; Land cover bio-geographic characteristics; Investigation of the most suitable approach for combining evidence. The results of these investigations were applied to semi-natural change problems: evidence about areas known to have changed was reasoned with and change directions identified by applying the knowledge and interpreter rules of thumb. The results were compared with field surveys of the change areas, and were shown to have correctly identified the land cover change direction in each case.
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Leach, Nicholas Persak. "Hydrologic response of land use and land cover changes." Thesis, University of Iowa, 2015. https://ir.uiowa.edu/etd/1870.
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Throughout the years, land use and land cover (LULC) changes have directly impacted the water cycle in Iowa. To better understand the hydrological response to LULC change, the Hydrological Simulation Program - FORTRAN (HSPF) model will be used to qualitatively evaluate the effects of different LULC’s and quantify the associated adjustments to model parameters. Anecdotal and observational evidence of the hydrologic response will be employed to define how model parameters should be adjusted to represent certain LULC changes. The hydrologic assessment will be of the Turkey River Watershed located in northeast Iowa, which covers approximately 1,693 square miles.
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Maluki, Peter Masavi. "MAPPING LAND COVER LAND USE CHANGE IN MBEERE DISTRICT, KENYA." Miami University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=miami1187030316.
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Lawrence, Peter. "Climate impacts of Australian land cover change /." [St. Lucia, Qld.], 2004. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe18055.pdf.
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Makido, Yasuyo Kato. "Land cover mapping at sub-pixel scales." Diss., Connect to online resource - MSU authorized users, 2006.
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Rosa, Isabel Maria Duarte. "Modelling land cover change in tropical rainforests." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/40148.
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Tropical deforestation is one of the most important drivers of biodiversity loss and carbon emissions. This thesis seeks to analyse the dynamics of tropical deforestation and develop a probabilistic model that predicts land cover change (LCC) in the tropics. The main findings from the analysis of the Brazilian Amazon deforestation dynamics are that large clearings comprised progressively smaller amounts of total annual deforestation while the number of smaller clearings remained unchanged over time. These changes were coincident with the implementation of conservation policies by the government. The review of LCC models presented here showed that this modelling community would benefit from improving: the openness to share model inputs, code and outputs; model validations; and standardised frameworks to be used for model comparisons. The modelling framework developed aimed to tackle the limitations found before and two scenarios of deforestation in the Brazilian Amazon were simulated. For both scenarios forest next to roads and areas already deforested were found to be more likely to be deforested. States in the south and east of the region showed high predicted probability of losing nearly all forest outside of protected areas by 2050. The release of carbon to the atmosphere is an important consequence of tropical deforestation. Even if deforestation had ended in 2010 there would still be large quantities of carbon to be released. The amount of carbon released immediately is higher than the one committed for future release in the first few years of analysis, but presently these accounted for at least two-thirds of total carbon emissions. Finally, the drivers of LCC were found to vary among transition types, but less so through time. The accuracy of the model predictions was heavily dependent on the year calibrated, suggesting that a widespread reliance on single calibration time period may be providing biased predictions of future LCC.
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White, Claire McKenzie. "Analysis and Comparison of a Detailed Land Cover Dataset versus the National Land Cover Dataset (NLCD) in Blacksburg, Virginia." Thesis, Virginia Tech, 2011. http://hdl.handle.net/10919/46322.
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While many studies have completed accuracy assessments on the National Land Cover Dataset (NLCD), little research has utilized a detailed digitized land cover dataset, like that available for the Town of Blacksburg, for this comparison. This study aims to evaluate the information available from a detailed land cover dataset and compare it with the National Land Cover Dataset (NLCD) at a localized scale. More specifically, it utilizes the detailed land cover dataset for the Town of Blacksburg to analyze the land cover distribution for varying land uses including single-family residential, multi-family residential, and non-residential. In addition, an application scenario assigns an area-weighted curve number to watersheds based on each land cover dataset. This study exhibits the importance of obtaining detailed land cover datasets for cities and towns. Furthermore, it shows the comprehensive information and subsequent quantifications that can be surmised from a detailed land cover dataset.Master of Science
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Guardiola-Claramonte, Maria Teresa. "EFFECTS OF LAND USE / LAND COVER CHANGE ON THE HYDROLOGICAL PARTITIONING." Diss., The University of Arizona, 2009. http://hdl.handle.net/10150/145730.
