scholarly journals Factors determining land use and transportation planning achievement in developing countries

2013 ◽  
Author(s):  
Y. A. Shamsul Harumain ◽  
A. Morimoto
Keyword(s):  
Land Use ◽  
Developing Countries ◽  
Transportation Planning

Integration of Land Use and Transportation Planning

1999 ◽  
Vol 34 (0) ◽  
pp. 163-168
Author(s):  
Takashi Shoyama ◽  
Kazuaki Miyamoto
Keyword(s):  
Land Use ◽  
Transportation Planning

The Use of Geographic Information Systems (GIS) in Land Resources Appraisal

1994 ◽  
Vol 23 (4) ◽  
pp. 249-255 ◽  
Author(s):  
W.G. Sombroek ◽  
J. Antoine
Keyword(s):  
Land Use ◽  
Developing Countries ◽  
Information Systems ◽  
Geographic Information Systems ◽  
Land Degradation ◽  
Geographic Information ◽  
Land Resource ◽  
Land Resources ◽  
Training Support ◽  
Village Communities

Nations, village communities and individual land users need to make choices about land use in order to support development without risk of land degradation. Computerized land information systems (LIS) based on geographic information systems (GIS) have emerged as powerful tools for generating maps and reports to inform such decisions. Recently, FAO has been developing GIS/LIS systems in linkage with its agroecological zoning (AEZ) and other models, and using them to tackle issues of land, food and people at global, national and subnational levels. They have been successfully developed for land resource management at different scales, but practical difficulties have been encountered in making them accessible to the casual user in most developing countries, due to scarcity of data and poor training support.

scholarly journals Impact of Urban Growth on Air Quality in Indian Cities Using Hierarchical Bayesian Approach

Atmosphere ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 517 ◽  
Author(s):  
Prakhar Misra ◽  
Ryoichi Imasu ◽  
Wataru Takeuchi
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Developing Countries ◽  
Air Quality ◽  
Bayesian Approach ◽  
Urban Growth ◽  
Chemical Transport ◽  
Pm 2.5 ◽  
Brick Kilns ◽  
Indian Cities

Several studies have found rising ambient particulate matter (PM 2.5 ) concentrations in urban areas across developing countries. For setting mitigation policies source-contribution is needed, which is calculated mostly through computationally intensive chemical transport models or manpower intensive source apportionment studies. Data based approach that use remote sensing datasets can help reduce this challenge, specially in developing countries which lack spatially and temporally dense air quality monitoring networks. Our objective was identifying relative contribution of urban emission sources to monthly PM 2.5 ambient concentrations and assessing whether urban expansion can explain rise of PM 2.5 ambient concentration from 2001 to 2015 in 15 Indian cities. We adapted the Intergovernmental Panel on Climate Change’s (IPCC) emission framework in a land use regression (LUR) model to estimate concentrations by statistically modeling the impact of urban growth on aerosol concentrations with the help of remote sensing datasets. Contribution to concentration from six key sources (residential, industrial, commercial, crop fires, brick kiln and vehicles) was estimated by inverse distance weighting of their emissions in the land-use regression model. A hierarchical Bayesian approach was used to account for the random effects due to the heterogeneous emitting sources in the 15 cities. Long-term ambient PM 2.5 concentration from 2001 to 2015, was represented by a indicator R (varying from 0 to 100), decomposed from MODIS (Moderate Resolution Imaging Spectroradiometer) derived AOD (aerosol optical depth) and angstrom exponent datasets. The model was trained on annual-level spatial land-use distribution and technological advancement data and the monthly-level emission activity of 2001 and 2011 over each location to predict monthly R. The results suggest that above the central portion of a city, concentration due to primary PM 2.5 emission is contributed mostly by residential areas (35.0 ± 11.9%), brick kilns (11.7 ± 5.2%) and industries (4.2 ± 2.8%). The model performed moderately for most cities (median correlation for out of time validation was 0.52), especially when assumed changes in seasonal emissions for each source reflected actual seasonal changes in emissions. The results suggest the need for policies focusing on emissions from residential regions and brick kilns. The relative order of the contributions estimated by this study is consistent with other recent studies and a contribution of up to 42.8 ± 14.1% is attributed to the formation of secondary aerosol, long-range transport and unaccounted sources in surrounding regions. The strength of this approach is to be able to estimate the contribution of urban growth to primary aerosols statistically with a relatively low computation cost compared to the more accurate but computationally expensive chemical transport based models. This remote sensing based approach is especially useful in locations without emission inventory.

scholarly journals Global estimates of carbon stock changes in living forest biomass: EDGARv4.3 – time series from 1990 to 2010

2012 ◽  
Vol 9 (8) ◽  
pp. 3437-3447 ◽  
Author(s):  
A. M. R. Petrescu ◽  
R. Abad-Viñas ◽  
G. Janssens-Maenhout ◽  
V. N. B. Blujdea ◽  
G. Grassi
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Developing Countries ◽  
Forest Biomass ◽  
Global Level ◽  
Tier 1 ◽  
The World ◽  
C Stock ◽  
Global Estimates ◽  
Stock Change

Abstract. While the Emissions Database for Global Atmospheric Research (EDGAR) focuses on global estimates for the full set of anthropogenic activities, the Land Use, Land-Use Change and Forestry (LULUCF) sector might be the most diverse and most challenging to cover consistently for all countries of the world. Parties to United Nations Framework Convention on Climate Change (UNFCCC) are required to provide periodic estimates of greenhouse gas (GHG) emissions, following the latest approved methodological guidance by the International Panel on Climate Change (IPCC). The current study aims to consistently estimate the carbon (C) stock changes from living forest biomass for all countries of the world, in order to complete the LULUCF sector in EDGAR. In order to derive comparable estimates for developing and developed countries, it is crucial to use a single methodology with global applicability. Data for developing countries are generally poor, such that only the Tier 1 methods from either the IPCC Good Practice Guide for Land Use, Land-Use Change and Forestry (GPG-LULUCF) 2003 or the IPCC 2006 Guidelines can be applied to these countries. For this purpose, we applied the IPCC Tier 1 method at global level following both IPCC GPG-LULUCF 2003 and IPCC 2006, using spatially coarse activity data (i.e. area, obtained combining two different global forest maps: the Global Land Cover map and the eco-zones subdivision of the Global Ecological Zone (GEZ) map) in combination with the IPCC default C stocks and C stock change factors. Results for the C stock changes were calculated separately for gains, harvest, fires (Global Fire Emissions Database version 3, GFEDv.3) and net deforestation for the years 1990, 2000, 2005 and 2010. At the global level, results obtained with the two sets of IPCC guidance differed by about 40 %, due to different assumptions and default factors. The IPCC Tier 1 method unavoidably introduced high uncertainties due to the "globalization" of parameters. When the results using IPCC 2006 for Annex I Parties are compared to other international datasets such as (UNFCCC, Food and Agriculture Organization of the United Nations (FAO)) or scientific publications, a significant overestimation of the sink emerges. For developing countries, we conclude that C stock change in forest remaining forest can hardly be estimated with the Tier 1 method especially for calculating the C losses, mainly because wood removal data are not separately available on harvesting or deforestation. Overall, confronting the IPCC GPG-LULUCF 2003 and IPCC 2006 methodologies, we conclude that IPCC 2006 suits best the needs of EDGAR and provide a consistent global picture of C stock changes from living forest biomass independent of country estimates.

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