scholarly journals Building Land Cover Objects Following the IPCC Guidelines for Carbon Emission Estimation. Case Study in the Central Highlands of Vietnam

2021 ◽  
Vol 6 (1) ◽  
pp. p45
Author(s):  
Le Q. Hung ◽  
Vu T. P. Thao
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Greenhouse Gas ◽  
Carbon Emissions ◽  
Input Data ◽  
Carbon Emission ◽  
Greenhouse Gas Inventory ◽  
Emission Estimation

We estimated the carbon emissions in the field of Land Use, Land Use Change, and Forestry (LULUCF) by using advanced technology to build the input data. Remote sensing, including satellite remote sensing and Unmanned Aerial Vehicles (UAV) with transparency, multi-time, and wide coverage characteristics, is useful in this area. The article focuses on the proposed regulations and building process of subjects in the land cover of the Vietnamese mainland following the guidance of the Intergovernmental Panel on Climate Change as applied to carbon emission estimation. We propose a process to estimate carbon emissions using the Agriculture and Land Use Greenhouse Gas Inventory software with input data extracted from the remote sensing images. An experiment on land cover change was carried out over ten years between 2006 and 2016 in the Central Highlands of Vietnam. The results obtained with remote sensing data classification for land cover categories achieved a reliability of 69% for the year 2006 and 66% for the year 2016. The carbon emission estimation data were checked and used in Vietnam’s biennial update report to the United Nations Framework Convention on Climate Change, including content and updated information on the greenhouse gas inventory.

scholarly journals MULTITEMPORAL ANALYSIS OF FOREST COVER CHANGE USING REMOTE SENSING AND GIS OF KANHA TIGER RESERVE, CENTRAL INDIA

2018 ◽  
Vol XLII-5 ◽  
pp. 211-219 ◽  
Author(s):  
R. M. Devi ◽  
B. Sinha ◽  
J. Bisaria ◽  
S. Saran
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Forest Cover ◽  
Central India ◽  
Adaptation Strategies ◽  
Land Use Land Cover ◽  
Tiger Reserve ◽  
The Impact

<p><strong>Abstract.</strong> Forest ecosystems play a key role in global ecological balance and provide a variety of tangible and intangible ecosystem services that support the livelihoods of rural poor. In addition to the anthropogenic pressure on the forest resources, climate change is also impacting vegetation productivity, biomass and phenological patterns of the forest. There are many studies reported all over the world which use change in Land Use Land Cover (LULC) to assess the impact of climate change on the forest. Land use change (LC) refers to any anthropogenic or natural changes in the terrestrial ecosystem at a variety of spatial or temporal scale. Changes in LULC induced by any causes (natural/anthropogenic) play a major role in global as well as regional scale pattern which in turn affects weather and climate. Remote sensing (RS) data along with Geographic Information System (GIS) help in inventorying, mapping and monitoring of earth resources for effective and sustainable landscape management of forest areas. Accurate information about the current and past LULC including natural forest cover along with accurate means of monitoring the changes are very necessary to design future adaptation strategies and formulation of policies in tune of climate change. Therefore, this study attempts to analyze the changes of LULC of Kanha Tiger Reserve (KTR) due to climate change. The rationale for selecting KTR is to have a largely intact forest area without any interference so that any change in LULC could be attributed to the impact of climate change. The change analysis depicted changes in land use land cover (LULC) pattern by using multi-temporal satellite data over a period of time. Further, these detected changes in different LULC class influence the livelihoods of forest-dependent communities. As the study site is a Sal dominated landscape; the findings could be applied in other Sal dominated landscape of central India in making future policies, adaptation strategies and silvicultural practices for reducing the vulnerability of forest-dependent communities.</p>

scholarly journals Global rules for translating land-use change (LUH2) to land-cover change for CMIP6 using GLM2

2020 ◽  
Vol 13 (7) ◽  
pp. 3203-3220 ◽  
Author(s):  
Lei Ma ◽  
George C. Hurtt ◽  
Louise P. Chini ◽  
Ritvik Sahajpal ◽  
Julia Pongratz ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Use Change ◽  
Land Cover ◽  
Land Cover Change ◽  
Carbon Emissions ◽  
Forest Cover ◽  
Land Use Changes ◽  
Change Models ◽  
Translation Rule

