Research priorities in land use and land-cover change for the Earth System and Integrated Assessment Modelling

This special issue has highlighted recent and innovative methods and results that integrate observations and modelling analyses of regional to global aspect of biophysical and biogeochemical interactions of land-cover change with the climate system. Both the Earth System and the Integrated Assessment modeling communities recognize the importance of an accurate representation of land use and land-cover change to understand and quantify the interactions and feedbacks with the climate and socio-economic systems, respectively. To date, cooperation between these communities has been limited. Based on common interests, this work discusses research priorities in representing land use and land-cover change for improved collaboration across modelling, observing and measurement communities. Major research topics in land use and land-cover change are those that help us better understand (1) the interaction of land use and land cover with the climate system (e.g. carbon cycle feedbacks), (2) the provision of goods and ecosystem services by terrestrial (natural and anthropogenic) land-cover types (e.g. food production), (3) land use and management decisions and (4) opportunities and limitations for managing climate change (for both mitigation and adaptation strategies

Future Land-use and Land-cover Scenarios for Mapping Flood-prone Areas in Pato Branco City, Brazil

Urban flooding is the most common type of disaster and the one that hit people most. Unplanned urbanization processes increase the recurrence of these events due to soil impermeabilization. Thus, land-use and land-cover is an important factor for urban flood research. Besides, mapping flood-prone areas has been an alternative for disaster prevention and urban planning. However, the use of future land-use and land-cover scenarios for flood mapping is a factor that still requires investigation. The study that is being developed by the authors of this paper aims to identify flood-prone areas in the upper third of the Ligeiro River basin in the city of Pato Branco, Parana, Brazil. For this purpose, this research makes use of the GIS-AHP integration, considering a current scenario and future land-use and land-cover scenarios. Therefore, the objective of the present study is to construct possible land-use and land-cover scenarios, according to municipal legislation, that could serve as a basis for mapping flood-prone areas. Two scenarios were built using Geographic Information Systems software. This tool proved to be efficient in the elaboration of maps and land representation. Pato Branco already has a history of flooding with the current scenario of land-use and land-cover. With future land-use and land-cover scenarios, it is possible to verify the influence of urban sprawl on urban flooding. Keywords: Land-use and land-cover (LULC), Floods, Geographic Information Systems (GIS), Analytic Hierarchy Process (AHP

Land use and land cover change in Ameleke Watershed, South Ethiopia

This study investigated the land use and land cover change at Ameleke watershed, middle catchment of Gidabo River, South Ethiopia that occurred from 1986-2006. Landsat TM of 1986, Landsat ETM+ of 2000 and SPOT of 2006 were used to produce land use and land cover maps of the watershed. A pixel-based supervised image classification through decision rule of maximum likelihood classifier algorithm was used to map land use and land covers on ERDAS Imagine 8.6.  For land use and land cover maps of 1986, 2000 and 2006, error matrixes were produced and have an accuracy assessment of 80%, 85% and 85.71% respectively. Focused group discussions and key informant interviews were also used for land use and land cover reconstruction. The result showed from 1986 up to 2006, cropland and mixed cover increased from 23.33% to 31% and 7.26 to 15.68% of the watershed respectively. In contrast grass lands and shrub lands decreased from 25.9% to 14.96% and 30.3% and 24.25% of the watershed respectively in 1986 to 2006. There was also an increasing trend on agroforestry while there was a decreasing trend on riverine forests. This study recommends further assessment and monitoring of spatial and temporal based land use and land cover change at homestead level having high resolution satellite images. Keywords: land use and land cover change, Ameleke watershe

Pressions Anthropiques Et Dynamique D’occupation Des Terres Dans Le Terroir De Ziguéna, Zone Cotonnière Du Mali

