Examinando por Autor "Arrogante-Funes, Patricia"

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An Alternative Method for the Generation of Consistent Mapping to Monitoring Land Cover Change: A Case Study of Guerrero State in Mexico
(Land, 2021-07-12) Vázquez-Jiménez, René; Romero-Calcerrada, Raúl; Ramos-Bernal, Rocío N; Arrogante-Funes, Patricia; Novillo, Carlos J
Land cover is crucial for ecosystems and human activities. Therefore, monitoring land cover changes has become relevant in recent years. This study proposes an alternative method based on conventional change detection techniques combined with maximum likelihood (MaxLike) supervised classification of satellite images to generate consistent Land Use/Land Cover (LULC) maps. The novelty of this method is that the supervised classification is applied in an earlier stage of change detection exclusively to identified dynamics zones. The LULC categories of the stable zones are acquired from an initial date’s previously elaborated base map. The methodology comprised the use of Landsat images from 2011 and 2016, applying the Sun Canopy Sensor (SCS + C) topographic correction model enhanced through the classification of slopes, using derived topographic corrected images with NDVI, and employing Tasseled Cap (TC) Brightness-Greenness-Wetness indices and Principal Components (PCs). The study incorporated a comparative analysis of the consistency of the LULC mapping, which is generated based on control areas. The results show that the proposed method, although slightly laborious, is viable and fully automatable. The generated LULC map is accurate and robust and achieves a Kappa concordance index of 87.53. Furthermore, the boundary consistency was visually superior to the conventional classified map.
Assessing urban ecosystem condition account with object-based methods
(Elsevier, 2024-11) Álvarez-Ripado, Ariadna; Bruzón, Adrián G.; Álvarez-García, David; Arrogante-Funes, Patricia
We present a methodology based on the System of Environmental-Economic Accounting-Ecosystems Accounting (SEEA-EA) framework to measure urban ecosystem condition accounts. This methodology allows the condition accounts of urban ecosystems to be spatially and explicitly evaluated at a detailed scale. The reference area is determined using object-based evaluation, where the reference value for each variable is set in a real geographic context rather than individual pixels. This methodology consists of the following steps: 1. Delimitation of the urban categories to be evaluated; 2. Selection of the variables that characterize the abiotic and biotic environment; 3. Establishment of the reference polygon with which to compare the condition values; 4. Calculation of weighted condition indicators; 5. Generation of a single condition index from the aggregation of the indicators. In Madrid, the areas with the highest condition levels are characterised by a significant density of trees and bird species richness. In contrast, areas with the lowest condition levels are defined by high contamination, impervious surfaces, built-up areas and major communication routes. This innovative approach to calculating urban conditions represents an advancement in local-scale urban condition accounting and offers a potentially compatible tool with current urban policy frameworks. Its applications can be various, from identifying urban problems to reviewing the effectiveness of a plan already implemented. Several advantages have been identified over other ecosystem accounting metrics. These advantages include lower operating costs, a more integrative vision, adaptability to different spatial scales, flexibility in modifying its structure, the capacity to incorporate complex urban dynamics, reduced dependence on human judgment and easier interpretation of results
Evaluation of the consistency of the three MRPV model parameters provided by the MISR level 2 land surface products: a case study in Mainland Spain
(Taylos & Francis, 2018-02-08) Arrogante-Funes, Patricia; Novillo, Carlos J; Romero-Calcerrada, Raúl; Vázquez-Jiménez, René; Ramos-Bernal, Rocío N
The multiangular Rahman–Pinty–Verstraete modified (MRPV) semi-empirical model uses three parameters (ρ0, Θ, and k) for describing the anisotropy of an arbitrary target. They have been usefully proved to characterize some forest attributes and land covers. However, there is no enough evaluation of the consistency of this product, and the possible affection from different factors in the reliability of them. Here, we explored the consistency of the MRPV parameters provided in the MISR L2 Land Surface (MIL2ASLS) product, with data from Mainland Spain, grouping MISR images into close time pairs. Thus, it was studied the three MRPV parameters through retrieving Spearman’s rank correlation coefficient (ρ) and mean relative differences related to every pair of images. The results showed the ρ0 parameter presented higher consistency than the others, with ρ over 0.85 and meant relative differences around 15%. The k parameter showed ρ over 0.65 and average relative disagreements over 8%. Finally, the Θ parameter reached ρ around 0.60. The Θ mean differences were over 25% unless the combination of the blue band which was especially bad and its values were up to 50%. So, it is crucial having into account when the parameters of this product are used to look into the band and the own parameter.