Abstract. Anthropogenic land-use and land-cover change activities play a critical role in Earth system dynamics through significant alterations to biogeophysical and biogeochemical properties at local to global scales. To quantify the magnitude of these impacts, climate models need consistent land-cover change time series at a global scale, based on land-use information from observations or dedicated land-use change models. However, a specific land-use change cannot be unambiguously mapped to a specific land-cover change. Here, nine translation rules are evaluated based on assumptions about the way land-use change could potentially impact land cover. Utilizing the Global Land-use Model 2 (GLM2), the model underlying the latest Land-Use Harmonization dataset (LUH2), the land-cover dynamics resulting from land-use change were simulated based on multiple alternative translation rules from 850 to 2015 globally. For each rule, the resulting forest cover, carbon density and carbon emissions were compared with independent estimates from remote sensing observations, U.N. Food and Agricultural Organization reports, and other studies. The translation rule previously suggested by the authors of the HYDE 3.2 dataset, that underlies LUH2, is consistent with the results of our examinations at global, country and grid scales. This rule recommends that for CMIP6 simulations, models should (1) completely clear vegetation in land-use changes from primary and secondary land (including both forested and non-forested) to cropland, urban land and managed pasture; (2) completely clear vegetation in land-use changes from primary forest and/or secondary forest to rangeland; (3) keep vegetation in land-use changes from primary non-forest and/or secondary non-forest to rangeland. Our analysis shows that this rule is one of three (out of nine) rules that produce comparable estimates of forest cover, vegetation carbon and emissions to independent estimates and also mitigate the anomalously high carbon emissions from land-use change observed in previous studies in the 1950s. According to the three translation rules, contemporary global forest area is estimated to be 37.42×106 km2, within the range derived from remote sensing products. Likewise, the estimated carbon stock is in close agreement with reference biomass datasets, particularly over regions with more than 50 % forest cover.

Estimation of emission from organic soils

2021 ◽  
Author(s):  
Sergiy Stepanenko ◽  
Anatoliy Polevoy ◽  
Alexander Mykytiuk
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Land Cover ◽  
Soil Carbon ◽  
Input Data ◽  
Carbon Deposition ◽  
Organic Soils ◽  
Climate Change Scenarios ◽  
Space Images ◽  
Soil System

&lt;p&gt;Dynamic modeling of the processes of transformation of soil organic matter is part of a more complex problem - modeling the processes of soil formation and functioning of soils, and the development of the entire soil system. It is important tool for studying the functioning and predicting changes in the soil system, quantifying the role of the soil cover in the balance of greenhouse gases in the atmosphere and in the processes of climate change&lt;/p&gt;&lt;p&gt;The PEAT-GHG-Model (furthermore &amp;#8211; PEAT-GHG-MODEL), based on further development of ROTHC-model (Coleman, Jenkinson, 2008) for mineral soil and ECOSSE model (Smith, Gottschalk et al., 2010) for organic soils.&lt;/p&gt;&lt;p&gt;&amp;#160;The PEAT-GHG-MODEL evaluates of CO&lt;sub&gt;2&lt;/sub&gt;, CH&lt;sub&gt;4&lt;/sub&gt;, N&lt;sub&gt;2&lt;/sub&gt;O fluxes values at organic soils and soil carbon deposition for non-forest types of land cover. The model utilize data from existing weather stations, published soil data, and data generated by remote sensing of land cover. The model evaluates of CO&lt;sub&gt;2&lt;/sub&gt;, CH&lt;sub&gt;4&lt;/sub&gt;, N&lt;sub&gt;2&lt;/sub&gt;O fluxes values at organic soils and soil carbon deposition, including at peatlands, retrospectively for targeted period or back in time with available space images library. The model can evaluates of CO&lt;sub&gt;2&lt;/sub&gt;, CH&lt;sub&gt;4&lt;/sub&gt;, N&lt;sub&gt;2&lt;/sub&gt;O fluxes values at organic soils and soil carbon deposition for future period based on meteorological input data generated by climate change scenarios and land cover data generated by relevant habitats (land cover) change scenarios. The PEAT-GHG-MODEL estimates of CO&lt;sub&gt;2&lt;/sub&gt;, CH&lt;sub&gt;4&lt;/sub&gt;, N&lt;sub&gt;2&lt;/sub&gt;O fluxes from organic soils and soil carbon deposition for non-forest types of land cover. The model input data generates by existing weather stations, remote sensing of land cover and published soils data. The model estimates of GHG emissions from organic soils, including peatlands, retrospectively for targeted period or back in time with available space images library. The model can simulates of GHG emissions for future period based on meteorological input data generated by climate change scenarios and land cover data generated by relevant habitats change scenarios. The model generates georeferenced data. Minimum land surface area, which can be evaluates by model, equal of size of one pixel of land cover images, used for remote sensing of land cover, it can be 1 m&lt;sup&gt;2&lt;/sup&gt; or less. Due to high resolution, the model estimates highly variable in space CO&lt;sub&gt;2&lt;/sub&gt;, CH&lt;sub&gt;4&lt;/sub&gt;, N&lt;sub&gt;2&lt;/sub&gt;O fluxes with high accuracy. Maximum land surface area is not limited. The model generates data on decade and/or annual bases. Article presents the model&amp;#8217; verification results. The model verified in 2017 by independent, from the model authors, verification team in frame of &amp;#8220;CLIMA EAST: conservation and sustainable use of peatlands&amp;#8221; project (UNDP-Ukraine). Direct field measurement data for two peatlands used for model verification, including one site drained, and another one is under natural hydrological conditions.&amp;#160; The cumulative annual of CH&lt;sub&gt;4&lt;/sub&gt; and CO&lt;sub&gt;2&lt;/sub&gt; emission presented in Table.&lt;/p&gt;&lt;p&gt;The model calculations were compared with the experimental data obtained for peat soils in the western Polesie of Belarus. The cumulative annual of CH&lt;sub&gt;4&lt;/sub&gt; and CO&lt;sub&gt;2&lt;/sub&gt; emission presented in Table.&lt;/p&gt;&lt;p&gt;Table. Cumulative annual of CH&lt;sub&gt;4&lt;/sub&gt; and CO&lt;sub&gt;2&lt;/sub&gt; emissions according to field measurements and assessment of PEAT-GHG-MODEL&lt;/p&gt;&lt;p&gt;&lt;img src=&quot;https://contentmanager.copernicus.org/fileStorageProxy.php?f=gepj.ccbf8aaeedff56227740161/sdaolpUECMynit/12UGE&amp;app=m&amp;a=0&amp;c=482eb671aeb385948d36c48791670031&amp;ct=x&amp;pn=gepj.elif&amp;d=1&quot; alt=&quot;&quot; width=&quot;906&quot; height=&quot;718&quot;&gt;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;div&gt; &lt;div&gt;&amp;#160;&lt;/div&gt; &lt;/div&gt;

