1,720,965 research outputs found
Crowdsourcing for deforestation detection in the Amazon
Full text linkEvery year, deforestation results in the loss of wide stretches of forest which is worsening the state of air quality, biodiversity, indigenous cultures, climate, meteorological conditions, etc. According to the Monitoring of the Andean Amazon Project (MAAP), roughly 20 million hectares of land were lost due to deforestation in 2020. To address the issue of deforestation, this study proposes a derivation of the deforestation risk model to target the spread of deforestation, which is the first step towards its prevention. The region of interest - North West of Mato Grosso, Brazil - was selected based on two characteristics: it is a deforestation hotspot according to MAAP and it comprises 4 indigenous lands. The sequence for developing the risk model comprises reference information collection, information cleaning, classification, postprocessing, and change detection. In a crowdsourcing mapathon, reference data were gathered, and they were refined in an iterative process using existing land cover maps and photo interpretation. Google Earth Engine and the Random Forest algorithm were used to classify Sentinel-2 imagery for 2019 and 2020. The results obtained are land cover maps from 2019 and 2020 and land cover change, and the risk model. The results are not demonstrating intensive deforestation in the region of interest, however, the deforestation appears to be systematic in two subregions, indicating that it has the potential to spread. An additional concern in the case of these subregions is their proximity to the indigenous land
INTER-COMPARISON OF THE GLOBAL LAND COVER MAPS IN AFRICA SUPPLEMENTED BY SPATIAL ASSOCIATION OF ERRORS
Full text linkRecent advances in Earth Observations supported development of high-resolution land cover (LC) maps on a large-scale. This is an important step forward, especially for developing countries, which experienced problems in the past due to absence of reliable LC information. Nevertheless, increasing number of LC products is imposing additional validation workload to confirm their quality. In this paper inter-comparison of two recent LC products (GlobeLand30 and S2 prototype LC 20m map of Africa) for country of Rwanda in Africa was done. It is a way to facilitate validation by identifying the areas with higher probability of error. Specific approach of comparison of single pixel of one map with multiple pixels of another map provided confusion matrix and sub-pixel agreement table. In this work, accuracy indexes based on the confusion matrix were computed as a measure of similarity between the two maps. Furthermore, Moran’s I index was computed for estimation of spatial association of the pixels in disagreement. Also, total disagreement, as well as disagreement of particularly confused classes was visualised to analyse their spatial distribution. The results are showing that similarity of the two maps is about 66%. Disagreements are spatially associated and the most evident in the eastern and north-western part of the area of interest. This coincides also with the distribution of the two most confused classes Wetland and Shrubland. The results delineate areas of inconsistency between the two maps, and therefore areas where careful accuracy analysis are needed
Benchmarking of high-resolution land cover maps in Africa
Full text linkThis paper addresses the issue of increased validation demands due to growth in the production of land cover (LC) maps, especially those with large coverage and high-resolution. The inter-comparison of two high-resolution LC (HRLC) maps – GlobeLand30 for the year 2015 (GL30-2015) and S2 Prototype LC 20m map of Africa for 2016 (CCI Africa Prototype) – was done to estimate the degree to which they share the information, as this can serve as a benchmark of their accuracy. Since the two maps compared are independently classified, there is a higher probability that areas, where they share information, are correctly classified. CCI Africa Prototype and GL30- 2015 have not been yet validated for the whole of Africa and therefore benchmark accuracy can be used to better design the validation and to make it more efficient. Based on the pixel-by-pixel comparison of GL30-2015 and CCI Africa Prototype, the error matrix and accuracy indexes (Overall, User’s, and Producer’s accuracy) were derived. Overall accuracy on the continent level is estimated to be around 66%, which is not considered satisfactory. The low value of overall accuracy is mostly due to the low accuracy of classes Shrubland, Wetland, and Permanent ice and snow, as their User’s and Producer’s accuracies are below 0.4. On the opposite, benchmark accuracy is fairly high for Forest (0.68), Water bodies (0.86), and Bareland (0.93). Nevertheless, class benchmark accuracies are different from country to country, such as the Overall accuracy. Benchmark accuracy was not estimated for Cultivated, Grassland, and Artificial surface classes due to the large difference between User’s and Producer’s accuracies
