scholarly journals Analysis of Forest Deforestation and its Driving Factors in Myanmar from 1988 to 2017

2019 ◽  
Vol 11 (11) ◽  
pp. 3047 ◽  
Author(s):  
Rongfeng Yang ◽  
Yi Luo ◽  
Kun Yang ◽  
Liang Hong ◽  
Xiaolu Zhou
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Forest Cover ◽  
Agricultural Land ◽  
Temporal Evolution ◽  
Driving Factors ◽  
Deforestation Rate ◽  
Land Cover Data ◽  
Spatio Temporal ◽  
Landsat Satellite

Myanmar, abundant in natural resources, is one of the countries with high forest cover in Southeast Asia. Along with its rapid socio-economic development, however, the construction of large-scale infrastructure, expansion of agricultural land, and an increasing demand for timber products have posed serious threats to the forests and significantly affected regional sustainable development. However, the geographical environment in Myanmar is complex, resulting in the lack of long-term sequence of land cover data products. Based on 30 years’ Landsat satellite remote sensing imagery data and the land cover data extracted by a mixed classification method, this paper examined the spatial and temporal evolution characteristics of forest cover in Myanmar and investigated driving factors of the spatio-temporal evolution. Results show that the forest cover has decreased by 110,621 km2 in the past 30 years with the annual deforestation rate of 0.87%. Cropland expansion is the main reason for the deforestation throughout the study period. The study can provide basic information of the forest cover data to the Myanmar government for ecological environment protection. At the same time, it can provide important support to the “Belt and Road” initiative to invest in the region’s economy.

scholarly journals Suburban frog ponds: mapping local land cover yields different estimates of landscape composition than the National Land Cover Database

2014 ◽  
Author(s):  
Max Lambert
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Forest Cover ◽  
Spatial Scales ◽  
Landscape Composition ◽  
Ecological Processes ◽  
Simple Method ◽  
Fine Grain ◽  
Land Cover Data ◽  
Ecological Patterns

Suburban neighborhoods are rapidly spreading globally. As such, there is an increasing need to study the environmental and ecological effects of suburbanization. At large spatial extents, from county-level to global, remote sensing-derived land cover data, such as the National Land Cover Dataset (NLCD), have yielded insight into patterns of urbanization and concomitant large-scale ecological patterns in response. However, the components of suburban land cover (houses, yards, etc.) are dispersed throughout the landscape at a finer scale than the relatively coarse grain size (30m pixels) of NLCD may be able to detect. Our understanding of ecological processes in heterogeneous landscapes is reliant upon the accuracy and resolution of our measurements as well as the scale at which we measure the landscape. Analyses of ecological processes along suburban gradients are restricted by the currently available data. As ecologists are becoming increasingly interested in describing phenomena at spatial extents as small as individual households, we need higher-resolution landscape measurements. Here, I describe a simple method of translating the components of suburban landscapes into finer-grain, local land cover (LLC) data in GIS. Using both LLC and NLCD, I compare the suburban matrix surrounding ponds occupied by two different frog species. I illustrate large discrepancies in Forest, Yard, and Developed land cover estimates between LLC and NLCD, leading to markedly different interpretations of suburban landscape composition. NLCD, relative to LLC, estimates lower proportions of forest cover and higher proportions of anthropogenic land covers in general. These two land cover datasets provide surprisingly different descriptions of the suburban landscapes, potentially affecting our understanding of how organisms respond to an increasingly suburban world. LLC provides a free and detailed fine-grain depiction of the components of suburban neighborhoods and will allow ecologists to better explore heterogeneous suburban landscapes at multiple spatial scales.

Spatio-temporal comparison of different approaches to derive land use and land cover change emissions by models

2021 ◽  
Author(s):  
Wolfgang Obermeier ◽  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Large Scale ◽  
Agricultural Land ◽  
Land Cover Changes ◽  
Legacy Effects ◽  
Equatorial Africa ◽  
The Usa ◽  
Spatio Temporal ◽  
Global Carbon

