Point-of-Interest (POI) Data Validation Methods: An Urban Case Study

Point-of-interest (POI) data from map sources are increasingly used in a wide range of applications, including real estate, land use, and transport planning. However, uncertainties in data quality arise from the fact that some of this data are crowdsourced and proprietary validation workflows lack transparency. Comparing data quality between POI sources without standardized validation metrics is a challenge. This study reviews and implements the available POI validation methods, working towards identifying a set of metrics that is applicable across datasets. Twenty-three validation methods were found and categorized. Most methods evaluated positional accuracy, while logical consistency and usability were the least represented. A subset of nine methods was implemented to assess four real-world POI datasets extracted for a highly urbanized neighborhood in Singapore. The datasets were found to have poor completeness with errors of commission and omission, although spatial errors were reasonably low (<60 m). Thematic accuracy in names and place types varied. The move towards standardized validation metrics depends on factors such as data availability for intrinsic or extrinsic methods, varying levels of detail across POI datasets, the influence of matching procedures, and the intended application of POI data.

Analysis of the Impact of Positional Accuracy When Using a Block of Pixels for Thematic Accuracy Assessment

Pixels, blocks (i.e., grouping of pixels), and polygons are the fundamental choices for use as assessment units for validating per-pixel image classification. Previous research conducted by the authors of this paper focused on the analysis of the impact of positional accuracy when using a single pixel for thematic accuracy assessment. The research described here provided a similar analysis, but the blocks of contiguous pixels were chosen as the assessment unit for thematic validation. The goal of this analysis was to assess the impact of positional errors on the thematic assessment. Factors including the size of a block, labeling threshold, landscape characteristics, spatial scale, and classification schemes were also considered. The results demonstrated that using blocks as an assessment unit reduced the thematic errors caused by positional errors to under 10% for most global land-cover mapping projects and most remote-sensing applications achieving a half-pixel registration. The larger the block size, the more the positional error was reduced. However, there are practical limitations to the size of the block. More classes in a classification scheme and higher heterogeneity increased the positional effect. The choice of labeling threshold depends on the spatial scale and landscape characteristics to balance the number of abandoned units and positional impact. This research suggests using the block of pixels as an assessment unit in the thematic accuracy assessment in future applications.

Progress in Grassland Cover Conservation in Southern European Mountains by 2020: A Transboundary Assessment in the Iberian Peninsula with Satellite Observations (2002–2019)

Conservation and policy agendas, such as the European Biodiversity strategy, Aichi biodiversity (target 5) and Common Agriculture Policy (CAP), are overlooking the progress made in mountain grassland cover conservation by 2020, which has significant socio-ecological implications to Europe. However, because the existing data near 2020 is scarce, the shifting character of mountain grasslands remains poorly characterized, and even less is known about the conservation outcomes because of different governance regimes and map uncertainty. Our study used Landsat satellite imagery over a transboundary mountain region in the northwestern Iberian Peninsula (Peneda-Gerês) to shed light on these aspects. Supervised classifications with a multiple classifier ensemble approach (MCE) were performed, with post classification comparison of maps established and bias-corrected to identify the trajectory in grassland cover, including protected and unprotected governance regimes. By analysing class-allocation (Shannon entropy), creating 95% confidence intervals for the area estimates, and evaluating the class-allocation thematic accuracy relationship, we characterized uncertainty in the findings. The bias-corrected estimates suggest that the positive progress claimed internationally by 2020 was not achieved. Our null hypothesis to declare a positive progress (at least equality in the proportion of grassland cover of 2019 and 2002) was rejected (X2 = 1972.1, df = 1, p < 0.001). The majority of grassland cover remained stable (67.1 ± 10.1 relative to 2002), but loss (−32.8 ± 7.1% relative to 2002 grasslands cover) overcame gain areas (+11.4 ± 6.6%), indicating net loss as the prevailing pattern over the transboundary study area (−21.4%). This feature prevailed at all extents of analysis (lowlands, −22.9%; mountains, −17.9%; mountains protected, −14.4%; mountains unprotected, −19.7%). The results also evidenced that mountain protected governance regimes experienced a lower decline in grassland extent compared to unprotected. Shannon entropy values were also significantly lower in correctly classified validation sites (z = −5.69, p = 0.0001, n = 708) suggesting a relationship between the quality of pixel assignment and thematic accuracy. We therefore encourage a post-2020 conservation and policy action to safeguard mountain grasslands by enhancing the role of protected governance regimes. To reduce uncertainty, grassland gain mapping requires additional remote sensing research to find the most adequate spatial and temporal data resolution to retrieve this process.