Повний текст джерелаАнотація:
Current global population growth and economic development accelerates the land cover conversion in many parts of the world and compromises the natural environment. However, the impacts of this land cover change on the hydrologic cycle at local to regional scales are poorly understood. The thesis presented here investigates the hydrologic implications of land use conversion in two different settings using two different approaches. The first study focuses in Southeast Asia and the expansion of rubber monocultures in a middle-sized basin. Field measurements suggest rubber has distinct dynamics compared to the area's native vegetation, depleting and exhausting the local water balance more than native vegetation. A phenology based evapotranspiration function is developed and used in a hillslope based hydrologic model to predict the implications of rubber expansion at a basin scale. The second study is centered in the semi-arid southwestern United States. This study challenges the traditional assumption that deforestation increases water yield at regional scales. Observations of water yield in basins affected by a regional piñon pine die-off show a decline in water yield during several years after die-off. These results suggest an increase in landscape sensitivity to vegetation disruption in semi-arid ecosystems as scale increases. Consequences of both studies have important implications for land and water managers in these different ecosystems.
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陳章偉 and Cheung-Wai Jonathan Chan. "A neural network approach to land use/land cover change detection." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B31238166.
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Chan, Cheung-Wai Jonathan. "A neural network approach to land use/land cover change detection /." Hong Kong : University of Hong Kong, 1998. http://sunzi.lib.hku.hk/hkuto/record.jsp?B21415067.
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Knorn, Jan. "Studying land-use and land-cover change with high resolution data." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2012. http://dx.doi.org/10.18452/16513.
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Naturschutzgebiete sind ein essentieller Bestandteil zur Wahrung natürlicher Lebensräume. Oft verfehlt die Einrichtung solcher Schutzzonen jedoch den erwarteten Effekt. Die größte Gefahr liegt hierbei neben dem Klimawandel im direkten Einfluss des Menschen. Besonders in Phasen sozioökonomischen Umschwungs und damit verbundenen Landnutzungsveränderungen oder auch illegaler Nutzung natürlicher Ressourcen, sind Naturschutzgebiete in ihrer Funktion gefährdet. Ziel dieser Arbeit ist somit, Ursachen des Landnutzungswandels sowie dessen Auswirkungen und Ausmaß am Beispiel des rumänischen Teils der Karpaten-Ökoregion abzuleiten. Das Untersuchungsgebiet ist ein wichtiges Zentrum für Biodiversität und in ihm befindet sich Osteuropas größte gemäßigte Waldregion sowie einige der letzten europäischen Urwälder. Rumänien umschließt mehr als die Hälfte der Karpaten und es ist hiernach von besonderem Interesse, Gründe und Auswirkungen des rezenten post-sozialistischen Landschaftswandels zu untersuchen. Mit Hilfe von Landsat Aufnahmen sowie einer ad hoc entwickelten Methode zur Klassifizierung großräumiger Gebiete, wurden Veränderungen in der Waldbedeckung für die post-sozialistische Zeit abgeleitet. Die Ergebnisse offenbaren großflächige Forstveränderungen, auch innerhalb von Naturschutzgebieten und Urwäldern. Institutionelle Umbrüche und eine rapide Umgestaltung in den Eigentumsverhältnissen, wurden als Hauptursachen herausgestellt. Rumänische Naturschutzgebiete erreichen nicht die gewünschte Effektivität und Urwäldern werden weiterhin dezimiert. Die Arbeit verdeutlicht den Einfluss sozioökonomischer Veränderungen auf die Entstehung von Raubbau und legt Defizite in der Effektivität von Schutzbemühungen zum Erhalt der Biodiversität und verbundener Ökosystemleistungen offen.Protected areas are one cornerstone of conservation efforts to safeguard natural habitats from destruction and overexploitation. Still, many of these areas remain less effective than initially envisioned. Besides climate change, main threats originate from enduring human activities. Protected areas are particularly at risk during periods of rapid socio-economic changes, which can trigger widespread land-use change and illegal resource use. The main goal of this thesis is to assess the extend and underlying causes of land-use change in protected areas and forest habitats within the Carpathian Ecoregion. The Romanian Carpathians were selected as a focus area in this study, because they comprise Eastern Europe’s largest continuous temperate forest region as well as some of the last and largest tracts of European old-growth forests, and they are a major hotspot of biodiversity. Romania comprises more than half of the Carpathian Ecoregion and it is of particular interest to study the causes and effects of land-use changes, which have emerged after the collapse of socialism in 1989. Post socialist forest cover change was quantified for the last 25 years using Landsat images and an ad hoc developed large area classification technique. Results show widespread forest disturbances, even inside protected areas and old-growth forests. Drivers of these disturbances can be related to institutional change and changes in ownership. The effectiveness of Romania’s protected area network in terms of its ability to safeguard biodiversity is most likely decreasing, and intact old-growth forests continue to disappear. This thesis reveals how rapid socio-economic changes may lead to overexploitation, and highlights substantial shortcomings in the effectiveness of protection efforts to safeguard biodiversity and related ecosystem services.
