CAN THE PEAT THICKNESS CLASSES BE ESTIMATED FROM LAND COVER TYPE APPROACH?
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Indonesia has been known as a home of the tropical peatlands. The peatlands are mainly in Sumatera, Kalimantan and Papua Islands. Spatial information on peatland depth is needed for the planning of agricultural land extensification. The research objective was to develop a preliminary estimation model of peat thickness classes based on land cover approach and analyse its applicability using Landsat 8 image. Ground data, including land cover, location and thickness of peat, were obtained from various surveys and peatlands potential map (Geology Map and Wetlands Peat Map). The land cover types were derived from Landsat 8 image. All data were used to build an initial model for estimating peat thickness classes in Merauke Regency. A table of relationships among land cover types, peat potential areas and peat thickness classes were made using ground survey data and peatlands potential maps of that were best suited to ground survey data. Furthermore, the table was used to determine peat thickness classes using land cover information produced from Landsat 8 image. The results showed that the estimated peat thickness classes in Merauke Regency consist of two classes, i.e., very shallow peatlands and shallow peatlands. Shallow peatlands were distributed at the upper part of Merauke Regency with mainly covered by forest. In comparison with Indonesia Peatlands Map, the number of classes was the two classes. The spatial distribution of shallow peatlands was relatively similar for its precision and accuracy, but the estimated area of shallow peatlands was greater than the area of shallow peatlands from Indonesia Peatlands Map. This research answered the question that peat thickness classes could be estimated by the land cover approach qualitatively. The precise estimation of peat thickness could not be done due to the limitation of insitu data.Keywords:
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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.
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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..
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The aims of the study were to reveal changes in the water-table depth and peat subsidence due to land-use change in West Kalimantan. The location of the study is peatland in Kubu Raya District-West Kalimantan, namely on four types of peatland-use, including secondary peat forest (SPF), shrubs (SB), oil palm plantation (CPP) and corn field (CF). The research parameters include depth of groundwater and peat subsidence. The results show that the conversion of peatland to other peatlands causes an increase in peat subsidy. The research parameters include water-table depth and peat subsidence. The results show that the land-use change of peatlands to other peatlands causes an increase in peat subsidence. The increase in subsidence in measurement II (October 2016) coincides with an increase in water-table depth and measurement V (April 2017) of 74.6%-90.9%. There is a tendency to increase water-table depth in August and October 2016 and January 2017, especially on SB, OPP and CF. SPF has a deeper water-table depth and deeper subsidence than other land. This is due to the deeper peat soil depth of the SPF (509 cm) while the other relatively shallow areas range from 108.2 to 115.5 cm. The correlation between water-table depth and subsidence shows a close relationship and significant (p<0.01, r = 0.824).
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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.
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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.
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Measurements of CH 4 flux from drained and undrained sites in three northern Ontario peatlands (a treed fen, a forested bog, and a treed bog) were made from the beginning of May to the end of October 1991. In the drained portions, the water table had been lowered between 0.1 and 0.5 m, compared to the water table of the undrained portion of the peatlands. The mean seasonal CH 4 flux from the undrained portions of three peatlands was small, ranging from 0 to 8 mg m −2 d −1 , but similar to the CH 4 flux from other treed and forested northern peatlands. The mean seasonal CH 4 flux from the drained portion of the peatlands was either near zero or slightly negative (i.e.,uptake): fluxes ranged from 0.1 to −0.4 mg m −2 d −1 . Profiles of CH 4 in the air‐filled pores in the unsaturated zone, and the water‐filled pores of the saturated zone of the peat at the undrained sites, showed that all the CH 4 produced at depth was consumed within 0.2 m of the water table and that atmospheric CH 4 was consumed in the upper 0.15 m of the peatland. On the basis of laboratory incubations of peat slurries to determine CH 4 production and consumption potentials, the lowering of the water table eliminated the near‐surface zone of CH 4 production that existed in the undrained peatland. However, drainage did not alter significantly the potential for CH 4 oxidation between the water table and peatland surface but increased the thickness of the layer over which CH 4 oxidation could take place. These changes occurred with a drop in the mean summer water table of only 0.1 m (from −0.2 to −0.3 m) suggesting that only a small negative change in soil moisture would be required to significantly reduce CH 4 flux from northern peatlands.
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Abstract Peat specific yield ( S Y ) is an important parameter involved in many peatland hydrological functions such as flood attenuation, baseflow contribution to rivers, and maintaining groundwater levels in surficial aquifers. However, general knowledge on peatland water storage capacity is still very limited, due in part to the technical difficulties related to in situ measurements. The objectives of this study were to quantify vertical S Y variations of water tables in peatlands using the water table fluctuation (WTF) method and to better understand the factors controlling peatland water storage capacity. The method was tested in five ombrotrophic peatlands located in the St. Lawrence Lowlands (southern Québec, Canada). In each peatland, water table wells were installed at three locations (up‐gradient, mid‐gradient, and down‐gradient). Near each well, a 1‐m long peat core (8 cm × 8 cm) was sampled, and subsamples were used to determine S Y with standard gravitational drainage method. A larger peat sample (25 cm × 60 cm × 40 cm) was also collected in one peatland to estimate S Y using a laboratory drainage method. In all sites, the mean water table depth ranged from 9 to 49 cm below the peat surface, with annual fluctuations varying between 15 and 29 cm for all locations. The WTF method produced similar results to the gravitational drainage experiments, with values ranging between 0.13 and 0.99 for the WTF method and between 0.01 and 0.95 for the gravitational drainage experiments. S Y was found to rapidly decrease with depth within 20 cm, independently of the within‐site location and the mean annual water table depth. Dominant factors explaining S Y variations were identified using analysis of variance. The most important factor was peatland site, followed by peat depth and seasonality. Variations in storage capacity considering site and seasonality followed regional effective growing degree days and evapotranspiration patterns. This work provides new data on spatial variations of peatland water storage capacity using an easily implemented method that requires only water table measurements and precipitation data.
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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.
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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.
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