Relevant bibliographies by topics / Monitoring of Land cover

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Monitoring of Land cover'

Author: Grafiati
Published: 7 September 2022

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Journal articles on the topic "Monitoring of Land cover"

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Guliyeva, S. H. "LAND COVER / LAND USE MONITORING FOR AGRICULTURE FEATURES CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 61–65. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-61-2020.

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Abstract. Remote sensing applications are directed to agricultural observation and monitoring. It has been huge of scientific papers are dedicated to the research of the contribution of remote sensing for agriculture studies. There are several global challenges needed to be considered within agriculture activities. It can be embraced by the main agriculture sector facing the obstacles impacting the production and productivity of the sector. These are the following options that can be pointed out: biomass and yield estimation; vegetation vigor and drought stress monitoring; assessment of crop phenological development; crop acreage estimation and cropland mapping; and mapping of disturbances and Land Use/Land Cover changes. In this study has been undertaken the realization of satellite-based Land Use/Land Cover monitoring based on various optical satellite data. It has been used satellite images taken from satellites AZERSKY, RapidEye, Sentinel-2B and further processed for Land Use/Land Cover classification. Following the complex approach of the supervised and unsupervised classification, the methodology has been used for satellite image processing. As the main satellite imagery for monitoring crop condition were AZERSKY taken during the growing season, from May to June of 2019 year. The study area was some part of the Sheki region, which covers the central part of the southern slope of the Greater Caucasus Mountain Range within Azerbaijan Republic. In this research work satellite imagery processing and mapping has been carried out on the basis of software application of ArcGIS Pro 2.5.
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Stefanov, William L., Michael S. Ramsey, and Philip R. Christensen. "Monitoring urban land cover change." Remote Sensing of Environment 77, no. 2 (August 2001): 173–85. http://dx.doi.org/10.1016/s0034-4257(01)00204-8.

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Skelsey, C., A. N. R. Law, M. Winter†, and J. R. Lishman. "A system for monitoring land cover." International Journal of Remote Sensing 24, no. 23 (January 2003): 4853–69. http://dx.doi.org/10.1080/0143116031000101585.

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Manakos, Ioannis, Garik Gutman, and Chariton Kalaitzidis. "Monitoring Land Cover Change: Towards Sustainability." Land 10, no. 12 (December 9, 2021): 1356. http://dx.doi.org/10.3390/land10121356.

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In 2015, the United Nations member states adopted the 2030 Agenda, within which the 17 Sustainable Development Goals (SDGs) were established, with many of these goals calling for further research into sustainability [...]
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Cieślak, Iwona, Karol Szuniewicz, Katarzyna Pawlewicz, and Szymon Czyża. "Land Use Changes Monitoring with CORINE Land Cover Data." IOP Conference Series: Materials Science and Engineering 245 (October 2017): 052049. http://dx.doi.org/10.1088/1757-899x/245/5/052049.

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Potapov, Peter, Matthew C. Hansen, Indrani Kommareddy, Anil Kommareddy, Svetlana Turubanova, Amy Pickens, Bernard Adusei, Alexandra Tyukavina, and Qing Ying. "Landsat Analysis Ready Data for Global Land Cover and Land Cover Change Mapping." Remote Sensing 12, no. 3 (January 29, 2020): 426. http://dx.doi.org/10.3390/rs12030426.

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The multi-decadal Landsat data record is a unique tool for global land cover and land use change analysis. However, the large volume of the Landsat image archive and inconsistent coverage of clear-sky observations hamper land cover monitoring at large geographic extent. Here, we present a consistently processed and temporally aggregated Landsat Analysis Ready Data produced by the Global Land Analysis and Discovery team at the University of Maryland (GLAD ARD) suitable for national to global empirical land cover mapping and change detection. The GLAD ARD represent a 16-day time-series of tiled Landsat normalized surface reflectance from 1997 to present, updated annually, and designed for land cover monitoring at global to local scales. A set of tools for multi-temporal data processing and characterization using machine learning provided with GLAD ARD serves as an end-to-end solution for Landsat-based natural resource assessment and monitoring. The GLAD ARD data and tools have been implemented at the national, regional, and global extent for water, forest, and crop mapping. The GLAD ARD data and tools are available at the GLAD website for free access.
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Congalton, Russell G. "Mapping and Monitoring Forest Cover." Forests 12, no. 9 (September 1, 2021): 1184. http://dx.doi.org/10.3390/f12091184.

