A method to compare and improve land cover datasets: Application to the GLC-2000 and MODIS land cover products

This paper presents a methodology for the comparison of different land cover datasets and illustrates how this can be extended to create a hybrid land cover product. The datasets used in this paper are the GLC-2000 and MODIS land cover products. The methodology addresses: 1) the harmonization of legend classes from different global land cover datasets and 2) the uncertainty associated with the classification of the images. The first part of the methodology involves mapping the spatial disagreement between the two land cover products using a combination of fuzzy logic and expert knowledge. Hotspots of disagreement between the land cover datasets are then identified to determine areas where other sources of data such as TM/ETM images or detailed regional and national maps can be used in the creation of a hybrid land cover dataset

Detection of Deforestation Using Low Resolution Satellite Images in the Islands of Sumatra 2000-2012

In the last two decades, the international community has given great attention to the issues of deforestation and degradation. In Indonesia, these issues had been a very critical as they were related to the Indonesian government's commitment in reducing greenhouse gases by 2020 through the Reducing Emission from Deforestation and forest Degradation (REDD) mechanism. This paper describes the use of low resolution satellite imagery, i.e., MODIS (Moderate Resolution Imaging Spectroradiometer) for monitoring deforestation in Sumatra during the period of 2000-2012. The main objective of the study was to derive rapid forest and land cover change information from low resolution imageries in Sumatra between 2000 - 2012. This study used level 2 Terra MODIS (MOD13Q1) imageries, acquired in 2000, 2006 and 2012 as the main data source, where the 16-day composite imageries were derived from NAS

Assessing Land-Use Changes in European Territories: A Retrospective Study from 1990 to 2012

The need to understand what land use is has motivated the development of programmes that aims to identify it and quantify it—CORINE Land Cover (CLC) in 1985. From this official and open geodatabase—through the using of geographic information system (GIS) tools—the amount of area established for each land use has been identified in all the 28 member states of the EU. This mostly corresponds to agricultural and forestry uses. Between 1990 and 2012, it was possible to determine countries with variable land use models such as Finland, Latvia, Portugal and Spain—the rest of the states presenting stable land use models. Additionally, some countries are characterized by the predominance of one or two land uses. Contextually, the proposal aims to develop a retrospective study regarding the land-use changes in the EU territories from 1990 to 2012, through the available tools such as CLC

Land cover changes mapping in cameron highlands using high resolution satellite and unmanned aerial vehicle imageries

Agriculture and tourism are two important economic activities in the hilly area of Cameron Highlands, Pahang, Malaysia. Land opening for agriculture and construction of settlements and hotels to cater for tourism activities are rapidly and continuously ongoing in this area. However, improper planning of these activities has resulted in various environmental issues such as landslide hazards. This research is undertaken to assess the land use and land cover (LULC) changes occurred in the study area for a period of 12 years (2001-2013) using high resolution optical satellite images (IKONOS and QuickBird) and unmanned aerial vehicle (UAV) images from a fixed wing Helang. An object based classification technique was used to classify the satellite images and UAV images into seven LULC classes, namely, forest, agriculture, grass, bare land, urban, water body and areas affected by landslides. The results obtained from the classification technique were verified using land use maps of 2003, 2008 and 2015 that were obtained from the Department of Town and Rural Planning. The overall accuracy and Kappa Coefficient values (values in brackets) of the LULC classification are 86.67% (0.84), 83.89% (0.81), and 93.80% (0.93) for 2001, 2007 and 2013 respectively. Post classification change detection technique was applied in this study to identify LULC changes. Results of the classification show that the forest area decreased consistently from 2001 (196.08ha) to 2007 (180.73ha) and to 2013 (160.09ha). On the other hand, the built-up area, increased during the years from 47.77ha in 2001 to 58.25ha in 2007 and to 63.43ha in 2013. In these periods, a slight increase was noticed in the agriculture and grass lands, however, water bodies did not change much. In general, bare soil areas have only minor changes. Areas affected by landslides are detected in the UAV image and it covered an area of 3.66ha. In conclusion, this study show that the optical satellites and UAV images can be processed to produce accurate classification map, therefore useful for the local authorities to identify land cover changes, furthermore to monitor land encroachment activities and to reduce landslide hazards from occurring and to mitigate its effect

