Challenges in using land use and land cover data for global change studies

Abstract

Land use and land cover data play a central role in climate change assessments. These data originate from different sources and inventory techniques. Each source of land use/cover data has its own domain of applicability and quality standards. Often data are selected without explicitly considering the suitability of the data for the specific application, the bias originating from data inventory and aggregation, and the effects of the uncertainty in the data on the results of the assessment. Uncertainties due to data selection and handling can be in the same order of magnitude as uncertainties related to the representation of the processes under investigation. While acknowledging the differences in data sources and the causes of inconsistencies, several methods have been developed to optimally extract information from the data and document the uncertainties. These methods include data integration, improved validation techniques and harmonization of classification systems. Based on the data needs of global change studies and the data availability, recommendations are formulated aimed at optimal use of current data and focused efforts for additional data collection. These include: improved documentation using classification systems for land use/cover data; careful selection of data given the specific application and the use of appropriate scaling and aggregation methods. In addition, the data availability may be improved by the combination of different data sources to optimize information content while collection of additional data must focus on validation of available data sets and improved coverage of regions and land cover types with a high level of uncertainty. Specific attention in data collection should be given to the representation of land management (systems) and mosaic landscapes.

The representation of landscapes in global scale assessments of environmental change

“…While land cover data are available at even higher resolutions, a further increase in spatial resolution would lead to high demands on computational capacity and a poor fit with other data that are not available at higher spatial resolutions. Many of the physical and socioeconomic data that are used as drivers of land change, or data needed to assess impacts of land change on environmental indicators, are limited in their spatial resolution (Verburg et al 2011b). Recent advances in the development of such datasets may move the possibilities to increase spatial resolution forward (Robinson et al 2007;Verburg et al 2011a).…”

Avaliação de técnicas de classificação digital de imagens landsat em diferentes padrões de cobertura da terra em Rondônia

“…Para adequada avaliação das diferentes técnicas de classificação, quando possível, devem ser considerados os seguintes fatores: (a) rigoroso tratamento do processo de registro e ortorretificação de imagens e correções atmosféricas (STEININGER, 2000;LU et al, 2004;ROBIN et al, 2007); (b) seleção criteriosa de dados/sensores, dada uma específica aplicação -diferentes sensores possuem diferentes resoluções espacial, temporal e espectral que influenciam os resultados e análises (FOODY et al, 1997;LU et al, 2004;VERBURG et al, 2011); (c) a qualidade e quantidade dos conjuntos de dados de treinamento para classes de interesse a serem mapeadas (LU et al, 2003;LU et al, 2004;VERBURG et al, 2011); as variações do sensor entre as passagens do satélite -que influenciam na variabilidade espectral de determinada classe de uso (LU et al, 2004;VERBURG et al, 2011); (e) conhecimento e experiência da dinâmica espacial e temporal de ocupação do território analisado (LU et al, 2004); (f) uso de escala de análise adequada (LU et al, 2004;VERBURG et al, 2011); e (g) agregação/ integração de diferentes métodos de classificação (FOODY et al, 1997;LU et al, 2004;ALVES et al, 2009;VERBURG et al, 2011). A seleção do método de classificação de imagens, muitas vezes, depende da diferenciação geográfica da área de estudo ou de interesse, sendo necessário iniciá-la pelo levantamento de dados disponíveis e, sobretudo, pela adequação desses dados ao problema sob investigação na área de estudo.…”

Challenges in developing regionalized characterization factors in land use impact assessment: impacts on ecosystem services in case studies of animal protein production in Sweden

“…However, gradually, data availability increases, whether it is measured, remotely sensed, modeled, or open source with multiple spatial and temporal resolutions (such as Hijmans et al 2005, Jarvis et al 2008, and Shangguan et al 2014. While this development is beneficial, it introduces another challenge when it comes to combining data from different sources: datasets with similar resolutions can display significant variations in values associated with different methods to derive data (Herold et al 2006;Verburg et al 2011). It is important to ensure consistency when using data from different sources and to communicate the uncertainties that may arise from any inconsistencies.…”