Evaluation of unsupervised change detection methods applied to landslide inventory mapping using ASTER imagery
(Remote Sensing, 2018-12-06) Ramos-Bernal, Rocío N; Vázquez-Jiménez, René; Romero-Calcerrada, Raúl; Arrogante-Funes, Patricia; Novillo, Carlos J
Natural hazards include a wide range of high-impact phenomena that affect socioeconomic and natural systems. Landslides are a natural hazard whose destructive power has caused a significant number of victims and substantial damage around the world. Remote sensing provides many data types and techniques that can be applied to monitor their effects through landslides inventory maps. Three unsupervised change detection methods were applied to the Advanced Spaceborne Thermal Emission and Reflection Radiometer (Aster)-derived images from an area prone to landslides in the south of Mexico. Linear Regression (LR), Chi-Square Transformation, and Change Vector Analysis were applied to the principal component and the Normalized Difference Vegetation Index (NDVI) data to obtain the difference image of change. The thresholding was performed on the change histogram using two approaches: the statistical parameters and the secant method. According to previous works, a slope mask was used to classify the pixels as landslide/No-landslide; a cloud mask was used to eliminate false positives; and finally, those landslides less than 450 m2 (two Aster pixels) were discriminated. To assess the landslide detection accuracy, 617 polygons (35,017 pixels) were sampled, classified as real landslide/No-landslide, and defined as ground-truth according to the interpretation of color aerial photo slides to obtain omission/commission errors and Kappa coefficient of agreement. The results showed that the LR using NDVI data performs the best results in landslide detection. Change detection is a suitable technique that can be applied for the landslides mapping and we think that it can be replicated in other parts of the world with results similar to those obtained in the present work.
How the ecosystem extent is changing: A national-level accounting approach and application
(Elsevier, 2022-01-06) Bruzón, Adrián G.; Arrogante-Funes, Patricia; de Anguita, Pablo M.; Santos-Martín, Fernando
Evaluar los cambios espaciales y temporales en los ecosistemas es esencial para tener en cuenta la contribución del capital natural al bienestar humano. Sin embargo, diversos métodos para cuantificar estos cambios desafían el desarrollo de valores confiables que puedan integrarse en las cuentas estadísticas nacionales. Siguiendo el marco del sistema internacional de contabilidad ambiental-económica, que recientemente adopta un estándar de contabilidad de ecosistemas. Presentamos un enfoque novedoso para desarrollar una cuenta de extensión de ecosistemas a partir de clasificaciones de ecosistemas existentes. Este estudio muestra la cuenta de extensión espacial y estadística de 26 ecosistemas (es decir, bosques, pastizales, tierras de cultivo y áreas urbanas, entre otros) entre 1970 y 2015 a escala nacional. Las cuentas de extensión se desarrollaron con una resolución de 25 m y proporcionaron información confiable sobre cómo han cambiado los tipos de ecosistemas a lo largo del tiempo en España. Nuestros resultados reflejan tres patrones principales en la cuenta de extensión: (i) un aumento en los ecosistemas forestales, (ii) una disminución considerable en los agroecosistemas (especialmente los cultivos anuales), y (iii) un desarrollo sustancial de áreas urbanas. Hasta donde sabemos, este método es el primer intento de desarrollar una metodología robusta para medir la extensión de los ecosistemas a nivel nacional. El enfoque propuesto es crucial para un conocimiento sólido de la dinámica de los ecosistemas y sus implicaciones para las condiciones y servicios de los ecosistemas a nivel nacional. Esto tiene aplicaciones potenciales en la planificación urbana, el desarrollo de infraestructura verde y múltiples usos para la gestión y políticas del territorio, integrando el capital natural en las estadísticas oficiales y la incorporación de ecosistemas en los procesos de planificación y monitoreo a nivel nacional.
How the ecosystem extent is changing: A national-level accounting approach and application.
