Trend analysis of variations in carbon stock using stock big data
1
Citation
22
Reference
10
Related Paper
Citation Trend
Keywords:
Stock (firearms)
Land Cover
Agricultural land
Land area
Land cover indicates the physical land type on the earths surface in the form of waterbodies, vegetation etc. whereas land use refers to the human adjustments with the land. Human has been modifying the land as a resource to fulfill their own needs since time immemorial but recently changes in land use land cover is unprecedented at local, regional as well as at the world level. These changes builds an enormous pressure on the surrounding environment and leading to climate change and loss of biodiversity. Thus, an attempt has been made to detect changes in land use land cover classes in Dehradun district of Uttarakhand state. The study has been carried out for 10 years (2009-2019) through remote sensing approach using satellite imageries of LANDSAT-5 TM for March 2009 and LANDSAT-8 OLI & TIRS sensor for April 2019. Methodology based on supervised classification has been applied using maximum likelihood in QGIS. The current analysis resulted that the district Dehradun has experienced land use land cover changes rapidly, as the area occupied by vegetation was about 46 percent during 2009 has decreased to 28 percent in 2019. About 27.54 percent area covered by vegetation gets turned into agriculture, 4.60 percent area into urban/ built-up and 6 percent into barren land. Agriculture and Urban/ Built-up area has increased immensely. Other land use land cover classes such as waterbody and barren land has also undergone changes. Monitoring and mediating the consequences of LULC classes has therefore become a major priority of researchers and policymakers around the world.
Land Cover
Agricultural land
Built-up area
Cite
Citations (2)
Change in land use and land cover (LUCC) is a major component of global environmental change, and the study of the mechanism of the change in land use and land cover is the key to LUCC. This paper analyzes land use/land cover change and its dynamics, points out that spatial analysis is an effective method in the research of LUCC. Then the application of spatial analysis in the research of LUCC, including the spatial analysis technology on the map, statistical analysis and the model of LUCC is discussed. In addition some suggestions for the solution of the present deficiency is the research are given in this paper, in order to lead to an explicit way for the research of mechanism of LUCC.
Land Cover
Global Change
Land information system
Environmental change
Cite
Citations (0)
Land Use and Land Cover Changes (LULC) are some of the worldwide factors that have the most impact on city growth. The aim of this study could be a quantification of the land use/land cover dynamic over three decades in Hawassa City, Ethiopia. This study used multi-spectral satellite images of the years 1990 and 2020. The six category classifications within the study area were agriculture, bare land, built-up, bushland, forest, and water body. The data for the study was acquired from a satellite image of Landsat 5TM 1990, 2000, and 2010 and Landsat 8 OLI and SWIR 2020. Packages like QGIS version 3.2, ArcGIS 10.3, ENVI 4.2, and ERDAS Imagine 2013 were accustomed to performing image classification. From this finding, it is clear that the study space is underneath serious land use conversion of bare land to built-up space and bush land to agricultural. The general LULCC between the years 1990 and 2020, satellite results show that between 1990 and 2020, the quantify of built-up land increased from 12.76% to 16.85% of the study area as bare land and bush decreased from 4.77% to 2.48% and 14.29% to 10.16%, respectively, from the entire study..
Land Cover
Agricultural land
Cite
Citations (2)
Landuse refers to the use of land by human beings while the land cover refers to the natural cover on land. Landuse and land cover mapping is important for better developmental planning purpose. In the present time remote sensing satellite data, geographical information system (GIS) and global positioning system (GPS) are widely used in mapping of land use and land cover. In the present study landuse and land cover change analysis of southeastern part of Panchkula city have been done using Google Earth satellite data of 2002 and 2018. Satellite data downloaded from Google Earth and geo-referenced in ArcGIS 10.4 software. Landuse and landcover classes had been interpreted and field visit was done at selected location to check the interpreted data. Final maps were prepared and area of landuse and land cover classes were calculated. The study shows that during the year 2002 to 2018 built-up land area increased 95.01Hect, agriculture land area increased 1.24Hect., river course area decreased 20.35 Hect., vacant land area decreased 119.43Hect., park area increased 14.64 Hect., open scrub area decreased 7.82 Hect., road area increased 7.21 Hect.,water body area increased 0.02 Hect. and forest area increased 30.48 Hect. The study can be used for monitoring land use and land cover for planning purpose in the study area.
Land Cover
Built-up area
Land information system
Agricultural land
Cite
Citations (1)
Land use and land cover change (LUCC) is one of the most important aspects within the scope of global change. As a critical portion of LUCC study, classification of land use and land cover types not only affects the classification results, but also determines applications of relevant data. In the paper, studies of land use and land cover classification systems were reviewed. It could be concluded that the land use and land cover classification laid more emphasis on the land use classification before the 1970s. The land use classification system emphasized the differences between land functions and was mainly applied to land use inventory investigation and land use mapping. After then, the classification system based on land cover rapidly developed due to the development of satellite remote sensing and computer technology. This classification system emphasized the differences between land categories and was mainly applied to land cover change studies. The review shows that almost all current land use and land cover classification systems tend to be suitable only for a specific research purpose at a certain scale. The incompatibility amongst the current classification systems has resulted in numerous inconveniences and difficulties in the courses of aggregation, analysis, and sharing of land use and land cover data due to the absence of consistent standards. Therefore, a standardized classification system is extremely warranted. However, a universal classification system suitable for all kinds of research purposes is neither possible nor necessary. The reasons lie in that on the one hand, the minimum classification unit could be dependent on the mapping scale as well as the spatial resolution of remote sensing data; on the other hand, land use and land cover types of varying detailed degrees are required for different research purposes. Furthermore, it would be probable that some special land use and land cover types existed in specific regions, for example, the Qinghai-Tibet Plateau in China. At last, this review states that a standardized classification system should be hierarchically organized and could be extended. The land use and land cover types of higher hierarchy could be directly identified from remote sensing images without auxiliary information, which would make it convenient for data comparison and sharing. The land use and land cover types of lower hierarchy could be defined in terms of a specific study purpose, which could satisfy the specific study needs.