<p>The quantification of the net carbon flux from land use and land cover changes (f<sub>LULCC</sub>) is essential to understand the global carbon cycle, and consequently, to support climate change mitigation. However, large-scale f<sub>LULCC</sub> is not directly measurable, and can only be inferred by models, such as semi-empirical bookkeeping models, and process-based dynamic global vegetation models (DGVMs). By definition, f<sub>LULCC</sub> estimates between these two model types are not directly comparable. For example, transient DGVM-based f<sub>LULCC</sub> of the annual global carbon budget includes the so-called Loss of Additional Sink Capacity (LASC). The latter accounts for environmental impacts on the land carbon storage capacities of managed land compared to potential vegetation which is not included in bookkeeping models. Additionally, estimates of transient DGVM-based f<sub>LULCC</sub> differ from bookkeeping model estimates, since they depend on arbitrarily chosen simulation time periods and the timing of land use and land cover changes within the historic period (which includes different accumulation periods for legacy effects). However, DGVMs enable a f<sub>LULCC</sub> approximation independent of the timing of land use and land cover changes and their legacy effects by simulations run under constant pre-industrial or present-day environmental forcings.</p><p>In this study, we analyze these different DGVM-derived f<sub>LULCC</sub> definitions, under transiently changing environmental conditions and fixed pre-industrial and fixed present-day conditions, within 18 regions for twelve DGVMs and quantify their differences as well as climate- and CO<sub>2</sub>-induced components. The multi model mean under transient conditions reveals a global f<sub>LULCC</sub> of 2.0±0.6 PgC yr<sup>-1</sup> for 2009-2018, with ~40% stemming from the LASC (0.8±0.3 PgC yr<sup>-1</sup>). Within the industrial period (1850 onward), cumulative f<sub>LULCC</sub> reached 189±56 PgC with 40±15 PgC from the LASC.</p><p>Regional hotspots of high LASC values exist in the USA, China, Brazil, Equatorial Africa and Southeast Asia, which we mainly relate to deforestation for cropland. Distinct negative LASC estimates were observed in Europe (early reforestation) and from 2000 onward in the Ukraine (recultivation of post-Soviet abandoned agricultural land). Negative LASC estimates indicate that fLULCC estimates in these regions are lower in transient DGVM simulations compared to bookkeeping-approaches. By unraveling the spatio-temporal variability of the different DGVM-derived f<sub>LULCC</sub> estimates, our study calls for a harmonized attribution of model-derived f<sub>LULCC</sub>. We propose an approach that bridges bookkeeping and DGVM approaches for f<sub>LULCC</sub> estimation by adopting a mean DGVM-ensemble LASC for a defined reference period.</p>

scholarly journals Suburban frog ponds: mapping local land cover yields different estimates of landscape composition than the National Land Cover Database

2014 ◽  
Author(s):  
Max Lambert
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Forest Cover ◽  
Spatial Scales ◽  
Landscape Composition ◽  
Ecological Processes ◽  
Simple Method ◽  
Fine Grain ◽  
Land Cover Data ◽  
Ecological Patterns

Suburban neighborhoods are rapidly spreading globally. As such, there is an increasing need to study the environmental and ecological effects of suburbanization. At large spatial extents, from county-level to global, remote sensing-derived land cover data, such as the National Land Cover Dataset (NLCD), have yielded insight into patterns of urbanization and concomitant large-scale ecological patterns in response. However, the components of suburban land cover (houses, yards, etc.) are dispersed throughout the landscape at a finer scale than the relatively coarse grain size (30m pixels) of NLCD may be able to detect. Our understanding of ecological processes in heterogeneous landscapes is reliant upon the accuracy and resolution of our measurements as well as the scale at which we measure the landscape. Analyses of ecological processes along suburban gradients are restricted by the currently available data. As ecologists are becoming increasingly interested in describing phenomena at spatial extents as small as individual households, we need higher-resolution landscape measurements. Here, I describe a simple method of translating the components of suburban landscapes into finer-grain, local land cover (LLC) data in GIS. Using both LLC and NLCD, I compare the suburban matrix surrounding ponds occupied by two different frog species. I illustrate large discrepancies in Forest, Yard, and Developed land cover estimates between LLC and NLCD, leading to markedly different interpretations of suburban landscape composition. NLCD, relative to LLC, estimates lower proportions of forest cover and higher proportions of anthropogenic land covers in general. These two land cover datasets provide surprisingly different descriptions of the suburban landscapes, potentially affecting our understanding of how organisms respond to an increasingly suburban world. LLC provides a free and detailed fine-grain depiction of the components of suburban neighborhoods and will allow ecologists to better explore heterogeneous suburban landscapes at multiple spatial scales.

scholarly journals Relief influence on the spatial distribution of the Atlantic Forest cover on the Ibiúna Plateau, SP

2007 ◽  
Vol 67 (3) ◽  
pp. 403-411 ◽  
Author(s):  
WG. Silva ◽  
JP. Metzger ◽  
S. Simões ◽  
C Simonetti
Keyword(s):  
Atlantic Forest ◽  
Urban Areas ◽  
Large Scale ◽  
Forest Cover ◽  
Agricultural Land ◽  
Environmental Gradients ◽  
Aerial Photographs ◽  
General Distribution ◽  
Successional Stage ◽  
Slope Orientation

