Generación de un banco de áreas de reflectividad pseudoinvariante para la Península Ibérica mediante imágenes MODIS

La reflectividad derivada de imágenes satelitales sigue generalmente el ciclo fenológico de las cubiertas presentes en el territorio. A pesar de ello, es posible encontrar zonas donde la reflectividad es prácticamente invariante. Estas áreas definidas como pseudoinvariantes (API) permiten comparar y calibrar imágenes provenientes de distintos sensores y procesar series temporales con una elevada coherencia. Se presenta un nuevo método automático (especialmente útil en entornos Big Data) para seleccionar API a partir del producto diario MOD09GA derivado de imágenes Terra-MODIS, utilizando una serie temporal de 14 años y las bandas del espectro solar (visible, infrarrojo cercano y de onda corta) con una resolución espacial de 500 m. Dicha metodología consta de dos etapas de filtrado, una primera que evalúa la calidad de las imágenes de la serie mediante técnicas geoestadísticas, seleccionando las mejores, y una segunda que define umbrales específicos para cada banda espectral, en función de la dispersión que presentan los datos en la selección previa de imágenes. La aplicación de este método sobre ámbitos de características topográficas y estructura de paisaje diferenciados en la Península Ibérica ha permitido la obtención de más de 12 000 API en una superficie asimilable a 9 escenas Landsat (WRS-2). Los resultados muestran que la metodología aplicada contempla la adecuada distribución tanto interanual como intraanual de las imágenes, dando lugar a API que abarcan una amplia variedad de cubiertas con reflectividades diversas, ubicadas principalmente en zonas boscosas o seminaturales (77%),zonas agrícolas (21 %), así como en otros tipos de cubiertas no vegetales

Does topographic normalization of landsat images improve fractional tree cover mapping in tropical mountains?

Volume: Volume XL-7/W3Fractional tree cover (Fcover) is an important biophysical variable for measuring forest degradation and characterizing land cover. Recently, atmospherically corrected Landsat data have become available, providing opportunities for high-resolution mapping of forest attributes at global-scale. However, topographic correction is a pre-processing step that remains to be addressed. While several methods have been introduced for topographic correction, it is uncertain whether Fcover models based on vegetation indices are sensitive to topographic effects. Our objective was to assess the effect of topographic correction on the accuracy of Fcover modelling. The study area was located in the Eastern Arc Mountains of Kenya. We used C-correction as a digital elevation model (DEM) based correction method. We examined if predictive models based on normalized difference vegetation index (NDVI), reduced simple ratio (RSR) and tasseled cap indices (Brightness, Greenness and Wetness) are improved if using topographically corrected data. Furthermore, we evaluated how the results depend on the DEM by correcting images using available global DEM (ASTER GDEM, SRTM) and a regional DEM. Reference Fcover was obtained from wall-to-wall airborne LiDAR data. Landsat images corresponding to minimum and maximum sun elevation were analyzed. We observed that topographic correction could only improve models based on Brightness and had very small effect on the other models. Cosine of the solar incidence angle (cos i) derived from SRTM DEM showed stronger relationship with spectral bands than other DEMs. In conclusion, our results suggest that, in tropical mountains, predictive models based on common vegetation indices are not sensitive to topographic effects.Peer reviewe

TOPOGRAPHIC EFFECT ON SPECTRAL VEGETATION INDICES FROM LANDSAT TM DATA: IS TOPOGRAPHIC CORRECTION NECESSARY?

The full potentiality of spectral vegetation indices (VIs) can only be evaluated after removing topographic, atmospheric and soil background effects from radiometric data. Concerning the former effect, the topographic effect was barely investigated in the context of VIs, despite the current availability correction methods and Digital elevation Model (DEM). In this study, we performed topographic correction on Landsat 5 TM spectral bands and evaluated the topographic effect on four VIs: NDVI, RVI, EVI and SAVI. The evaluation was based on analyses of mean and standard deviation of VIs and TM band 4 (near-infrared), and on linear regression analyses between these variables and the cosine of the solar incidence angle on terrain surface (cos i). The results indicated that VIs are less sensitive to topographic effect than the uncorrected spectral band. Among VIs, NDVI and RVI were less sensitive to topographic effect than EVI and SAVI. All VIs showed to be fully independent of topographic effect only after correction. It can be concluded that the topographic correction is required for a consistent reduction of the topographic effect on the VIs from rugged terrain