Land Cover
Land information system
Scope (computer science)
Classification scheme
Cite
Citations (36)
The National Consortium on Remote Sensing in Transportation - environmental assessment conducts research into the nature of land cover change as it relates to transportation features. For an environmentally sensitive area on the Mississippi Gulf Coast ongoing research studies the changes in land cover for the area with particular emphasis on changes that have occurred related to the completion of Interstate 10. Analyses of population and demographic information, existing land cover data, and non-spectral retrospective research illustrate that the area has changed dramatically in the past 39 years as evidenced by a population increase of around 50%. These preliminary studies have detected the growth and change, but the lack of spectral analysis precludes the identification of spatial patterns of growth and change for the area. In this study, three specific bands were used with reasonable success to correlate soil, wetness, and vegetation. Tasseled cap results and changes in values with the time were used to assess changes in environment. The changes were compared and contrasted to charges in Normalized Difference Vegetation Indices and results were used to assist on dividing areas on the landscape prior to classification. Combinations of supervised and unsupervised classifications were conducted to define land cover and land use types for the area. The resulting products of the exploratory analyses and classification were then used to assess spatial patterns of land cover and land use change for the area.
Land Cover
Environmental change
Cite
Citations (9)
Land Cover
Land information system
Global Change
Cite
Citations (554)
Land cover/use of Earth's surface has been extensively changing due to several natural and human induced factors. Agricultural activities have been causing changes on the landscape resulting in conversion of land cover/use classes and/or variations in agricultural land patterns. Mugla city of Turkey has experienced significant land cover/usage changes mostly because of changes in agricultural practices in the last decade. This research aims to quantify changes in agricultural land of the study area between 2006 and 2014 using CORINE Land Cover methodology. 2006 and 2014 dated satellite images were used to delineate boundaries of different land cover/use classes based on CORINE nomenclature. Our results indicate that agricultural lands have changed from 2006 to 2014 with size of 6 645,84 ha of the whole study area.
Land Cover
Agricultural land
Cite
Citations (6)
The modelling and projecting of land-use change is essential to the assessment of consequent environmental impacts. In agricultural landscapes, land-use patterns nearly always exhibit spatial autocorrelation, which is largely due to the clustered distribution of landscape features as hedgerows and wetlands and also to the spatial interactions between land-use types themselves. The importance of such structural spatial dependencies has to be taken into account while conducting land-use projections. Also, land-use simulations have to be based on land-use and land-cover trends for two reasons: to identify the land-use and land-cover change processes and to be logical with the land-use and land-cover temporal dynamic. The objective of this work is to improve landuse projections in considering the influences of landscape features on land-use and land-cover change and in using long/short series of past observations in the modelling process. Cellular automata (CA) provide a powerful tool for the dynamic modelling of land-use change and are a common methodology used to take spatial interactions into consideration. They have been implemented in land-use models that are able to simulate multiple land-use types. This research adopts the spatial evolution concept embedded in CA and applies it to land-use and land-cover change study in one watershed. This watershed is characterised by a patchy landscape inserted in an intensive agricultural area in Central Brittany (France). Land-use and land-cover changes and agricultural practices have induced water pollution. A time-series of multi-scale and multi-temporal (including historical) satellite imagery and aerial photographs were used to determine both landscape features and the spatial characteristics and land-use and land-cover trends over the period from 1952 to 2003. Socio-economic and biophysical driving forces of observed changes have been established through a network of collaborating partners and agencies willing to share resources and eager to utilise developed techniques and model results. All these input data were compiled, analysed and assessed using spatial statistical techniques to quantify spatial dependencies. A summary of neighbourhood conditions of each target cell reveals the dynamic processes of landuse change constrained within the landscape frame and thus enhances the understanding of transition rules, which is the key element of a CA. Cellular automaton modelling procedures were then applied to develop a spatially explicit model. Model performance was evaluated in comparing simulations where the influence of landscape features on land-use and land-cover change and have been considered insignificant and negligible. The influence of the duration of land-use and land-cover trends has been also tested on land-use and land-cover projections. Results show that introducing landscape features and using a long-term land-use and land-cover trend improve simulations of the future states of land-use and land-cover and contribute to more plausible and realistic scenarios of future changes.
Land Cover
Agricultural land
Cite
Citations (93)
Urbanization processes are some of the key drivers of spatial changes which shape and influence land use and land cover. The aim of sustainable land use policies is to preserve and manage existing resources for present and future generations. Increasing access to information about land use and land cover has led to the emergence of new sources of data and various classification systems for evaluating land use and spatial changes. A single globally recognized land use classification system has not been developed to date, and various sources of land-use/land-cover data exist around the world. As a result, data from different systems may be difficult to interpret and evaluate in comparative analyses. The aims of this study were to compare land-use/land-cover data and selected land use classification systems, and to determine the influence of selected classification systems and spatial datasets on analyses of land-use structure in the examined area. The results of the study provide information about the existing land-use/land-cover databases, revealing that spatial databases and land use and land cover classification systems contain many equivalent land-use types, but also differ in various respects, such as the level of detail, data validity, availability, number of land-use types, and the applied nomenclature.
Land Cover
Land information system
Cite
Citations (11)