(Science of The Total Environment, 815, Elsevier, 2022-01-06) Bruzón, Adrian; Arrogante-Funes, Patricia; Martínez de Anguita, Pablo; Novillo, Carlos J.; Santos-Martín, Fernando
Assessing the spatial and temporal changes in ecosystems is essential to account for natural capital contribution to human well-being. However, various methods to quantify these changes challenge the development of reliable values which can be integrated into national statistical accounts. Following the international system of environmentaleconomic accounting framework, which recently adopts an ecosystem accounting standard. We present a novel approach to develop an ecosystem extent account from existing ecosystem classifications. This study shows the spatial and statistical extent account of 26 ecosystems (i.e. forests, grasslands, croplands, and urban, among others) between 1970 and 2015 at the national scale. Extent accounts were developed at a resolution of 25 m and provided reliable information on how ecosystem types have changed over time in Spain. Our results reflect three main patterns in the extension account: (i) an increase in forest ecosystems, (ii) a considerable decrease in agroecosystems (especially annual croplands), and (iii) substantial development of urban areas. To the best of our knowledge, this method is the first attempt to develop a robust methodology to measure the extent of ecosystems at the national level. The proposed approach is crucial for a strong knowledge of ecosystem dynamics and their implications for ecosystem conditions and services at a national level. This has potential applications in urban planning, green infrastructure development, and multiple uses for territory management and policies, integrating natural capital into official statistics and mainstreaming ecosystems into national-level planning and monitoring processes.
Improving land cover classifications with multiangular data: MISR data in mainland Spain
(Remote Sensing, 2018-10-24) Novillo, Carlos J; Arrogante-Funes, Patricia; Romero-Calcerrada, Raúl
In this study, we deal with the application of multiangular data from the Multiangle Imaging Spectroradiometer (MISR) sensor for studying the effect of surface anisotropy and directional information on the classification accuracy for different land covers with different rate of disaggregation classes (from four to 35 different classes) from a Mediterranean bioregion in Iberian, Spain. We used various MISR band groups from nadir to blue, green, red, and NIR channels at nadir and off-nadir. The MISR data utilized here were provided by the L1B2T product (275 m spatial resolution) and belonged to two different orbits. We performed 23 classifications with the k-means algorithm to test multiangular data, number of clusters, and iteration effects. Our findings confirmed that the multiangular information, in addition to the multispectral information used as the input of the k-means algorithm, improves the land cover classification accuracy, and this improvement increased with the level of disaggregation. A very large number of clusters produced even better improvements than multiangular data.
Modelling and testing forest ecosystems condition account
(Elsevier, 2023-08-08) Bruzón, Adrián G.; Arrogante-Funes, Patricia; Santos-Martín, Fernando
Desarrollamos un modelo de aplicación basado en el marco del Sistema de Contabilidad Económica Ambiental-Contabilidad de Ecosistemas (SEEA-EA), avalado por la Comisión de Estadística de las Naciones Unidas en 2021. Este modelo permite mapear cuentas de condición de ecosistemas forestales mediante cálculos automatizados. Se aplicó nacionalmente en España entre 2000 y 2015 para probar su eficacia. El modelo sigue cinco pasos metodológicos para generar cuentas de condición de bosques, incluyendo la definición y delimitación espacial de tipos de ecosistemas forestales, selección de variables, establecimiento de niveles de referencia, agregación de variables en un índice de condición, y cálculo de un índice de condición único. Los resultados proporcionan información valiosa sobre el estado y tendencias de los indicadores de condición y los valores del índice de condición agregado de manera espacialmente explícita. En general, la condición de los ecosistemas forestales en España mostró un ligero aumento. Se observaron tendencias distintas para cada tipo de ecosistema, indicando mejoras significativas en algunos, mientras que otros no mostraron cambios. Este enfoque innovador tiene aplicaciones potenciales importantes en políticas y procesos de toma de decisiones, contribuyendo a la conservación de la naturaleza basada en evidencia, la gestión de servicios de ecosistemas y la identificación de áreas de restauración.