The EAGLE concept - A vision of a future European Land Monitoring Framework

Abstract. This paper describes the EAGLE concept, an object-oriented data model for land moni-toring. It highlights the background situation in the field of land monitoring, identifies the team in-volved, explains the technical and strategic considerations behind the concept, describes the cur-rent status of the harmonization and the developments made and outlines the future activities and requirements. After the structure and the content of the data model and matrix are explained, ex-amples are given on how to use the matrix. Besides its possible function as a semantic translation tool between different classification systems, it also can help to analyze class definitions to find semantic gaps, overlaps and inconsistencies and can serve as data model for new mapping initia-tives. On the long-term, the EAGLE concept aims at sketching a vision of a future integrated and harmonized European land monitoring system, which is designed to store all kinds of environmen-tally relevant information on the Earth´s surface, coming from both national and European data sources. Being still in the state of development, some first applications and test cases are under way. This paper also dedicates a chapter referring to the context between the concept and remote sensing in general as well as the relation between land monitoring and the principles of the Euro

Sentinel2GlobalLULC: A Sentinel-2 RGB image tile dataset for global land use/cover mapping with deep learning

Land-Use and Land-Cover (LULC) mapping is relevant for many applications, from Earth system and climate modelling to territorial and urban planning. Global LULC products are continuously developing as remote sensing data and methods grow. However, there still exists low consistency among LULC products due to low accuracy in some regions and LULC types. Here, we introduce Sentinel2GlobalLULC, a Sentinel-2 RGB image dataset, built from the spatial-temporal consensus of up to 15 global LULC maps available in Google Earth Engine. Sentinel2GlobalLULC v2.1 contains 194877 single-class RGB image tiles organized into 29 LULC classes. Each image is a 224 × 224 pixels tile at 10 × 10 m resolution built as a cloud-free composite from Sentinel-2 images acquired between June 2015 and October 2020. Metadata includes a unique LULC annotation per image, together with level of consensus, reverse geo-referencing, global human modification index, and number of dates used in the composite. Sentinel2GlobalLULC is designed for training deep learning models aiming to build precise and robust global or regional LULC maps.This work is part of the project “Thematic Center on Mountain Ecosystem & Remote sensing, Deep learning-AI e-Services University of Granada-Sierra Nevada” (LifeWatch-2019-10-UGR-01), which has been co-funded by the Ministry of Science and Innovation through the FEDER funds from the Spanish Pluriregional Operational Program 2014-2020 (POPE), LifeWatch-ERIC action line, within the Workpackages LifeWatch-2019-10-UGR-01 WP-8, LifeWatch-2019-10-UGR-01 WP-7 and LifeWatch-2019-10-UGR-01 WP-4. This work was also supported by projects A-RNM-256-UGR18, A-TIC-458-UGR18, PID2020-119478GB-I00 and P18-FR-4961. E.G. was supported by the European Research Council grant agreement n° 647038 (BIODESERT) and the Generalitat Valenciana, and the European Social Fund (APOSTD/2021/188). We thank the “Programa de Unidades de Excelencia del Plan Propio” of the University of Granada for partially covering the article processing charge

Sentinel2GlobalLULC: A Sentinel-2 RGB image tile dataset for global land use/cover mapping with deep learning

