LAND USE DATASET COLLECTION AND PUBLICATION BASED ON LUCAS AND HILUCS

Abstract Spatial data have become very important phenomena within the last decade in Europe due to a strong support from the political spectrum with regard to related legislation and resulting in financial support to several research, educational, and enlargement projects. INSPIRE (Infrastructure for Spatial Information in the European Community) Directive indeed defines the principles for the harmonization of spatial data infrastructure in the European community, including Land Use and Land Cover data themes. INSPIRE defines a methodology on how to transform datasets to common data models, but it does not cover the process of data collection and update, because it is out of its scope. Evaluation of the Land Use dataset derived from remote sensing products complemented by fieldworks has been realized since 2006 by Eurostat within the LUCAS (Land Use and Cover Area frame Survey) project. The work presented in this paper follows the LUCAS fieldwork methodology, which was applied during the fieldwork in July 2014 in the City of Zagreb (Croatia), to use at the local (municipal) geoportal level. The surveying groups collected point features with the following data type attributes: Land Use codes defined by HILUCS (Hierarchical INSPIRE Land Use Classification System) and optional Land Cover codes defined by LUCAS classification. In addition, photographs representing the observed areas were collected by cameras embedded in the mobile GIS platforms. An update of original topological layer was performed and Web GIS components for sharing the newly developed datasets were implemented. The results presented provide a suitable proposal for fieldworks methodology and updates of a land use database in line with the INSPIRE directive applicable at a local spatial data infrastructure level

Metodologia para monitoramento agrícola com emprego de imagens orbitais e amostragem estatística.

Abstract: Brazil still has not a system based in earth observation images to map and monitoring the aimed crops in large scale. Many programs have been made with Landsat-like and MODIS data to monitoring crops in Brazil, but only the CANASAT has worked in operation level. The clouds and unit products (UPS) size in Brazil, have not permitted the use these data to correct classify maize, sugarcane and soybean. The use of sample frame and visual pixels classification with multitemporal OLI images could be a solution to monitor these three crops. The goal of this study was evaluate the sample frame performance to maize (c1), soybean (c2) and sugarcane (c3) in Paraná (PR) State using OLI images and pixel visual classification. Were used four periods to classify 20.000 random pixels over all the Paraná State: (p1) Nov/Dec, (p2) Jan/Feb, (p3) Mar/Apr and (p4) May/Jun. Each period was compost for 4 OLI images, and 5.000 pixels were classified as c1, c2, c3 and others. IBGE data from 2012 were used to determinate the number of random pixels in each PR mesoregion/stratum. The Stratified Random Sample by Maximum Corrected (SRSMC) showed good performance for tree crops. The coefficient of variation (CV) for each period ranged of 1.42 for soybean in p2 until 16.87 for soybean in p4. The sugarcane CVs have not varied ( and maize CV had the minimum value (2.16) in p4

Forest cover estimation in Ireland using radar remote sensing: a comparative analysis of forest cover assessment methodologies

Quantification of spatial and temporal changes in forest cover is an essential component of forest monitoring programs. Due to its cloud free capability, Synthetic Aperture Radar (SAR) is an ideal source of information on forest dynamics in countries with near-constant cloud-cover. However, few studies have investigated the use of SAR for forest cover estimation in landscapes with highly sparse and fragmented forest cover. In this study, the potential use of L-band SAR for forest cover estimation in two regions (Longford and Sligo) in Ireland is investigated and compared to forest cover estimates derived from three national (Forestry2010, Prime2, National Forest Inventory), one pan-European (Forest Map 2006) and one global forest cover (Global Forest Change) product. Two machine-learning approaches (Random Forests and Extremely Randomised Trees) are evaluated. Both Random Forests and Extremely Randomised Trees classification accuracies were high (98.1–98.5%), with differences between the two classifiers being minimal (<0.5%). Increasing levels of post classification filtering led to a decrease in estimated forest area and an increase in overall accuracy of SAR-derived forest cover maps. All forest cover products were evaluated using an independent validation dataset. For the Longford region, the highest overall accuracy was recorded with the Forestry2010 dataset (97.42%) whereas in Sligo, highest overall accuracy was obtained for the Prime2 dataset (97.43%), although accuracies of SAR-derived forest maps were comparable. Our findings indicate that spaceborne radar could aid inventories in regions with low levels of forest cover in fragmented landscapes. The reduced accuracies observed for the global and pan-continental forest cover maps in comparison to national and SAR-derived forest maps indicate that caution should be exercised when applying these datasets for national reporting