Monitoring NDVI Inter-Annual Behavior in Mountain Areas of Mainland Spain (2001–2016)
(Sustainability, 2018-11-19) Arrogante-Funes, Patricia; Novillo, Carlos J; Romero-Calcerrada, Raúl
Currently, there exists growing evidence that warming is amplified with elevation resulting in rapid changes in temperature, humidity and water in mountainous areas. The latter might result in considerable damage to forest and agricultural land cover, affecting all the ecosystem services and the socio-economic development that these mountain areas provide. The Mediterranean mountains, moreover, which host a high diversity of natural species, are more vulnerable to global change than other European ecosystems. The protected areas of the mountain ranges of peninsular Spain could help preserve natural resources and landscapes, as well as promote scientific research and the sustainable development of local populations. The temporal statistical trends (2001–2016) of the MODIS13Q1 Normalized Difference Vegetation Index (NDVI) interannual dynamics are analyzed to explore whether the NDVI trends are found uniformly within the mountain ranges of mainland Spain (altitude > 1000 m), as well as in the protected or non-protected mountain areas. Second, to determine if there exists a statistical association between finding an NDVI trend and the specific mountain ranges, protected or unprotected areas are studied. Third, a possible association between cover types in pure pixels using CORINE (Co-ordination of Information on the Environment) land cover cartography is studied and land cover changes between 2000 and 2006 and between 2006 and 2012 are calculated for each mountainous area. Higher areas are observed to have more positive NDVI trends than negative in mountain areas located in mainland Spain during the 2001–2016 period. The growing of vegetation, therefore, was greater than its decrease in the study area. Moreover, differences in the size of the area between growth and depletion of vegetation patterns along the different mountains are found. Notably, more negatives than expected are found, and fewer positives are found than anticipated in the mountains, such as the Cordillera Cantábrica (C.Cant.) or Montes de Murcia y Alicante (M.M.A). Quite the reverse happened in Pirineos (Pir.) and Montes de Cádiz y Málaga (M.C.M.), among others. The statistical association between the trends found and the land cover types is also observed. The differences observed can be explained since the mountain ranges in this study are defined by climate, land cover, human usage and, to a small degree, by land cover changes, but further detailed research is needed to get in-depth detailed conclusions. Conversely, it is found that, in protected mountain areas, a lower NDVI pixels trend than expected (>20%) occurs, whereas it is less than anticipated in unprotected mountain areas. This could be caused by management and the land cover type.
Recent NDVI trends in mainland Spain: Land-cover and phytoclimatic-type implications
(ISPRS International Journal of Geo-Information, 2019-01-11) Novillo, Carlos J; Arrogante-Funes, Patricia; Romero-Calcerrada, Raúl
The temporal evolution of vegetation is one of the best indicators of climate change, and many earth system models are dependent on an accurate understanding of this process. However, the effect of climate change is expected to vary from one land-cover type to another, due to the change in vegetation and environmental conditions. Therefore, it is pertinent to understand the effect of climate change by land-cover type to understand the regions that are most vulnerable to climate change. Hence, in this study we analyzed the temporal statistical trends (2001–2016) of the MODIS13Q1 normalized difference vegetation index (NDVI) to explore whether there are differences, by land-cover class and phytoclimatic type, in mainland Spain and the Balearic Islands. We found 7.6% significant negative NDVI trends and 11.8% significant positive NDVI trends. Spatial patterns showed a non-random distribution. The Atlantic biogeographical region showed an unexpected 21% significant negative NDVI trends, and the Alpine region showed only 3.1% significant negative NDVI trends. We also found statistical differences between NDVI trends by land cover and phytoclimatic type. Variance explained by these variables was up to 35%. Positive trends were explained, above all, by land occupations, and negative trends were explained by phytoclimates. Warmer phytoclimatic classes of every general type and forest, as well as some agriculture land covers, showed negative trends.
Thresholding Algorithm Optimization for Change Detection to Satellite Imagery
(Intechopen, 2018-01-24) Vázquez-Jiménez, René; Ramos-Bernal, Rocío N.; Romero-Calcerrada, Raúl; Arrogante-Funes, Patricia; Sánchez Tizapa, Sulpicio; Novillo, Carlos J.
To detect changes in satellite imagery, a supervised change detection technique was applied to Landsat images from an area in the south of México. At first, the linear regression (LR) method using the first principal component (1-PC) data, the Chi-square transformation (CST) method using first three principal component (PC-3), and tasseled cap (TC) images were applied to obtain the continuous images of change. Then, the threshold was defined by statistical parameters, and histogram secant techniques to categorize as change or unchanged the pixels. A threshold optimization iterative algorithm is proposed, based on the ground truth data and assessing the accuracy of a range of threshold values through the corresponding Kappa coefficient of concordance. Finally, to evaluate the change detection accuracy of conventional methods and the threshold optimization algorithm, 90 polygons (15,543 pixels) were sampled, categorized as real change/unchanged zones, and defined as ground truth, from the interpretation of color aerial photo slides aided by the land cover maps to obtain the omission/commission errors and the Kappa coefficient of agreement. The results show that the threshold optimization is a suitable approach that can be applied for change detection analysis.