Several studies suggest that, on a large scale, relief conditions influence the Atlantic Forest cover. The aim of this work was to explore these relationships on a local scale, in Caucaia do Alto, on the Ibiúna Plateau. Within an area of about 78 km², the distribution of forest cover, divided into two successional stages, was associated with relief attribute data (slope, slope orientation and altitude). The mapping of the vegetation was based on the interpretation of stereoscopic pairs of aerial photographs, from April 2000, on a scale of 1:10,000, while the relief attributes were obtained by geoprocessing from digitalized topographic maps on a scale of 1:10,000. Statistical analyses, based on qui-square tests, revealed that there was a more extensive forest cover, irrespective of the successional stage, in steeper areas (>10 degrees) located at higher altitudes (>923 m), but no influence of the slope orientation. There was no sign of direct influence of relief on the forest cover through environmental gradients that might have contributed to the forest regeneration. Likewise, there was no evidence that these results could have been influenced by the distance from roads or urban areas or with respect to permanent preservation areas. Relief seems to influence the forest cover indirectly, since agricultural land use is preferably made in flatter and lower areas. These results suggest a general distribution pattern of the forest remnants, independent of the scale of study, on which relief indirectly has a strong influence, since it determines human occupation.

scholarly journals The Spatio-Temporal Evolution of River Island Based on Landsat Satellite Imagery, Hydrodynamic Numerical Simulation and Observed Data

2018 ◽  
Vol 10 (12) ◽  
pp. 2046 ◽  
Author(s):  
Haiyun Shi ◽  
Yuhan Cao ◽  
Changming Dong ◽  
Changshui Xia ◽  
Chunhui Li
Keyword(s):  
Numerical Simulation ◽  
Temporal Evolution ◽  
Thematic Mapper ◽  
Sediment Discharge ◽  
Natural Factor ◽  
River Islands ◽  
Island Evolution ◽  
Spatio Temporal ◽  
Landsat Satellite ◽  
Changing Rate

A river island is a shaped sediment accumulation body with its top above the water’s surface in crooked or branching streams. In this paper, four river islands in Yangzhong City in the lower reaches of the Yangtze River were studied. The spatio-temporal evolution information of the islands was quantitatively extracted using the threshold value method, binarization model, and cluster analysis, based on Thematic Mapper (TM) and Enhanced Thematic Mapper+ (ETM+) images of the Landsat satellite series from 1985 to 2015. The variation mechanism and influencing factors were analyzed using an unstructured-grid, Finite-Volume Coastal Ocean Model (FVCOM) hydrodynamic numerical simulation, as well as the water-sediment data measured by hydrological stations. The annual average total area of these islands was 251,224.46 m2 during 1985–2015, and the total area first increased during 1985–2000 and decreased later during 2000–2015. Generally, the total area increased during these 30 years. Taipingzhou island had the largest area and the biggest changing rate, Xishadao island had the smallest area, and Zhongxinsha island had the smallest changing rate. The river islands’ area change was influenced by river runoff, sediment discharge, and precipitation, and sediment discharge proved to be the most significant natural factor in island evolution. River island evolution was also found to be affected by both runoff and oceanic tide. The difference in flow-field caused silting up in the Leigongdao Island and the head of Taipingzhou Island, and a serious reduction in the middle and tail of Taipingzhou Island. The method used in this paper has good applicability to river islands in other rivers around the world.

scholarly journals Uncertainty Analysis of Multisource Land Cover Products in China

2021 ◽  
Vol 13 (16) ◽  
pp. 8857
Author(s):  
Longhao Wang ◽  
Jiaxin Jin
Keyword(s):  
Land Cover ◽  
Forest Cover ◽  
Regional Scale ◽  
Least Squares Regression ◽  
Average Deviation ◽  
Deviation Analysis ◽  
Land Cover Data ◽  
Spatial Changes ◽  
Forest Coverage ◽  
High Consistency

Satellite-based land cover products play a crucial role in sustainability. There are several types of land cover products, such as qualitative products with discrete classes, semiquantitative products with several classes at a predetermined ratio, and quantitative products with land cover fractions. The proportions of land cover types in the grids with coarse resolution should be considered when used at the regional scale (e.g., modeling and remote sensing inversion). However, uncertainty, which varies with spatial distribution and resolution, needs to be studied further. This study used MCD12, ESA CCI, and MEaSURES VCF land cover data as indicators of qualitative, semiquantitative, and quantitative products, respectively, to explore the uncertainty of multisource land cover data. The methods of maximum area aggregation, deviation analysis, and least squares regression were used to investigate spatiotemporal changes in forests and nontree vegetation at diverse pixel resolutions across China. The results showed that the average difference in forest coverage for the three products was 8%, and the average deviation was 11.2%. For forest cover, the VCF and ESA CCI exhibited high consistency. For nontree vegetation, the ESA CCI and MODIS exhibited the lowest differences. The overall uncertainty in the temporal and spatial changes of the three products was relatively small, but there were significant differences in local areas (e.g., southeastern hills). Notably, as the spatial resolution decreased, the three products’ uncertainty decreased, and the resolution of 0.1° was the inflection point of consistency.