In Ziguéna terroir, the combined effects of drought and anthropogenic actions led to the widespread degradation of vegetation cover and of land. This work aimed at characterizing the dynamics of land use and land cover in relation to anthropogenic pressures in Ziguéna terroir. The methodology consisted in identifying and characterizing land use and land cover classes. Landsat images for the years 1986 and 2013 and population data for the years 1987, 1998 and 2009 were used. Visual interpretation of the images and post-classification comparison of the results were used to generate land use and land cover classes and calculate their rate of change. The results reveal that the natural vegetation has lost 55% of its original coverage (1514.3 ha) between 1987 and 2013. During the same period, the agricultural area increased by 47% (1608 ha). The projection of land use and land cover classes predicted an increase of agricultural land of about 34.60% by year 2030 compared to its coverage of year 2013 (+1191.03 ha) at the expense of natural vegetation which will lose about 40.63% of its coverage (-1121.70 ha). The dynamics of agricultural land is strongly linked to population growth rates with a correlation coefficient r equal to 0.99. This confirms a strong anthropogenic influence on land use and land cover dynamics. The results show the usefulness of remote sensing for mapping land use and land cover. Nevertheless it would be interesting to take into account the socioeconomic aspects for proper understanding of the dynamics

Research priorities in land use and land-cover change for the Earth system and integrated assessment modelling

Copyright © 2010 Royal Meteorological Society and Crown Copyright.This special issue has highlighted recent and innovative methods and results that integrate observations and modelling analyses of regional to global aspect of biophysical and biogeochemical interactions of land-cover change with the climate system. Both the Earth System and the Integrated Assessment modeling communities recognize the importance of an accurate representation of land use and land-cover change to understand and quantify the interactions and feedbacks with the climate and socio-economic systems, respectively. To date, cooperation between these communities has been limited. Based on common interests, this work discusses research priorities in representing land use and land-cover change for improved collaboration across modelling, observing and measurement communities. Major research topics in land use and land-cover change are those that help us better understand (1) the interaction of land use and land cover with the climate system (e.g. carbon cycle feedbacks), (2) the provision of goods and ecosystem services by terrestrial (natural and anthropogenic) land-cover types (e.g. food production), (3) land use and management decisions and (4) opportunities and limitations for managing climate change (for both mitigation and adaptation strategies)

Small area estimation for land use and land cover

Small Area Estimation (SAE) is a part of statistical science that combines survey sampling and inference of finite populations with statistical modeling. The main objective of this paper is to analyze and test the implementation of different types of estimators of small domains in order to improve the quality of the estimates produced within the framework of the Farm Structure Survey (FSS) at NUTS III level. Under the EUROSTAT Land Use and Cover Area Statistical Survey (LUCAS) project, this is a fundamental tool for environmental studies, forestry and agricultural resource planning

Detection and Mapping of Land Use and Land Cover Classes of a Developing City in Southeastern Region of Nigeria, using Multi-band Digital Remotely-sensed Data

Land use and land cover dynamics has been commonplace around Owerri and environs. Landsat TM 86 and ETM+ 2000 were used to detect and map these changes. The imageries were mapped at the scales of 1:250,000 and 1:150,000 using ILWIS Academic 3.0 GIS software. The result showed significant shift in the aggregate land use and land cover class due to naturaland anthropogenic forcing agents. Forest vegetation class had the largest coverage on the land use and land cover maps of 1986 and 2000. The bare/eroded surfaces class gave the highest PAVM value of 65.7% followedby water body of 44.9%. This implies that the LU and LC underwent massive  transformation and change in the period of study. These types of LU and LCand the spate of dynamics must be considered in the preparation of development plans for cities in the region