Satellite Image Classes Categorization Schemes for United Nations Framework Convention on Climatic Change (UNFCCC)

2018 ◽  
pp. 101-113
Author(s):  
Joyce Gosata Maphanyane ◽  
Gofetamang Phunyuka
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Electromagnetic Waves ◽  
Satellite Image ◽  
National Representation ◽  
Thematic Resolution ◽  
The One

This chapter looks at the disparities between the UNFCCC – GHG – Land-Use and Land-Cover Change (LULCC) remote sensing images classification scheme with that of Botswana for the GHG inventory for the National Representation. This chapter has points out that the Botswana Scheme maximizes the LANDSAT System electromagnetic waves capabilities and maps produced give more classes and better thematic resolution for the classification of land cover classes. Suggestions are made for these two schemes to be reconciled and use the one which gives the best GHG calculated results for inventories for Inter-Governmental Panel on Climate Change (IPCC) Reporting

scholarly journals Predicting the Potential Impact of Climate Change on Carbon Stock in Semi-Arid West African Savannas

Land ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 124 ◽  
Author(s):  
Kangbéni Dimobe ◽  
Jean Kouakou ◽  
Jérôme Tondoh ◽  
Benewinde Zoungrana ◽  
Gerald Forkuor ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Land Cover ◽  
Carbon Emissions ◽  
Carbon Stock ◽  
Carbon Stocks ◽  
West African ◽  
Impact Of Climate Change ◽  
Gallery Forests ◽  
Semi Arid