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Howard, Madeleine A. "Patterns of land cover change in Kanyati communal land in Zimbabwe." Master's thesis, University of Cape Town, 1999. http://hdl.handle.net/11427/19518.
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Bibliography : pages 138-149.Expanding areas under cultivation and settlement are a global trend with significant effects on existing land cover types and ecosystems. High rates of human population growth in southern Africa and subsequent increased pressure on land has led to the extension of cultivation and settlement into marginal lands. This study investigates the spatial patterns ofland cover change in a communal land in Zimbabwe over the period 1973 to 1993, and their likely ecological effects. The study site is in the Zambezi Valley and has a well preserved area ofmiombo woodland and has the potential to become an important wildlife corridor between a national park, safari area and communal lands with local community based wildlife management projects. The area is divided into wildlife and settled areas by a game fence so provided an opportunity to compare patterns ofland cover change vvith and without extensive human impact within the same administrative area. The land cover types were derived from manually interpreted aerial photographs as multispectral satellite imagery is not available before the 1980's and is expensive. Geographical Information Systems were used to analyse the spatial patterns ofland covers identified, the sizes and shapes of spatial entities and the spatial distribution of land cover types in relation to slope and proximity to rivers. The likely ecological effects of land cover change were investigated by deriving habitat suitability maps using the habitat requirements of seven large herbivore species: buffalo, bushbuck, elephant, kudu, sable, waterbuck and zebra.
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Batunacun. "Modelling land use and land cover change on the Mongolian Plateau." Doctoral thesis, Humboldt-Universität zu Berlin, 2020. http://dx.doi.org/10.18452/21796.
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Der Bezirk Xilingol wurde als geeignetes Beispiel ausgewählt, weil es zu einem großen Flächenanteil von Grassteppe bedeckt ist und fast alle Phasen der Umweltpolitik Chinas durchlaufen hat. Es wurden zwei deutlich voneinander abgrenzbare Phasen identifiziert, von 1975 bis 2000 und von 2000 bis 2015. Während der ersten Phase, bis 2000, war Landdegradation der dominante Landnutzungswandelprozess, der 11.4 % der Gesamtfläche betraf. In dieser Phase war die menschliche Einflussnahme der Hauptfaktor in acht Landkreisen, die sich ändernden Wasserverhältnisse war es in sechs Landkreisen. Während der zweiten Phase, ab 2000, setzte ein spürbare Erholung des Zustandes auf 12 % des Gesamtgebietes ein, während die Degradation jedoch weiter voranschritt und zusätzliche 9,5 % des Landes veränderte. Während dieser Phase wurde die Städtebildung zum dominanten Treiber für die Landdegradierung in sieben Landkreisen, während der Einfluss menschlicher Störungen und der Wasserverfügbarkeit wieder zurückging.
Nach der Identifizierung der Haupttreiber für die Landdegradation, wurde die komplexe Beziehung zwischen verschiedenen Treibern und der Grassteppen-Degradation untersucht. Die Ergebnisse zeigten, dass die Beziehung zwischen dicht bedeckter, moderat bedeckter, und spärlich bedeckter Grassteppe und die Dichte des Schafbesatzes für die Degradationsdynamik in der Grassteppe verantwortlich waren.