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Luo, H., B. He, X. Kuai, Y. Li, and R. Z. Guo. "LAND COVER EXTRACTION OF COASTAL AREA FROM GF-1 WFV IMAGERY USING ONTOLOGICAL METHOD." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2020 (August 3, 2020): 53–58. http://dx.doi.org/10.5194/isprs-annals-v-3-2020-53-2020.

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Abstract. As a knowledge organization and representation method, ontology that can store land cover spectral, texture, shape attributes and relationships derived from image analysis. With the knowledge organized in ontology, the efficiency of automatic or semi-automatic land cover information extraction for the large coastal area is supposed to be improved. Together with the help of GF-1 Wide Field of View (WFV) data, which covers almost 200 km width area, the more frequent monitoring and change detection for coastal area of Guangxi province are available. This study makes attempt to monitor the land cover of Guangxi coastal area using GF-1 WFV data with ontological method. The land cover ontology for this area is established first via image feature analysis. Using this ontology, automatic image extraction from GF-1 WFV data of subsequent monitoring time is realized. The results of this study reveal that, using ontology, land cover extraction can be completed in acceptable accuracy but with higher efficiency.
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Zerrouki, Nabil, Fouzi Harrou, and Ying Sun. "Statistical Monitoring of Changes to Land Cover." IEEE Geoscience and Remote Sensing Letters 15, no. 6 (June 2018): 927–31. http://dx.doi.org/10.1109/lgrs.2018.2817522.

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Aryal, Rajaram. "National Land Cover Monitoring System for Nepal." Banko Janakari 32, no. 1 (May 31, 2022): 1–2. http://dx.doi.org/10.3126/banko.v32i1.45429.

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Dissertations / Theses on the topic "Monitoring of Land cover"

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Skelsey, Chris. "A system for monitoring land cover." Thesis, University of Aberdeen, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.361785.

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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.
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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.

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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.
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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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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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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.

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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.
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Urban Planning and Design
Doctoral
Doctor of Philosophy
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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.

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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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Books on the topic "Monitoring of Land cover"

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Mackey, E. C. Land cover change: Scotland from the 1940s to the 1980s. Edinburgh: Stationery Office, 1998.

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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 ; International Geographical Union Section The Netherlands, 2005.

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Muzein, Bedru Sherefa. Remote sensing & GIS for land cover/land use change detection and analysis in the semi-natural ecosystems and agriculture landscapes of the Central Ethiopian Rift Valley. Berlin: Rhombos-Verlag, 2010.

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Stager, Robert. A121/RENO/XMONITOR: An interactive program to analyze frequency and cover monitoring data for the Bureau of Land Management : user's guide. [Nevada]: U.S. Dept. of the Interior, Bureau of Land Management, Nev. State Office, 1985.

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Mölders, Nicole. Land-Use and Land-Cover Changes. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-1527-1.

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Lambin, Eric F., and Helmut Geist, eds. Land-Use and Land-Cover Change. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-32202-7.

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Hyatt, Edward Charles. Derelict land monitoring with remote sensing. Birmingham: Aston University. Department of Civil Engineering, 1987.

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Meiner, Andrus. Eesti maakate: CORINE Land Cover projekti täitmine Eestis = Land cover of Estonia : implementation of CORINE Land Cover project in Estonia. Tallinn: EV Keskkonnaministeeriumi Info- ja Tehnokeskus, 1999.

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Dynamic world: Land-cover and land-use change. London: Arnold, 2002.

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Manakos, Ioannis, and Matthias Braun, eds. Land Use and Land Cover Mapping in Europe. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-7969-3.

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Book chapters on the topic "Monitoring of Land cover"

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Probeck, Markus, Gernot Ramminger, David Herrmann, Sharon Gomez, and Thomas Häusler. "European Forest Monitoring Approaches." In Land Use and Land Cover Mapping in Europe, 89–114. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-7969-3_7.