Methodological Proposal for Automated Detection of the Wildland–Urban Interface: Application to the Metropolitan Regions of Madrid and Barcelona

Official information on Land Use Land Cover is essential for mapping wildland–urban interface (WUI) zones. However, these resources do not always provide the geometrical or thematic accuracy required to delimit buildings that are easily exposed to risk of wildfire at the appropriate scale. This research shows that the integration of active remote sensing and official Land Use Land Cover (LULC) databases, such as the Spanish Land Use Land Cover information system (SIOSE), creates the synergy capable of achieving this. An automated method was developed to detect WUI zones by the massive geoprocessing of data from official and open repositories of the Spanish national plan for territory observation (PNOT) of the Spanish national geographic institute (IGN), and it was tested in the most important metropolitan zones in Spain: Barcelona and Madrid. The processing of trillions of LiDAR data and their integration with thousands of SIOSE polygons were managed in a Linux environment, with libraries for geographic processing and a PostgreSQL database server. All this allowed the buildings that are exposed to wildfire risk with a high level of accuracy to be obtained with a methodology that can be applied anywhere in the Spanish territory.

Assessing the Connectivity of Riparian Forests across a Gradient of Human Disturbance: The Potential of Copernicus “Riparian Zones” in Two Hydroregions

The connectivity of riparian forests can be used as a proxy for the capacity of riparian zones to provide ecological functions, goods and services. In this study, we aim to test the potential of the freely available Copernicus “Riparian Zones” dataset to characterize the connectivity of riparian forests located in two European bioclimatic regions—the Mediterranean and the Central Baltic hydroregions—when subject to a gradient of human disturbance characterized by land-use/land-cover and hydromorphological pressures. We extracted riparian patches using the Copernicus “Actual Riparian Zone” (ARZ) layer and calculated connectivity using the Integral Index of Connectivity (IIC). We then compared the results with a “Manual Riparian Zone” (MRZ) layer, produced by manually digitizing riparian vegetation patches over a very high-resolution World Imagery layer. Our research evidenced reduced forest connectivity in both hydroregions, with the exception of Least Disturbed sites in the Central Baltic hydroregion. The ARZ layer exhibited overall suitability to assess the connectivity of riparian forests in the Central Baltic hydroregion, while the Mediterranean hydroregion displayed a consistent pattern of connectivity overestimation in all levels of human disturbance. To address this, we recommend some improvements in the spatial resolution and thematic accuracy of the Copernicus ARZ layer.

Vegetation inventory, mapping, and characterization report, Saguaro National Park: Volume III, type descriptions

The Sonoran Desert Network (SODN) conducted a vegetation mapping and characterization effort at the two districts of Saguaro National Park from 2010 to 2018. This project was completed under the National Park Service (NPS) Vegetation Mapping Inventory, which aims to complete baseline mapping and classification inventories at more than 270 NPS units. The vegetation map data were collected to provide park managers with a digital map product that meets national standards of spatial and thematic accuracy, while also placing the vegetation into a regional and national context. A total of 97 distinct vegetation communities were described: 83 exclusively at the Rincon Mountain District, 9 exclusively at the Tucson Mountain District, and 5 occurring in both districts. These communities ranged from low-elevation creosote (Larrea tridentata) shrub-lands spanning broad alluvial fans to mountaintop Douglas fir (Pseudotsuga menziesii) forests on the slopes of Rincon Peak. All 97 communities were described at the association level, each with detailed narratives including lists of species found in each association, their abundance, landscape features, and overall community structural characteristics. Only 15 of the 97 vegetation types were existing “accepted” types within the NVC. The others are newly de-scribed and specific to Saguaro National Park (and will be proposed for formal status within the NVC). This document is Volume III of three volumes comprising the Saguaro National Park Vegetation Mapping Inventory. This volume provides full type descriptions of the 97 associations identified and mapped during the project, and detailed in Volume I. Volume II provides abridged versions of these full descriptions, briefly describing the floristic and structural characteristics of the vegetation and showing representative photos of associations, their distribution, and an example of the satellite imagery for one polygon.

Vegetation inventory, mapping, and characterization report, Saguaro National Park: Volume II, association summaries

The Sonoran Desert Network (SODN) conducted a vegetation mapping and characterization effort at the two districts of Saguaro National Park from 2010 to 2018. This project was completed under the National Park Service (NPS) Vegetation Mapping Inventory, which aims to complete baseline mapping and classification inventories at more than 270 NPS units. The vegetation map data were collected to provide park managers with a digital map product that meets national standards of spatial and thematic accuracy, while also placing the vegetation into a regional and national context. A total of 97 distinct vegetation communities were described: 83 exclusively at the Rincon Mountain District, 9 exclusively at the Tucson Mountain District, and 5 occurring in both districts. These communities ranged from low-elevation creosote (Larrea tridentata) shrub-lands spanning broad alluvial fans to mountaintop Douglas fir (Pseudotsuga menziesii) forests on the slopes of Rincon Peak. All 97 communities were described at the association level, each with detailed narratives including lists of species found in each association, their abundance, landscape features, and overall community structural characteristics. Only 15 of the 97 vegetation types were existing “accepted” types within the National Vegetation Classification (NVC). The others are newly described and specific to Saguaro National Park (and will be proposed for formal status within the NVC). This document is Volume II of three volumes comprising the Saguaro National Park Vegetation Mapping Inventory. This volume provides two-page summaries of the 97 associations identified and mapped during the project, and detailed in Volume I. Summaries are presented by district, starting with the Tucson Mountain District. These summaries are abridged versions of the full association descriptions found in Volume III.