Geographical downscaling of outputs provided by an economic farm model calibrated at the regional level

International audienceThere is a strong need for accurate and spatially referenced information regarding policy making and model linkage. This need has been expressed by land users, and policy and decision makers in order to estimate both spatially and locally the impacts of European policy (like the Common Agricultural Policy) and/or global changes on farm-groups. These entities are defined according to variables such as altitude, economic size and type of farming (referring to land uses). European farm-groups are provided through the Farm Accountancy Data Network (FADN) as statistical information delivered at regional level. The aim of the study is to map locally farm-group probabilities within each region. The mapping of the farm-groups is done in two steps: (1) by mapping locally the co-variables associated to the farm-groups, i.e. altitude and land uses; (2) by using regional FADN data as a priori knowledge for transforming land uses and altitude information into farm-groups location probabilities within each region. The downscaling process focuses on the land use mapping since land use data are originally point information located every 18 km. Interpolation of land use data is done at 100 m by using co-variables like land cover, altitude, climate and soil data which are continuous layers usually provided at fine resolution. Once the farm-groups are mapped, European Policy and global changes scenarios are run through an agro-economic model for assessing environmental impacts locally

State and evolution of the African rainforests between 1990 and 2010

This paper presents a 2005 map of Africa’s rainforests with new levels of spatial and thematic detail, being derived from 250m resolution MODIS data, and having an overall accuracy of 84%. A systematic sample of Landsat images (with supplemental data from equivalent platforms to fill sample gaps) is used to produce a consistent assessment of deforestation between 1990, 2000 and 2010 for West Africa, Central Africa and Madagascar. Net deforestation is estimated at 0.28% yr-1 for the period 1990-2000 and 0.14% yr-1 for the period 2000-2010. West Africa and Madagascar exhibit a much higher deforestation rate than the Congo Basin. Based on a simple analysis of the variance over the Congo Basin, we show that expanding agriculture and increasing fuelwood demands are key drivers of deforestation while well-controlled timber exploitation programmes have little or no direct influence on forest-cover reduction at present. Rural and urban population concentrations and fluxes are identified as strong underlying causes of deforestation in this study.JRC.H.5-Land Resources Managemen

Cloud cover assessment for operational crop monitoring systems in tropical areas.

Abstract: The potential of optical remote sensing data to identify, map and monitor croplands is well recognized. However, clouds strongly limit the usefulness of optical imagery for these applications. This paper aims at assessing cloud cover conditions over four states in the tropical and sub-tropical Center-South region of Brazil to guide the development of an appropriate agricultural monitoring system based on Landsat-like imagery. Cloudiness was assessed during overlapping four months periods to match the typical length of crop cycles in the study area. The percentage of clear sky occurrence was computed from the 1 km resolution MODIS Cloud Mask product (MOD35) considering 14 years of data between July 2000 and June 2014. Results showed high seasonality of cloud occurrence within the crop year with strong variations across the study area. The maximum seasonality was observed for the two states in the northern part of the study area (i.e., the ones closer to the Equator line), which also presented the lowest averaged values (15%) of clear sky occurrence during the main (summer) cropping period (November to February). In these locations, optical data faces severe constraints for mapping summer crops. On the other hand, relatively favorable conditions were found in the southern part of the study region. In the South, clear sky values of around 45% were found and no signi?cant clear sky seasonality was observed. Results underpin the challenges to implement an operational crop monitoring system based solely on optical remote sensing imagery in tropical and sub-tropical regions, in particular if short-cycle crops have to be monitored during the cloudy summer months. To cope with cloudiness issues, we recommend the use of new systems with higher repetition rates such as Sentinel-2. For local studies, Unmanned Aircraft Vehicles(UAVs) might be used to augment the observing capability. Multi-sensor approaches combining optical and microwave data can be another option. In cases where wall-to-wall maps are not mandatory, statistical sampling approaches might also be a suitable alternative for obtaining useful crop area information