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Munafò, Michele, and Luca Congedo. "Measuring and monitoring land cover." In Urban Expansion, Land Cover and Soil Ecosystem Services, 19–32. London ; Boston : Routledge, 2017.: Routledge, 2017. http://dx.doi.org/10.4324/9781315715674-2.

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Woodcock, Curtis E., and Mutlu Ozdogan. "Trends in Land Cover Mapping and Monitoring." In Land Change Science, 367–77. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-1-4020-2562-4_21.

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Guàrdia, Núria Blanes, Tim Green, and Alejandro Simón. "The Users’ Role in the European Land Monitoring Context." In Land Use and Land Cover Mapping in Europe, 31–41. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-7969-3_3.

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Budd, Jonathan T. C. "Remote sensing techniques for monitoring land-cover." In Monitoring for Conservation and Ecology, 33–59. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3086-8_3.

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Roy, Santanu, Gouri Sankar Bhunia, and Abhisek Chakrabarty. "Land Use/Land Cover Characteristics of Odisha Coastal Zone." In Mapping, Monitoring, and Modeling Land and Water Resources, 49–70. First edition. | Boca Raton : CRC Press, 2021.: CRC Press, 2021. http://dx.doi.org/10.1201/9781003181293-5.

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Kuntz, Steffen, Elisabeth Schmeer, Markus Jochum, and Geoffrey Smith. "Towards an European Land Cover Monitoring Service and High-Resolution Layers." In Land Use and Land Cover Mapping in Europe, 43–52. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-7969-3_4.

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Loveland, Thomas R., and Ruth S. DeFries. "Observing and monitoring land use and land cover change." In Ecosystems and Land Use Change, 231–46. Washington, D. C.: American Geophysical Union, 2004. http://dx.doi.org/10.1029/153gm18.

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Munafò, Michele, and Luca Congedo. "Soil consumption monitoring in Italy." In Urban Expansion, Land Cover and Soil Ecosystem Services, 217–30. London ; Boston : Routledge, 2017.: Routledge, 2017. http://dx.doi.org/10.4324/9781315715674-13.

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Kumar, Mundlamuri Satish, Venkatesh Kolluru, S. B. Gowthami, N. A. Anjita, N. Nayana, Linda Regi, and G. S. Dwarakish. "Monitoring Land Use and Land Cover Changes in Coastal Karnataka." In Lecture Notes in Civil Engineering, 785–95. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6828-2_57.

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Conference papers on the topic "Monitoring of Land cover"

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Xian, George, and Collin Homer. "Monitoring urban land cover change by updating the National Land Cover Database impervious surface products." In 2009 Joint Urban Remote Sensing Event. IEEE, 2009. http://dx.doi.org/10.1109/urs.2009.5137597.

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Gamba, Paolo, Fabio Dell'Acqua, and Giovanna Trianni. "Hypertemporal SAR sequences for monitoring land cover dynamics." In 2008 IEEE Radar Conference (RADAR). IEEE, 2008. http://dx.doi.org/10.1109/radar.2008.4720989.

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Sari, Suci Puspita, and Aditya Pamungkas. "Monitoring Of Land Cover Changes in Mine Environment." In Proceedings of the International Conference on Maritime and Archipelago (ICoMA 2018). Paris, France: Atlantis Press, 2019. http://dx.doi.org/10.2991/icoma-18.2019.50.

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Orlovsky, L., S. Kaplan, N. Orlovsky, D. Blumberg, and E. Mamedov. "Monitoring land use and land cover changes in Turkmenistan using remote sensing." In RAVAGE OF THE PLANET 2006. Southampton, UK: WIT Press, 2006. http://dx.doi.org/10.2495/rav060461.

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Gaetano, R., D. Cozzolino, L. D'Amiano, L. Verdoliva, and G. Poggi. "Fusion of sar-optical data for land cover monitoring." In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2017. http://dx.doi.org/10.1109/igarss.2017.8128242.