scholarly journals Regionalisation of asset values for risk analyses

2006 ◽  
Vol 6 (2) ◽  
pp. 167-178 ◽  
Author(s):  
A. H. Thieken ◽  
M. Müller ◽  
L. Kleist ◽  
I. Seifert ◽  
D. Borst ◽  
...  
Keyword(s):  
Land Cover ◽  
Urban Areas ◽  
Large Scale ◽  
Census Data ◽  
Population Data ◽  
Dasymetric Mapping ◽  
Unit Value ◽  
Land Cover Data ◽  
Mapping Techniques ◽  
Asset Data

Abstract. In risk analysis there is a spatial mismatch of hazard data that are commonly modelled on an explicit raster level and exposure data that are often only available for aggregated units, e.g. communities. Dasymetric mapping techniques that use ancillary information to disaggregate data within a spatial unit help to bridge this gap. This paper presents dasymetric maps showing the population density and a unit value of residential assets for whole Germany. A dasymetric mapping approach, which uses land cover data (CORINE Land Cover) as ancillary variable, was adapted and applied to regionalize aggregated census data that are provided for all communities in Germany. The results were validated by two approaches. First, it was ascertained whether population data disaggregated at the community level can be used to estimate population in postcodes. Secondly, disaggregated population and asset data were used for a loss evaluation of two flood events that occurred in 1999 and 2002, respectively. It must be concluded that the algorithm tends to underestimate the population in urban areas and to overestimate population in other land cover classes. Nevertheless, flood loss evaluations demonstrate that the approach is capable of providing realistic estimates of the number of exposed people and assets. Thus, the maps are sufficient for applications in large-scale risk assessments such as the estimation of population and assets exposed to natural and man-made hazards.

Unmixing the regional climate response to recent historical land cover changes in Europe

2020 ◽  
Author(s):  
Bo Huang ◽  
Xiangping Hu ◽  
Geir-Arne Fuglstad ◽  
Xu Zhou ◽  
Wenwu Zhao ◽  
...  
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Agricultural Land ◽  
Climate Model ◽  
Regional Climate ◽  
Surface Energy Budget ◽  
Land Cover Changes ◽  
Climate Response ◽  
Statistical Regression ◽  
Cooling Effects

<p>Land cover changes (LCCs) influence the regional climate because they alter biophysical mechanisms like evapotranspiration, albedo, and surface roughness. Previous research mainly assessed the regional climate implications of individual land cover transitions, such as the effects of historical forest clearance or idealized large-scale scenarios of deforestation/afforestation, but the combined effects from the mix of recent historical land cover changes in Europe have not been explored. In this study, we use a combination of high resolution land cover data with a regional climate model (the Weather Research and Forecasting model, WRF, v3.9.1) to quantify the effects on surface temperature of land cover changes between 1992 and 2015. Unlike many previous studies that had to use one unrealistic large-scale simulation for each LCC to single out its climate effects, our analysis simultaneously considers the effects of the mix of historical land cover changes in Europe and introduces a new method to disentangle the individual contributions. This approach, based on a ridge statistical regression, does not require an explicit consideration of the different components of the surface energy budget, and directly shows the temperature changes from each land transition.</p><p>            From 1992 to 2015, around 70 Mha of land transitions occurred in Europe. Approximately 25 Mha of agricultural land was left abandoned, which was only partially compensated by cropland expansion (about 20 Mha). Declines in agricultural land mostly occurred in favor of forests (15 Mha) and urban settlements (8 Mha). Relative to 1992, we find that the land covers of 2015 are associated with an average temperature cooling of -0.12±0.20 °C, with seasonal and spatial variations. At a continental level, the mean cooling is mainly driven by agriculture abandonment (cropland-to-forest transitions). Idealized simulations where cropland transitions to other land classes are excluded result in a mean warming of +0.10±0.19 °C, especially during summer. Conversions to urban land always resulted in warming effects, whereas the local temperature response to forest gains and losses shows opposite signs from the western and central part of the domain (where forests have cooling effects) to the eastern part (where forests are associated to warming). Gradients in soil moisture and local climate conditions are the main drivers of these differences. Our findings are a first attempt to quantify the regional climate response to historical LCC in Europe, and our method allows to unmix the temperature signal of a grid cell to the underlying LCCs (i.e., temperature impact per land transition). Further developing biophysical implications from LCCs for their ultimate consideration in land use planning can improve synergies for climate change adaptation and mitigation.</p><p> </p>

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