Climate-Relevant Land Use and Land Cover Change Policies

Both observational and modeling studies clearly demonstrate that land-use and land-cover change (LULCC) play an important biogeophysical and biogeochemical role in the climate system from the landscape to regional and even continental scales. Without comprehensively considering these impacts, an adequate response to the threats posed by human intervention into the climate system will not be adequate. Public policy plays an important role in shaping local- to national-scale land-use practices. An array of national policies has been developed to influence the nature and spatial extent of LULCC. Observational evidence suggests that these policies, in addition to international trade treaties and protocols, have direct effects on LULCC and thus the climate system. However, these policies, agreements, and protocols fail to adequately recognize these impacts. To make these more effective and thus to minimize climatic impacts, we propose several recommendations: 1) translating international treaties and protocols into national policies and actions to ensure positive climate outcomes; 2) updating international protocols to reflect advancement in climate–LULCC science; 3) continuing to invest in the measurements, databases, reporting, and verification activities associated with LULCC and LULCC-relevant climate monitoring; and 4) reshaping Reducing Emissions from Deforestation and Forest Degradation+ (REDD+) to fully account for the multiscale biogeophysical and biogeochemical impacts of LULCC on the climate system

A Framework for Evaluating Land Use and Land Cover Classification Using Convolutional Neural Networks

Analyzing land use and land cover (LULC) using remote sensing (RS) imagery is essential for many environmental and social applications. The increase in availability of RS data has led to the development of new techniques for digital pattern classification. Very recently, deep learning (DL) models have emerged as a powerful solution to approach many machine learning (ML) problems. In particular, convolutional neural networks (CNNs) are currently the state of the art for many image classification tasks. While there exist several promising proposals on the application of CNNs to LULC classification, the validation framework proposed for the comparison of different methods could be improved with the use of a standard validation procedure for ML based on cross-validation and its subsequent statistical analysis. In this paper, we propose a general CNN, with a fixed architecture and parametrization, to achieve high accuracy on LULC classification over RS data from different sources such as radar and hyperspectral. We also present a methodology to perform a rigorous experimental comparison between our proposed DL method and other ML algorithms such as support vector machines, random forests, and k-nearest-neighbors. The analysis carried out demonstrates that the CNN outperforms the rest of techniques, achieving a high level of performance for all the datasets studied, regardless of their different characteristics.Ministerio de Economía y Competitividad TIN2014-55894-C2-1-RMinisterio de Economía y Competitividad TIN2017-88209-C2-2-

Analysis of urban land use and land cover changes: a case of study in Bahir Dar, Ethiopia

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial TechnologiesThe high rate of urbanization coupled with population growth has caused changes in land use and land cover in Bahir Dar, Ethiopia. Therefore, understanding and quantifying the spatio- temporal dynamics of urban land use and land cover changes and its driving factors is essential to put forward the right policies and monitoring mechanisms on urban growth for decision making. Thus, the objective of this study was to analyze land use and land cover changes in Bahir Dar area, Ethiopia by applying geospatial and land use change modeling tools. In order to achieve this, satellite data of Landsat TM for 1986 and ETM for 2001 and 2010 have been obtained and preprocessed using ArcGIS. The Maximum Liklihood Algorithm of Supervised Classification has been used to generate land use and land cover maps. For the accuracy of classified land use and land cover maps, a confusion matrix was used to derive overall accuracy and results were above the minimum and acceptable threshold level. The generated land cover maps have been run with Land Change Modeler for quantifying land use and land cover changes, to examine land use transitions between land cover classes, to identify gain and losses of built up areas in relation to other land cover classes and to asses spatial trend of built up areas. Finally, Land Change Modeler has been run to model land use and land cover changes in Bahir Dar area and to predict future urban land use changes. To achieve this, four model variables that explain urban growth and six land cover transitions were incorporated in the modeling process. Multi-layer perceptron neural network was used to model the transition potential maps and achieved an accuracy of 61%. This result was acceptable to make actual prediction using Markov chain analysis for year 2010. Validation results showed that the model (Land Change Modeler) had a lower accuracy in simulating changes for the year 2010. Generally, the results of this study have shown that there was an increased expansion of built up areas in the last 25 years from 1.5% in 1986 to 4.1 % in 2001 and 9.4% in 2010 at the expense of agricultural areas. The spatial trend of built up areas also showed that there was a growing trend in the western part of Bahir Dar relative to other directions. Therefore, the findings of this study could provide as decision making for urban planning