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RongFeng, Yang, Hong Liang, Xian Xia, and Yang Kun. "Remote Sensing Monitoring of Land Cover Change in Myanmar." In 2018 26th International Conference on Geoinformatics. IEEE, 2018. http://dx.doi.org/10.1109/geoinformatics.2018.8557102.

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Zhu, Lifen, Yongzhong Tian, and Wenzuo Zhou. "Land cover dynamic monitoring model of Three Gorges area." In Optical Engineering + Applications, edited by Wei Gao and Hao Wang. SPIE, 2008. http://dx.doi.org/10.1117/12.795867.

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Sugita, Mikio. "Monitoring land cover with a vegetation-soil-water index." In Remote Sensing, edited by Edwin T. Engman. SPIE, 1998. http://dx.doi.org/10.1117/12.332750.

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Zewdie, Worku, and E. Csaplovics. "Monitoring land use/land cover dynamics in northwestern Ethiopia using support vector machine." In SPIE Remote Sensing, edited by Ulrich Michel and Karsten Schulz. SPIE, 2014. http://dx.doi.org/10.1117/12.2066461.

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Oad, Vipin Kumar, Muhammad Raza Ul Mustafa, Husna Binti Takaijudin, Ghulam Nabi, and Mubasher Hussain. "Monitoring Trends of Land Use and Land Cover Changes in Rajang River Basin." In 2020 Second International Sustainability and Resilience Conference: Technology and Innovation in Building Designs. IEEE, 2020. http://dx.doi.org/10.1109/ieeeconf51154.2020.9319939.

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Reports on the topic "Monitoring of Land cover"

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Czaplewski, Raymond L., Glenn P. Catts, and Paul W. Snook. National land cover monitoring using large, permanent photo plots. Washington, DC: U.S. Department of Agriculture, Forest Service, Washington Office, 1987. http://dx.doi.org/10.2737/wo-gtr-39.

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Douglas, Thomas, M. Jorgenson, Hélène Genet, Bruce Marcot, and Patricia Nelsen. Interior Alaska DoD training land wildlife habitat vulnerability to permafrost thaw, an altered fire regime, and hydrologic changes. Engineer Research and Development Center (U.S.), February 2022. http://dx.doi.org/10.21079/11681/43146.

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Climate change and intensification of disturbance regimes are increasing the vulnerability of interior Alaska Department of Defense (DoD) training ranges to widespread land cover and hydrologic changes. This is expected to have profound impacts on wildlife habitats, conservation objectives, permitting requirements, and military training activities. The objective of this three-year research effort was to provide United States Army Alaska Garrison Fort Wainwright, Alaska (USAG-FWA) training land managers a scientific-based geospatial framework to assess wildlife habitat distribution and trajectories of change and to identify vulnerable wildlife species whose habitats and resources are likely to decline in response to permafrost degradation, changing wildfire regimes, and hydrologic reorganization projected to 2100. We linked field measurements, data synthesis, repeat imagery analyses, remote sensing measurements, and model simulations focused on land cover dynamics and wildlife habitat characteristics to identify suites of wildlife species most vulnerable to climate change. From this, we created a robust database linking vegetation, soil, and environmental characteristics across interior Alaska training ranges. The framework used is designed to support decision making for conservation management and habitat monitoring, land use, infrastructure development, and adaptive management across the interior Alaska DoD cantonment and training land domain.
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Leis, Sherry. Vegetation community monitoring at Lincoln Boyhood National Memorial: 2011–2019. National Park Service, April 2021. http://dx.doi.org/10.36967/nrr-2284711.