In dieser Arbeit wurden die Methoden der Clusteranalyse, der Partial-Order-Theorie, und der Hasse Diagramme eingesetzt, um die Haupttreiber der Landdegradation auf Landkreisebene zu identifizieren. Dann wurde ein Ansatz aus dem maschinellen Lernen, XGBoost (eXtreme Gradient Boosting) verwendet, um die Dynamik der Grassteppen-Degradation vorauszusagen. Darüber hinaus wurde SHAP (SHapley Additive exPlanations) eingesetzt, um das von XGBoost erstellte Black-Box-Modell zu in seine Bestanteile zu zerlegen und für jedes Degradations-Pixel in der Karte den Haupttreiber zu extrahieren.The aims of this thesis are to gain an integrated and systematic understanding of the processes and determinants of land degradation on the Mongolian Plateau. Xilingol was chosen as a suitable example, mainly since it is covered by vast grassland, and has experienced almost all ecological policies that have been implemented in China. Two distinct phases were identified in this region: 1975-2000 and 2000-2015. During the first phase (up to 2000), land degradation was the dominant land use change process, accounting for 11.4% of the total area. During this phase, human disturbance was the major driver in eight counties, whereas the water condition was the dominant driver in six counties. During the second phase (post-2000), land restoration increased (12.0% of the total area), whereas degradation continued, resulting in a further 9.5% of degraded land. During this phase, urbanisation became the dominant driver of land degradation in seven counties, while effects resulting from human disturbance and water availability decreased after 2000. After identifying the major drivers of degradation, the complex relationships between drivers and grassland degradation were captured. The results indicated that the distance to dense, moderately dense grass and sparse grass and sheep density were responsible for the grassland degradation dynamics. In this thesis, a clustering method, partial order theory and Hasse diagram techniques were first used to identify the major drivers of land degradation at the county level. Subsequently, an approach from machine learning, XGBoost (eXtreme Gradient Boosting), was used to predict the dynamics of grassland degradation. Moreover, SHAP (SHapley Additive exPlanations) values were used to open up the black box model, and the primary driver was extracted for each pixel showing degradation.
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Saxena, Rishu. "Towards a Polyalgorithm for Land Use and Land Cover Change Detection." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/93177.
Повний текст джерелаАнотація:
Earth observation satellites (EOS) such as Landsat provide image datasets that can be immensely useful in numerous application domains.
One way of analyzing satellite images for land use and land cover change (LULCC) is time series analysis (TSA). Several algorithms for time series analysis have been proposed by various groups in remote sensing; more algorithms (that can be adapted) are available in the general time series literature. However, in spite of an abundance of algorithms, the choice of algorithm to be used for analyzing an image stack is presently an open question. A concurrent issue is the prohibitive size of Landsat datasets, currently of the order of petabytes and growing. This makes them computationally unwieldy --- both in storage and processing. An EOS image stack typically consists of multiple images of a fixed area on the Earth's surface (same latitudes and longitudes)
taken at different time points. Experiments on multicore servers indicate
that carrying out meaningful time series analysis on one such interannual, multitemporal stack with existing state of the art codes can take several days.
This work proposes using multiple algorithms to analyze a given image
stack in a polyalgorithmic framework. A polyalgorithm combines several basic algorithms, each meant to solve the same problem, producing a strategy that unites the strengths and circumvents the weaknesses of constituent algorithms. The foundation of the proposed TSA based polyalgorithm is laid using three algorithms (LandTrendR, EWMACD, and BFAST). These algorithms are precisely described mathematically, and chosen to be fundamentally distinct from each other in design and in the phenomena they capture. Analysis of results representing success, failure, and parameter sensitivity for each algorithm is presented. Scalability issues, important for real simulations, are also discussed, along with scalable implementations, and speedup results. For a given pixel, Hausdorff distance is used to compare the distance between the
change times (breakpoints) obtained from two different algorithms. Timesync validation data, a dataset that is based on human interpretation of Landsat time series in concert with historical aerial photography, is used for validation. The polyalgorithm yields more accurate results than EWMACD and LandTrendR alone, but counterintuitively not better than BFAST alone. This nascent work will be directly useful in land use and land cover change studies, of interest to terrestrial science research, especially regarding anthropogenic impacts on the environment, and in much broader applications such as health monitoring and urban transportation.M. S.
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Martínez, Vázquez Alberto. "Snow cover monitoring techniques with gb-SAR." Doctoral thesis, Universitat Politècnica de Catalunya, 2008. http://hdl.handle.net/10803/6922.
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Los radares de apertura sintética basados en tierra (GB-SAR) son instrumentos que, aun siendo relativamente jóvenes, se utilizan con éxito para monitorizar operacionalmente deslizamientos de tierra. Por otra parte, el manto nivoso se ha estudiado tradicionalmente a nivel local con sensores invasivos o con radares de onda continua. El trabajo presentado en esta tesis contribuye a la conjunción de ambos campos: el uso de sensores GB-SAR para la monitorización del manto nivoso. En los capítulos 5, 6 y 7 se muestran las tres técnicas desarrolladas: cálculo de la altura del manto nivoso, cálculo del volumen de nieve desplazado en una avalancha, y detección y clasificación de avalanchas.La altura del manto nivoso se calcula a partir de la fase interferométrica diferencial de dos adquisiciones SAR consecutivas bajo la hipótesis de nieve seca y asumiendo un modelo de nieve de una capa. El volumen desplazado en una avalancha se obtiene mediante la resta de dos modelos del terreno digitales (DTM) generados justo antes y después del fenómeno avalancha. Los DTMs se obtienen con el modo topográfico del instrumento: dos antenas separadas una cierta distancia reciben simultáneamente los ecos de una tercera antena que transmite. La tercera técnica de monitorización, detección y clasificación de avalanchas, se realiza mediante la magnitud de la coherencia compleja entre dos imágenes SAR consecutivas. Las avalanchas de nieve presentan valores de coherencia muy bajos en el área afectada, que puede ser identificada mediante un modelo basado en características morfológicas y estadísticas. En la presente tesis se presenta un innovador algoritmo para la detección y clasificación de avalanchas.