Multitemporal evaluation of topographic correction methods using multispectral synthetic images

Revista oficial de la Asociación Española de Teledetección[EN] This paper presents a multitemporal evaluation of topographic correction (TOC) methods based on synthetically generated images in order to evaluate the influence of solar angles on the performance of TOC methods. These synthetic images represent the radiance an optical sensor would receive for different periods of the year considering the real topography (SR image), and considering the relief completely horizontal (SH image). The comparison between the corrected image obtained applying a TOC method to a SR image and the SH image of the same area, i.e. considered the ideal correction, allows assessing the performance of each TOC algorithm, quantitatively measured through the Structural Similarity Index (SSIM).[ES] En este trabajo se presentan los resultados de la evaluación multitemporal de varios métodos de corrección topográfica (TOC), cuya bondad se determina de forma cuantitativa mediante el uso de imágenes sintéticas multiespectrales simuladas para diferentes fechas de adquisición a lo largo del año. Para cada fecha se generan dos imágenes sintéticas, una considerando el relieve real (imagen SR), y otra el relieve horizontal (imagen SH). Las imágenes SR se corrigen utilizando distintos TOC y estas imágenes corregidas se comparan con la corrección ideal (imagen SH) mediante el índice de similitud estructural (SSIM). Los valores de SSIM nos permiten evaluar la eficacia de cada corrección para distintas fechas, es decir, para distintos ángulos de elevación solar.Sola, I.; González-Audícana, M.; Álvarez-Mozos, J.; Torres, J. (2014). Evaluación multitemporal de métodos de corrección topográfica mediante el uso de imágenes sintéticas multiespectrales. Revista de Teledetección. (41):71-78. doi:10.4995/raet.2014.2246.SWORD717841Baraldi, A., Gironda, M., & Simonetti, D. (2010). Operational Two-Stage Stratified Topographic Correction of Spaceborne Multispectral Imagery Employing an Automatic Spectral-Rule-Based Decision-Tree Preliminary Classifier. IEEE Transactions on Geoscience and Remote Sensing, 48(1), 112-146. doi:10.1109/tgrs.2009.2028017Civco, D.L. 1989. Topographic Normalization of Landsat Thematic Mapper Digital Imagery. Photogramm. Eng. Remote S., 55: 1303-1309.Dumortier, D. 1998. The satellight model of turbidity variations in Europe. Report for the 6th Satel-Light meeting. Freiburg, Germany.Law, K.H., Nichol, J. 2004. Topographic correction for differential illumination effects on IKONOS satellite imagery. Int. Arch. Photogramm. Remote Sens. Spat. Inform. Sci., pp. 641-646.Page, J. 1996. Algorithms for the Satellight programme. Technical Report for the 2nd SATEL-LIGHT meeting. June, 1996, Bergen, Norway.Smith, J.A., Lin, T.L., Ranson, K.J. 1980. The Lambertian Assumption and Landsat Data. Photogrammetric Engineering & Remote Sensing, 46(9): 1183-1189Teillet, P. M., Guindon, B., & Goodenough, D. G. (1982). On the Slope-Aspect Correction of Multispectral Scanner Data. Canadian Journal of Remote Sensing, 8(2), 84-106. doi:10.1080/07038992.1982.10855028Twele, A., Kappas, M., Lauer, J., Erasmi, S. 2006. The effect of stratified topographic correction on land cover classificacion in tropical mountainous regions ISPRS Comm. VII Symp., 8-11 May, Enschede, The Netherlands, pp. 432-437

Geospatial modeling of surface runoff in watersheds of the southwest hills, Tucumán, Argentina