SEMI-AUTOMATED PRODUCTION AND FILTERING OF SATELLITE DERIVED WATER QUALITY PARAMETERS
Full text linkThis paper describes the semi-automated procedure implemented for the production of Water Quality Parameters (WQP) maps obtained processing Sentinel-3 and Landsat-8 imagery in the framework of SIMILE Interreg project. The processing chain includes the use of the C2RCC processor to obtain Chl-a (Chlorophyll-a) and TSM (Total Suspended Matter) and the Barsi method to produce maps of water surface temperature. The maps were filtered to exclude anomalous values due for example to clouds, water reflection (such as glint), or mixed pixels and compared to in-situ data. The filtering included an outlier rejection performed with the 36 rule. The values singled out as local anomalies where checked with respect to possible local behaviours, such as the presence of very small gulfs and inflow/outflow streams and providing guidelines with visual examples, to support the operator. The idea of a procedure as much as possible automated and guided is to foster the WQP maps production after the end of SIMILE project
Extending accuracy assessment procedures of global coverage land cover maps through spatial association analysis
Full text linkHigh-resolution land cover maps are in high demand for many environmental applications. Yet, the information they provide is uncertain unless the accuracy of these maps is known. Therefore, accuracy assessment should be an integral part of land cover map production as a way of ensuring reliable products. The traditional accuracy metrics like Overall Accuracy and Producer's and User's accuracies – based on the confusion matrix – are useful to understand global accuracy of the map, but they do not provide insight into the possible nature or source of the errors. The idea behind this work is to complement traditional accuracy metrics with the analysis of error spatial patterns. The aim is to discover errors underlying features which can be later employed to improve the traditional accuracy assessment. The designed procedure is applied to the accuracy assessment of the GlobeLand30 global land cover map for the Lombardy Region (Northern Italy) by means of comparison with the DUSAF regional land cover map. Traditional accuracy assessment quantified the classification accuracies of the map. Indeed, critical errors were pointed out and further analyses on their spatial patterns were performed by means of the Moran's I indicator. Additionally, visual exploration of the spatial patterns was performed. This allowed describing possible sources of errors. Both software and analysis strategies were described in detail to facilitate future improvement and replication of the procedure. The results of the exploratory experiments are critically discussed in relation to the benefits that they potentially introduce into the traditional accuracy assessment procedure
Crowdsourcing water quality with the simile app
Full text linkThis paper aims at presenting the application for lake water quality monitoring which has been developed in the framework of SIMILE (Informative System for the Integrated Monitoring of Insubric Lakes and their Ecosystems) Interreg Italy-Switzerland project. The objective of SIMILE project is to facilitate the monitoring of the Maggiore, Como, and Lugano lakes through the integration of different techniques: in situ monitoring with buoys, remote sensing and citizen science. A mobile application has been designed in agreement with the project partners, who are also actors working for lake quality monitoring, such as CNR (Italian National Research Council) and ARPA (Agency for prevention and environmental protection). The developed application allows to collect data over the area of interest, such as pictures and parameters which can be acquired by visual inspection as well as with appropriate tools, depending on the user typology. The application has then been implemented with open source software to foster its use also for other projects with similar goals. In the paper, the design choices, the architecture and the implementation details are described
High-resolution land cover classification: cost-effective approach for extraction of reliable training data from existing land cover datasets
Full text linkThere has been a significant increase in the availability of global high-resolution land cover (HRLC) datasets due to growing demand and favorable technological advancements. However, this has brought forth the challenge of collecting reference data with a high level of detail for global extents. While photo-interpretation is considered optimal for collecting quality training data for global HRLC mapping, some producers of existing HRLCs use less trustworthy sources, such as existing land cover at a lower resolution, to reduce costs. This work proposes a methodology to extract the most accurate parts of existing HRLCs in response to the challenge of providing reliable reference data at a low cost. The methodology combines existing HRLCs by intersection, and the output represents a Map Of Land Cover Agreement (MOLCA) that can be utilized for selecting training samples. MOLCA's effectiveness was demonstrated through HRLC map production in Africa, in which it generated 48,000 samples. The best classification test had an overall accuracy of 78%. This level of accuracy is comparable to or better than the accuracy of existing HRLCs obtained from more expensive sources of training data, such as photo-interpretation, highlighting the cost-effectiveness and reliability potential of the developed methodology in supporting global HRLC production