Land-Use and Land-Cover (LULC) mapping is relevant for many applications, from Earth system and climate modelling to territorial and urban planning. Global LULC products are continuously developing as remote sensing data and methods grow. However, there still exists low consistency among LULC products due to low accuracy in some regions and LULC types. Here, we introduce Sentinel2GlobalLULC, a Sentinel-2 RGB image dataset, built from the spatial-temporal consensus of up to 15 global LULC maps available in Google Earth Engine. Sentinel2GlobalLULC v2.1 contains 194877 single-class RGB image tiles organized into 29 LULC classes. Each image is a 224 × 224 pixels tile at 10 × 10 m resolution built as a cloud-free composite from Sentinel-2 images acquired between June 2015 and October 2020. Metadata includes a unique LULC annotation per image, together with level of consensus, reverse geo-referencing, global human modification index, and number of dates used in the composite. Sentinel2GlobalLULC is designed for training deep learning models aiming to build precise and robust global or regional LULC maps.Ministry of Science and Innovation through the FEDER funds from the Spanish Pluriregional Operational Program LifeWatch-2019-10-UGR-01LifeWatch-ERIC action line, within the Workpackages LifeWatch-2019-10-UGR-01 WP-8 LifeWatch-2019-10-UGR-01 WP-7 LifeWatch-2019-10-UGR-01 WP-4European Research Council (ERC)European Commission 647038Center for Forestry Research & Experimentation (CIEF) APOSTD/2021/188 A-RNM-256-UGR18 A-TIC-458-UGR18 PID2020-119478GB-I00 P18-FR-496

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

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 landscape

LULC Map Comparison: Comparison and validation of land use land cover maps derived from satellite imagery

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesThe technological evolution of remote sensing techniques has allowed for the ever-growing creation of land use land cover maps. Nowadays mapping entities from all around the globe are capable of producing their own products, be it for their region or country, or for a whole continent or even the whole world. However, this raises an issue regarding the comparison of these maps, in one sense direct comparison is difficult due to the lack of harmonization of the data, be it the nomenclature’s classification, or technical specifications of the imagery. In another sense, the creation of global cover maps, or continent-wide maps, hinges the ability to accurately classify LULC appropriately due to the complexity of land cover, often leaving specific regions of the map with a less accurate classification, even though the overall one is good. Throughout this study, five maps from five different mapping entities will be compared and evaluated, these maps are COSsim, ELC10, ESA Worldcover, ESRILC and S2GLC. The study area is Continental Portugal, and the main objective is to understand how the international mapping entities’ maps compare with the Portuguese map of COSsim, by observing nomenclature differences and accuracy scores. As well as understand what the impact in accuracy is, in European cover or World cover maps, when only analyzing them for the study area of Continental Portugal. The results obtained showed that most international maps proved to have a much smaller accuracy score for Continental Portugal, most of these even having a 20% to 30% drop in their overall accuracy. This research helps understand the necessity for the harmonization of nomenclatures, and at the same time investment necessary for national mapping entities to create their own more accurate maps

Land cover harmonization using Latent Dirichlet Allocation

Large-area land cover maps are produced to satisfy different information needs. Land cover maps having partial or complete spatial and/or temporal overlap, different legends, and varying accuracies for similar classes, are increasingly common. To address these concerns and combine two 30-m resolution land cover products, we implemented a harmonization procedure using a Latent Dirichlet Allocation (LDA) model. The LDA model used regionalized class co-occurrences from multiple maps to generate a harmonized class label for each pixel by statistically characterizing land attributes from the class co-occurrences. We evaluated multiple harmonization approaches: using the LDA model alone and in combination with more commonly used information sources for harmonization (i.e. error matrices and semantic affinity scores). The results were compared with the benchmark maps generated using simple legend crosswalks and showed that using LDA outputs with error matrices performed better and increased harmonized map overall accuracy by 6–19% for areas of disagreement between the source maps. Our results revealed the importance of error matrices to harmonization, since excluding error matrices reduced overall accuracy by 4–20%. The LDA-based harmonization approach demonstrated in this paper is quantitative, transparent, portable, and efficient at leveraging the strengths of multiple land cover maps over large areas