AVALIAÇÃO DA EXATIDÃO TEMÁTICA DA COBERTURA E USO DA TERRA REPRESENTADA ATRAVÉS DO MAPBIOMAS NO RIO DE JANEIRO

O conhecimento das interações entre os tipos de cobertura e uso da terra e seus padrões espaço-temporais é fundamental para orientar a utilização racional do espaço. O avanço significativo na disponibilidade de produtos de sensoriamento remoto e de técnicas de processamento em larga escala têm impulsionado o surgimento de importantes iniciativas, como o Projeto MapBiomas. Contudo, há a necessidade de ampliar o conhecimento sobre as vantagens e desvantagens dessas novas tecnologias e produtos. Assim, este estudo buscou compreender as potencialidades dessas novas abordagens através da comparação pixel a pixel dos Mapas de Uso e Cobertura dos Projetos MapBiomas e SEA/2015, este último usado como referência espacial, para duas áreas pilotos do Estado do Rio de Janeiro. Os resultados alcançados sugerem que os Mapas de Uso e Cobertura da Terra do Projeto MapBiomas apresentam alto grau de confiabilidade e se posicionam, no cenário atual, como um importante conjunto de dados e referência para a realização de mapeamentos temáticos. Palavras-chave: Mapeamento Uso e Cobertura da Terra. MapBiomas. Acurácia Global.LAND USE AND LAND COVER THEMATIC ACCURACY EVALUATION REPRESENTED THROUGH MAPBIOMAS IN RIO DE JANEIRO STATEAbstract: Land Use and Land Cover Mappings are important tools to promote the rational use of space. The increase in the availability of remote sensing products, large-scale processing, and machine learning techniques have been promoting great initiatives, such as the MapBiomas Project. However, it is important to recognize the positive aspects and limitations of these new technologies and products. This study aims to better understand the potential of these new approaches through a pixel-by-pixel comparison between MapBiomas and SEA / 2015 LULC. SEA / 2015 LULC was used as a spatial and thematic reference for two pilot areas in the State of Rio de Janeiro. The results suggest that the MapBiomas Project achieved accuracy around 85% and a high level of reliability, and it is also an important source of geospatial data and reference for conducting thematic mappings. Keywords: Thematic Mapping, MapBiomas, Land Use Land Cover Mapping.EVALUACIÓN DE LA PRECISIÓN TEMÁTICA DE LA COBERTURA Y USO DEL TERRENO REPRESENTADO A TRAVÉS DE MAPBIOMES EN RÍO DE JANEIROResumen: El conocimiento de las interacciones entre los tipos de cobertura y uso del suelo y sus patrones espacio-temporales es esencial para orientar el uso racional del espacio. El importante avance en la disponibilidad de productos de teledetección y técnicas de procesamiento a gran escala ha impulsado el surgimiento de importantes iniciativas, como el Proyecto MapBiomas. Sin embargo, es necesario ampliar el conocimiento sobre las ventajas y desventajas de estas nuevas tecnologías y productos. Así, este estudio buscó comprender el potencial de estos nuevos enfoques a través de la comparación píxel a píxel de los Mapas de Uso y Cobertura MapBiomas y SEA / 2015, este último utilizado como referencia espacial, para dos áreas piloto del Estado de Río. de Janeiro. Los resultados obtenidos, con una precisión general de aproximadamente el 85% para ambas áreas de estudio, sugieren que los Mapas de Cobertura y Uso del Suelo del Proyecto MapBiomas tienen un alto grado de confiabilidad, y están posicionados en el escenario actual, como un importante conjunto de datos y referencia para realización de mapeos temáticos. Palabras clave: Uso del suelo y mapeo de cobertura. MapBiomas. Precisión global.

The Effect of Environmental Conditions on the Quality of UAS Orthophoto-Maps in the Coastal Environment

Marine conservation and management require detailed and accurate habitat mapping, which is usually produced by collecting data using remote sensing methods. In recent years, unmanned aerial systems (UAS) are used for marine data acquisition, as they provide detailed and reliable information through very high-resolution orthophoto-maps. However, as for all remotely sensed data, it is important to study and understand the accuracy and reliability of the produced maps. In this study, the effect of different environmental conditions on the quality of UAS orthophoto-maps was examined through a positional and thematic accuracy assessment. Selected objects on the orthophoto-maps were also assessed as to their position, shape, and extent. The accuracy assessment results showed significant errors in the different maps and objects. The accuracy of the classified images varied between 2.1% and 27%. Seagrasses were under-classified, while the mixed substrate class was overclassified when environmental conditions were not optimal. The highest misclassifications were caused due to sunglint presence in combination with a rough sea-surface. A change detection workflow resulted in detecting misclassifications of up to 45%, on orthophoto-maps that had been generated under non-optimal environmental conditions. The results confirmed the importance of optimal conditions for the acquisition of reliable marine information using UAS.