Examinando por Autor "Novillo, Carlos J"

Mostrando 1 - 7 de 7

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.
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.
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.
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.
Remotely sensed albedo allows the identification of two ecosystem states along aridity gradients in Africa
(Wiley, 2019-05-06) Zhao, Yanchuang; Wang, Xinyuan; Novillo, Carlos J; Arrogante, Patricia; Vázquez, René; Berdugo, Miguel; Maestre, Fernando T.
La verificación empírica de múltiples estados en zonas áridas es escasa, dificultando el diseño de indicadores para anticipar el inicio de la desertificación. Los indicadores derivados de teledetección de los estados del ecosistema están ganando terreno debido a las posibilidades que ofrecen de aplicarse de manera económica en áreas extensas. El albedo derivado de la teledetección se ha utilizado con frecuencia para monitorear zonas áridas debido a su estrecha relación con el estado del ecosistema y el clima. En este estudio, empleamos un enfoque de sustitución espacio-temporal para evaluar si el albedo (promediado de 2000 a 2016) puede identificar múltiples estados del ecosistema en zonas áridas africanas que abarcan desde el desierto del Sáhara hasta África tropical. Mediante el análisis de clases latentes, descubrimos que el albedo mostró dos estados (bajo y alto; el nivel de corte fue 0.22 en la banda de onda corta). El análisis potencial reveló que el albedo experimentó un aumento abrupto y discontinuo con el aumento de la aridez (1 − [precipitación/evapotranspiración potencial]). Los dos estados de albedo coocurrieron a lo largo de valores de aridez que iban de 0.72 a 0.78, durante los cuales la cobertura vegetal experimentó una disminución rápida y continua de ~90% a ~50%. En valores de aridez de 0.75, el estado de bajo albedo comenzó a mostrar menos atracción que el estado de alto albedo. Las áreas de bajo albedo más allá de este valor de aridez se consideraron como regiones vulnerables donde podrían ocurrir cambios abruptos en el albedo si aumenta la aridez, según lo pronosticado por los modelos actuales de cambio climático. Nuestros hallazgos indican que el albedo derivado de la teledetección puede identificar dos estados del ecosistema en zonas áridas africanas. Respaldan la idoneidad de los índices de albedo para informarnos sobre respuestas discontinuas a la aridez experimentadas por las zonas áridas, que pueden estar vinculadas al inicio de la degradación del suelo.