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Lincoln Boyhood National Memorial celebrates the lives of the Lincoln family including the final resting place of Abraham’s mother, Nancy Hanks Lincoln. Lincoln’s childhood in Indiana was a formative time in the life our 16th president. When the Lincoln family arrived in Indiana, the property was covered in the oak-hickory forest type. They cleared land to create their homestead and farm. Later, designers of the memorial felt that it was important to restore woodlands to the site. The woodlands would help visitors visualize the challenges the Lincoln family faced in establishing and maintaining their homestead. Some stands of woodland may have remained, but significant restoration efforts included extensive tree planting. The Heartland Inventory and Monitoring Network began monitoring the woodland in 2011 with repeat visits every four years. These monitoring efforts provide a window into the composition and structure of the wood-lands. We measure both overstory trees and the ground flora within four permanently located plots. At these permanent plots, we record each species, foliar cover estimates of ground flora, diameter at breast height of midstory and overstory trees, and tree regeneration frequency (tree seedlings and saplings). The forest species composition was relatively consistent over the three monitoring events. Climatic conditions measured by the Palmer Drought Severity Index indicated mild to wet conditions over the monitoring record. Canopy closure continued to indicate a forest structure with a closed canopy. Large trees (>45 cm DBH) comprised the greatest amount of tree basal area. Sugar maple was observed to have the greatest basal area and density of the 23 tree species observed. The oaks characteristic of the early woodlands were present, but less dominant. Although one hickory species was present, it was in very low abundance. Of the 17 tree species recorded in the regeneration layer, three species were most abundant through time: sugar maple (Acer saccharum), red bud (Cercis canadensis), and ash (Fraxinus sp.). Ash recruitment seemed to increase over prior years and maple saplings transitioned to larger size classes. Ground flora diversity was similar through time, but alpha and gamma diversity were slightly greater in 2019. Percent cover by plant guild varied through time with native woody plants and forbs having the greatest abundance. Nonnative plants were also an important part of the ground flora composition. Common periwinkle (Vinca minor) and Japanese honeysuckle (Lonicera japonica) continued to be the most abundant nonnative species, but these two species were less abundant in 2019 than 2011. Unvegetated ground cover was high (mean = 95%) and increased by 17% since 2011. Bare ground increased from less than 1% in 2011 to 9% in 2019, but other ground cover elements were similar to prior years. In 2019, we quantified observer error by double sampling two plots within three of the monitoring sites. We found total pseudoturnover to be about 29% (i.e., 29% of the species records differed between observers due to observer error). This 29% pseudoturnover rate was almost 50% greater than our goal of 20% pseudoturnover. The majority of the error was attributed to observers overlooking species. Plot frame relocation error likely contributed as well but we were unable to separate it from overlooking error with our design.
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Short, Mary, and Sherry Leis. Vegetation monitoring in the Manley Woods unit at Wilson’s Creek National Battlefield: 1998–2020. Edited by Tani Hubbard. National Park Service, June 2022. http://dx.doi.org/10.36967/nrr-2293615.

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Natural resource management at Wilson’s Creek National Battlefield (NB) is guided by our understanding of the woodlands and prairies at the time of the Civil War battle in 1861. This report is focused on the Manley Woods unit of the park. This unit is an oak-hickory woodland in the Springfield Plain subsection of the Ozarks. Canopy closure for Missouri oak woodlands can be highly variable and ranges from 30–100% across the spectrum of savanna, open woodland, and closed woodland types. In 1861, the woodland was likely a savanna community. Changes in land use (e.g., fire exclusion) caused an increase in tree density in woodlands at Wilson’s Creek NB and across the Ozarks. Savannas and open woodlands transitioned to closed canopy woodlands over time. Park management plans include restoring the area to a savanna/open woodland structure. Prescribed fire was reintroduced to Wilson’s Creek NB in 1988 and continues as the primary mechanism for reducing the tree canopy. The Manley Woods unit of Wilson’s Creek NB has been subject to intense natural and anthropogenic disturbance events such as a tornado in 2003, timber removal in 2005, prescribed fires in 2006, 2009, and 2019, an ice storm in 2007, and periodic drought. The Heartland Inventory and Monitoring Network (hereafter, Heartland Network) installed four permanent monitoring sites within the Manley Woods area of the park in 1997. Initially, we assessed ground flora and regeneration within the sites (1998–1999). We added fuel sampling after the 2003 tornado. Although overstory sampling occurred prior to the tornado, the protocol was not yet stabilized and pre-2003 overstory data were not included in these analyses. In this report, we focus on the overstory, tree regeneration, and ground cover metrics; ground flora data will be assessed in future analyses. Heartland Network monitoring data reveal that Manley Woods has undergone substantial change in canopy cover and midstory trees since 1998. While basal area and density metrics classify Manley Woods as an open woodland, the closed canopy of the midstory and overstory reveal a plant community that is moving toward closed woodland or forest structure. The most recent fire in 2019 was patchy and mild, resulting in continued increases in fuels. Ground cover metrics indicate infrequent disturbance since leaf litter continued to increase. Management objectives to restore savanna or woodland composition and structure to the Manley Woods overstory, regeneration layer, and ground cover will require implementation of prescribed fire in the future. Repeated fires can thin midstory trees and limit less fire tolerant early seral species. Additionally, mechanical or chemical treatments to reduce undesirable tree species should be considered for woodland restoration. Decreasing canopy closure is an important and essential step toward the restoration of a functioning savanna/open woodland plant community in Manley Woods. Treatments that thin the midstory and reduce fuel loading will also benefit these plant communities. With the anticipated changing climate, maintaining an open woodland community type may also provide resilience through management for native species tolerant of increasingly warmer temperatures.
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Cooper, Christopher, Jacob McDonald, and Eric Starkey. Wadeable stream habitat monitoring at Congaree National Park: 2018 baseline report. National Park Service, June 2021. http://dx.doi.org/10.36967/nrr-2286621.