West African savannas are experiencing rapid land cover change that threatens biodiversity and affects ecosystem productivity through the loss of habitat and biomass, and carbon emissions into the atmosphere exacerbating climate change effects. Therefore, reducing carbon emissions from deforestation and forest degradation in these areas is critical in the efforts to combat climate change. For such restorative actions to be successful, they must be grounded on a clear knowledge of the extent to which climate change affects carbon storage in soil and biomass according to different land uses. The current study was undertaken in semi-arid savannas in Dano, southwestern Burkina Faso, with the threefold objective of: (i) identifying the main land use and land cover categories (LULCc) in a watershed; (ii) assessing the carbon stocks (biomass and soil) in the selected LULCc; and (iii) predicting the effects of climate change on the spatial distribution of the carbon stock. Dendrometric data (Diameter at Breast Height (DBH) and height) of woody species and soil samples were measured and collected, respectively, in 43 plots, each measuring 50 × 20 m. Tree biomass carbon stocks were calculated using allometric equations while soil organic carbon (SOC) stocks were measured at two depths (0–20 and 20–50 cm). To assess the impact of climate change on carbon stocks, geographical location records of carbon stocks, remote sensing spectral bands, topographic data, and bioclimatic variables were used. For projections of future climatic conditions, predictions from two climate models (MPI-ESM-MR and HadGEM2-ES) of CMIP5 were used under Representative Concentration Pathway (RCP) 8.5 and modeling was performed using random forest regression. Results showed that the most dominant LULCc are cropland (37.2%) and tree savannas (35.51%). Carbon stocks in woody biomass were higher in woodland (10.2 ± 6.4 Mg·ha−1) and gallery forests (7.75 ± 4.05 Mg·ha−1), while the lowest values were recorded in shrub savannas (0.9 ± 1.2 Mg·ha−1) and tree savannas (1.6 ± 0.6 Mg·ha−1). The highest SOC stock was recorded in gallery forests (30.2 ± 15.6 Mg·ha−1) and the lowest in the cropland (14.9 ± 5.7 Mg·ha−1). Based on modeling results, it appears clearly that climate change might have an impact on carbon stock at horizon 2070 by decreasing the storage capacity of various land units which are currently suitable. The decrease was more important under HadGEM2-ES (90.0%) and less under MPI-ESM-MR (89.4%). These findings call for smart and sustainable land use management practices in the study area to unlock the potential of these landscapes to sequestering carbon.

scholarly journals Rainfall prediction to evaluate the flood risk using remote sensing and geographical information system

2021 ◽  
Vol 1 (2) ◽  
pp. 14-22
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Land Use ◽  
Information System ◽  
Land Cover ◽  
Geographical Information System ◽  
Flood Risk ◽  
Curve Number ◽  
Geographical Information ◽  
Runoff Curve Number

Abstract: In this study, the runoff curve number map for Navrud watershed in north of Iran was determined based on the soil hydrological group, land-use and land-cover using remote sensing and geographical information system. For this objective, land-cover and Land-use situation maps were prepared using NDVI index and Landsat satellite data, respectively. Runoff curve number maps were determined using the overlay prepared maps in GIS and SCS table. For evaluating the accuracy of estimated curve numbers, runoff maximum discharge was calculated using HEC-HMS model and compared to the observed values. Furthermore, the climate change trend and probabilistic distribution functions were considered to predict the flood risk. The effects of climate change were defined by atmospheric general circulation models for A1B, A2 and B1 scenarios. Error analysis between calculated and observed discharge showed that watershed curve number was determined with acceptable accuracy.

scholarly journals Spatial-Temporal Land Use and Land Cover Changes in Urban Areas Using Remote Sensing Images and GIS Analysis: The Case Study of Opole, Poland

Geosciences ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 312
Author(s):  
Barbara Wiatkowska ◽  
Janusz Słodczyk ◽  
Aleksandra Stokowska
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Agricultural Land ◽  
Urban Expansion ◽  
Landsat 8 ◽  
Rate Of Increase ◽  
Management Procedures

Urban expansion is a dynamic and complex phenomenon, often involving adverse changes in land use and land cover (LULC). This paper uses satellite imagery from Landsat-5 TM, Landsat-8 OLI, Sentinel-2 MSI, and GIS technology to analyse LULC changes in 2000, 2005, 2010, 2015, and 2020. The research was carried out in Opole, the capital of the Opole Agglomeration (south-western Poland). Maps produced from supervised spectral classification of remote sensing data revealed that in 20 years, built-up areas have increased about 40%, mainly at the expense of agricultural land. Detection of changes in the spatial pattern of LULC showed that the highest average rate of increase in built-up areas occurred in the zone 3–6 km (11.7%) and above 6 km (10.4%) from the centre of Opole. The analysis of the increase of built-up land in relation to the decreasing population (SDG 11.3.1) has confirmed the ongoing process of demographic suburbanisation. The paper shows that satellite imagery and GIS can be a valuable tool for local authorities and planners to monitor the scale of urbanisation processes for the purpose of adapting space management procedures to the changing environment.

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