REVIEW OF HIGH-RESOLUTION GLOBAL LAND COVER
Full text linkThe land cover detection on our planet at high spatial resolution has a key role in many scientific and operational applications, such as climate modeling, natural resources management, biodiversity studies, urbanization analyses and spatial demography. Thanks to the progresses in Remote Sensing, accurate and high-resolution land cover maps have been developed over the last years, aiming at detecting the spatial resolution of different types of surfaces. In this paper we propose a review of the high-resolution global land cover products developed through Earth Observation technologies. A series of general information regarding imagery and data used to produce the map, the procedures employed for the map development and for the map accuracy assessment have been provided for every dataset. The land cover maps described in this paper concern the global distribution of settlements (Global Urban Footprint, Global Human Settlement Built-Up, World Settlement Footprint), water (Global Surface Water), forests (Forest/Non-forest, Tree canopy cover), and a two land cover maps describing world in 10 generic classes (GlobeLand30 and Finer Resolution Observation and Monitoring of Global Land Cover). The advantages and shortcomings of these maps and of the methods employed to produce them are summarized and compared in the conclusions
A FREE AND OPEN SOURCE TOOL TO ASSESS THE ACCURACY OF LAND COVER MAPS: IMPLEMENTATION AND APPLICATION TO LOMBARDY REGION (ITALY)
Full text linkThe availability of thematic maps has significantly increased over the last few years. Validation of these maps is a key factor in assessing their suitability for different applications. The evaluation of the accuracy of classified data is carried out through a comparison with a reference dataset and the generation of a confusion matrix from which many quality indexes can be derived. In this work, an ad hoc free and open source Python tool was implemented to automatically compute all the matrix confusion-derived accuracy indexes proposed by literature. The tool was integrated into GRASS GIS environment and successfully applied to evaluate the quality of three high-resolution global datasets (GlobeLand30, Global Urban Footprint, Global Human Settlement Layer Built-Up Grid) in the Lombardy Region area (Italy). In addition to the most commonly used accuracy measures, e.g. overall accuracy and Kappa, the tool allowed to compute and investigate less known indexes such as the Ground Truth and the Classification Success Index. The promising tool will be further extended with spatial autocorrelation analysis functions and made available to researcher and user community
Lake water quality monitoring tools
Full text linkLakes as ecosystems provide many goods and services. To benefit from them in long term we must assure sustainable management. SIMILE (informative System for the Integrated Monitoring of Insubric Lakes and their Ecosystems) project is focused on developing efficient monitoring of lake water quality since it gives the critical input for adequate management. The lakes of interest for SIMILE are the Insubric lakes Como, Lugano, and Maggiore. The paper is focused on describing which tools are used in the SIMILE project to exploit different sources of lake water quality data: in-situ high-frequency monitoring (HFM) through sensors, satellite observations, and data collected by citizens. Even though the paper is focused on the SIMILE project, and thus on tools and procedures for the Insubric lakes, it can serve as an example for other lakes too, especially because the tools developed in the project, such as a collaborative platform for sharing satellite-derived water quality parameters, and mobile application and web administrator interface for citizen science, are free and open-source, they can be easily adapted if needed. Moreover, the procedures for the processing of data coming from different sources are based on free (and often also open source) software and are well documented. The tools and procedures described in this paper might be a foundation for similar practice for lakes worldwide, and thus a step forward the 6th Sustainable Development Goal (SDG) of the United Nations (“Ensure availability and sustainable management of water and sanitation for all”)
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