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The Southeast Coast Network (SECN) Wadeable Stream Habitat Monitoring Protocol collects data to give park resource managers insight into the status of and trends in stream and near-channel habitat conditions (McDonald et al. 2018a). Wadeable stream monitoring is currently implemented at the five SECN inland parks with wadeable streams. These parks include Horseshoe Bend National Military Park (HOBE), Kennesaw Mountain National Battlefield Park (KEMO), Ocmulgee Mounds National Historical Park (OCMU), Chattahoochee River National Recreation Area (CHAT), and Congaree National Park (CONG). Streams at Congaree National Park chosen for monitoring were specifically targeted for management interest (e.g., upstream development and land use change, visitor use of streams as canoe trails, and potential social walking trail erosion) or to provide a context for similar-sized stream(s) within the park or network (McDonald and Starkey 2018a). The objectives of the SECN wadeable stream habitat monitoring protocol are to: Determine status of upstream watershed characteristics (basin morphology) and trends in land cover that may affect stream habitat, Determine the status of and trends in benthic and near-channel habitat in selected wadeable stream reaches (e.g., bed sediment, geomorphic channel units, and large woody debris), Determine the status of and trends in cross-sectional morphology, longitudinal gradient, and sinuosity of selected wadeable stream reaches. Between June 11 and 14, 2018, data were collected at Congaree National Park to characterize the in-stream and near-channel habitat within stream reaches on Cedar Creek (CONG001, CONG002, and CONG003) and McKenzie Creek (CONG004). These data, along with the analysis of remotely sensed geographic information system (GIS) data, are presented in this report to describe and compare the watershed-, reach-, and transect-scale characteristics of these four stream reaches to each other and to selected similar-sized stream reaches at Ocmulgee Mounds National Historical Park, Kennesaw Mountain National Battlefield Park, and Chattahoochee National Recreation Area. Surveyed stream reaches at Congaree NP were compared to those previously surveyed in other parks in order to provide regional context and aid in interpretation of results. edar Creek’s watershed (CONG001, CONG002, and CONG003) drains nearly 200 square kilometers (77.22 square miles [mi2]) of the Congaree River Valley Terrace complex and upper Coastal Plain to the north of the park (Shelley 2007a, 2007b). Cedar Creek’s watershed has low slope and is covered mainly by forests and grasslands. Cedar Creek is designated an “Outstanding Resource Water” by the state of South Carolina (S.C. Code Regs. 61–68 [2014] and S.C. Code Regs. 61–69 [2012]) from the boundary of the park downstream to Wise Lake. Cedar Creek ‘upstream’ (CONG001) is located just downstream (south) of the park’s Bannister Bridge canoe landing, which is located off Old Bluff Road and south of the confluence with Meyers Creek. Cedar Creek ‘middle’ and Cedar Creek ‘downstream’ (CONG002 and CONG003, respectively) are located downstream of Cedar Creek ‘upstream’ where Cedar Creek flows into the relatively flat backswamp of the Congaree River flood plain. Based on the geomorphic and land cover characteristics of the watershed, monitored reaches on Cedar Creek are likely to flood often and drain slowly. Flooding is more likely at Cedar Creek ‘middle’ and Cedar Creek ‘downstream’ than at Cedar Creek ‘upstream.’ This is due to the higher (relative to CONG001) connectivity between the channels of the lower reaches and their out-of-channel areas. Based on bed sediment characteristics, the heterogeneity of geomorphic channel units (GCUs) within each reach, and the abundance of large woody debris (LWD), in-stream habitat within each of the surveyed reaches on Cedar Creek (CONG001–003) was classified as ‘fair to good.’ Although, there is extensive evidence of animal activity...
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Leis, Sherry. Vegetation community monitoring trends in restored tallgrass prairie at Wilson’s Creek National Battlefield: 2008–2020. National Park Service, April 2022. http://dx.doi.org/10.36967/nrr-2293117.