El GB-SAR del Joint Research Centre, una vez mejorado para permitir su uso continuado 24h al día y 7 días a la semana (capítulo 3), ha sido utilizado para desarrollar y verificar las técnicas mencionadas anteriormente. Se han realizado medidas durante seis inviernos, que han proporcionado más de 120000 imágenes SAR a una frecuencia aproximada de 12 minutos por imagen. Se han estudiado dos emplazamientos, cada uno con características diferentes con el fin de verificar el uso operativo de las técnicas bajo condiciones reales. Los resultados muestran que la obtención de la altura del manto nivoso es posible pero su aplicación está limitada al campo experimental ya que su uso está condicionado por fuertes restricciones. Un estudio de viabilidad posteriormente confirmado con medidas muestra, en cambio, las limitaciones de precisión de los instrumentos GB-SAR para obtener una estima del volumen de nieve desplazado en una avalancha. Finalmente, la técnica más prometedora es la de detección y clasificación de avalanchas, la cuál se demuestra factible y robusta desde un punto de vista operativo y práctica desde una perspectiva de usuario final.
Ground-based synthetic aperture radars (GB-SAR) are instruments that, although relatively young, are operationally used to monitor landslides with sub-millimetre accuracy. There are however other application fields that deserve some attention.
Monitoring of the snow cover and, in particular, of those processes associated with the snow avalanches are a clear example of this. To date, monitoring of the snow cover has been traditionally carried out at local scale either with invasive sensors or with continuous wave (CW) radars, while at wider scales air- or satellite-borne sensors have been employed. The work presented in this PhD thesis is a first attempt to study the potential use of GB-SAR sensors to monitor the snow cover. Three techniques for monitoring snow cover with GB-SAR are presented through Chapters 5, 6 and 7: snow height retrieval, snow avalanche volume retrieval and avalanche events detection and classification.
Snow height is retrieved by using the differential interferometric phase of two consecutive SAR acquisitions under the assumption of single layer model and dry snow conditions. The volume displaced in a snow avalanche is computed by subtracting two digital terrain models (DTM) generated immediately before and after the avalanche event.
DTMs are obtained with the topographic interferometric mode of the instrument: a spatial base line separates two antennas that simultaneously receive the echoes of a third transmitting antenna. The third monitoring technique, avalanche detection and classification, is achieved identifying low-coherence anomalies in the coherence image between two successive SAR acquisitions. Snow avalanches present a low-coherence signature that can be identified by a morphological and statistical parameter model. A novel detection and classification scheme for snow avalanches is proposed.
The Joint Research Centre's GB-SAR sensor, upgraded to allow continuou operation 24 hours a day 7 days a week, also as part of the work of this thesis (Chapter 3), was used to develop and assess the before mentioned techniques. Measurements were carried out during six winters, providing more than 120000 SAR images at a rate of one image every 12 minutes approximately. Two test sites were studied, each with different characteristics in order to assess under real conditions the operational use of the monitoring techniques. Results show that snow height retrieval is achievable but remains, for the moment, an on-going research topic due to strong constraints limiting its use. A feasibility study is presented showing the accuracy limitations of GB-SAR to compute the snow volume involved in an avalanche. Finally, the most promising technique is snow avalanche detection and classification, which is demonstrated to be feasible and robust from an operational point of view and practical from an end user perspective.
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Schehl, Donald J. "Monitoring of thermoplastic pipes under deep cover." Ohio : Ohio University, 2000. http://www.ohiolink.edu/etd/view.cgi?ohiou1172865071.
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Bates-Lanclos, Melissa Marie. "Assessing urban land use/land cover change in Springfield, Missouri 1972-2000 /." free to MU campus, to others for purchase, 2004. http://wwwlib.umi.com/cr/mo/fullcit?p1426046.
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