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Plant community monitoring at Wilson’s Creek National Battlefield (NB) focused on the restored tallgrass prairie community. Six monitoring sites were visited four times and observations of plant species and ground cover were made. In addition to those observations, we included two environmental factors in this report—precipitation and recent fire history—to help understand the vegetation data status and trends. Precipitation data (standardized vegetation index) indicated drought conditions in 2012 and some dry periods in 2016. Although prairies are adapted to drought, we found that species richness at the site and community scales (alpha and gamma diversity) were reduced in dry years. Fire management also plays an important role in shaping the plant communities. Prescribed fire occurrence became less frequent through the monitoring period. Also, additional treatments, including herbicide and mowing, likely shaped the prairie community. Tree regeneration and nonnative plants in particular may have been affected by these techniques. The prairie plant community continues to be moderately diverse despite recent increases in tree seedlings and small saplings. Species richness varied over time and was correlated with precipitation; diversity indices (H′ and J′) were similar across monitored years. Species guilds (also known as functional groups) demonstrated differing patterns. Woody plants, long a concern at the park, were abundant and statistically similar across years. Many guilds were quite variable across the sites, but nonnative forbs declined, and nonnative grasses increased. Overstory trees and canopy cover, measured for the first time in 2020, have likely influenced the composition of one site. The composition of this site points to a shrubland-savanna community. Four of the sites tended towards shrubland rather than tallgrass prairie. The vegetation monitoring protocol experienced some changes between 2008 and 2020. A key difference was a shift from sampling twice during the field season to sampling only once in a monitoring year. An anticipated decline in species richness was observed in 2012 and 2016, but we were unable to isolate sample design as the cause. Additionally, we remedied inconsistencies in how tree regeneration was recorded by tallying seedlings and saplings in the field. Our quality assurance procedures indicated that our observer error from pseudoturnover was 20.2%, meeting our expectations. Cover class estimates agreed 73% of the time, with all disagreements within one cover class. Coordinating management actions to achieve plant community goals like structure and composition of tallgrass prairie will be critical to the survival of the prairie species at the park. Fire and nonnative plant treatments along with the reduction of woody cover including trees are needed to arrest the transition to savanna and woodland community types. Frequent prescribed fire is an integral process for this community and there is no equivalent substitute. Continued focus on management for the desired tallgrass prairie community will also provide needed habitat for imperiled pollinators such as the monarch butterfly. Best management practices for pollinators on federal lands specify that treatments (prescribed fire, mowing or haying) should not occur during the blooming season or when pollinator breeding, egg, larval or pupal stages are present.
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Wang, S. Land use/cover. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2016. http://dx.doi.org/10.4095/298873.

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Latifovic, R. Canada's land cover. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2019. http://dx.doi.org/10.4095/313355.

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Latifovic, R. Canada's land cover. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2019. http://dx.doi.org/10.4095/315659.

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Hansen, Leslie. Los Alamos Land Cover. Office of Scientific and Technical Information (OSTI), March 2022. http://dx.doi.org/10.2172/1855122.

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