La aplicación del modelo hidrológico L-THIA ©, con apoyo en la metodología del número de la curva (CN) del Servicio de Conservación de Suelos de Estados Unidos, es empleado para transformar la precipitación total en precipitación efectiva, constituyéndose en una herramienta de gran valor para realizar estudios hidrológicos en cuencas hidrográficas, en las que no se cuenta con registros lo suficientemente extensos y confiables. Esta metodología requiere del conocimiento del tipo y uso de suelo de la cuenca en estudio, así como de registros de precipitación, en estaciones cercanas a ella. El presente estudio se aplica en la cuenca hidrográfica de los ríos Singuil y Chavarria, Tucumán, Argentina. Se cuantificó el uso y cobertura del suelo a partir del procesamiento de imágenes Landsat TM, identificando los cambios de uso y cobertura del suelo para el período 1986-2010. El procesamiento digital de la base de datos vectorial consistió en la rasterización automática con herramientas de sistema de información geográfica. Se obtuvo el valor de CN y se cuantificó la lámina de escurrimiento. La disminución de la cobertura de pastizal y su reemplazo por bosque nativo, incrementa la tasa de infiltración reduciendo el escurrimiento superficial.The application of the L-THIA ©, hydrologic model, supported on the curve number methodology (CN), SCS-USA, it is used to transform total precipitation into effective precipitation. This becomes a useful tool for hydrologic studies in basins lacking extended and truthful registers. This methodology requires: type of soil data, land use land cover map, and precipitation data. The study area was Singuil and Chavarria basins, Tucumán, Argentina. We analyzed land use and cover change during 1986-2010 using Landsat TM images. Vectorial data base was rasterized using Geographic Information System. CN value and run off level were obtained.The decrease of grassland cover and its replacement by native forest increases the rate of infiltration reducing the surface runoff in the analyzed basins.Facultad de Ciencias Agrarias y Forestales (FCAF

Geospatial modeling of surface runoff in watersheds of the southwest hills, Tucumán, Argentina

La aplicación del modelo hidrológico L-THIA ©, con apoyo en la metodología del número de la curva (CN) del Servicio de Conservación de Suelos de Estados Unidos, es empleado para transformar la precipitación total en precipitación efectiva, constituyéndose en una herramienta de gran valor para realizar estudios hidrológicos en cuencas hidrográficas, en las que no se cuenta con registros lo suficientemente extensos y confiables. Esta metodología requiere del conocimiento del tipo y uso de suelo de la cuenca en estudio, así como de registros de precipitación, en estaciones cercanas a ella. El presente estudio se aplica en la cuenca hidrográfica de los ríos Singuil y Chavarria, Tucumán, Argentina. Se cuantificó el uso y cobertura del suelo a partir del procesamiento de imágenes Landsat TM, identificando los cambios de uso y cobertura del suelo para el período 1986-2010. El procesamiento digital de la base de datos vectorial consistió en la rasterización automática con herramientas de sistema de información geográfica. Se obtuvo el valor de CN y se cuantificó la lámina de escurrimiento. La disminución de la cobertura de pastizal y su reemplazo por bosque nativo, incrementa la tasa de infiltración reduciendo el escurrimiento superficial.The application of the L-THIA ©, hydrologic model, supported on the curve number methodology (CN), SCS-USA, it is used to transform total precipitation into effective precipitation. This becomes a useful tool for hydrologic studies in basins lacking extended and truthful registers. This methodology requires: type of soil data, land use land cover map, and precipitation data. The study area was Singuil and Chavarria basins, Tucumán, Argentina. We analyzed land use and cover change during 1986-2010 using Landsat TM images. Vectorial data base was rasterized using Geographic Information System. CN value and run off level were obtained.The decrease of grassland cover and its replacement by native forest increases the rate of infiltration reducing the surface runoff in the analyzed basins.Facultad de Ciencias Agrarias y Forestales (FCAF

SCS+C Topographic Correction to Enhance SVM Classification Accuracy

The topographic impact may change the radiance values captured by the spacecraft sensors, resulting in distinct reflectance value for similar land cover classes and mischaracterization. The problem can be more clearly seen in rugged terrain landscapes than in flat terrains, such as the mountainous areas. In order to minimize topographic impacts, we suggested the implementation of Modified Sun-Canopy-Sensor Correction (SCS+C) technique to generate land cover maps in Gua Musang district which is located in a rugged mountainous terrain area in Kelantan state, Malaysia using an atmospherically corrected Landsat 8 imagery captured on 22 April 2014 by Support Vector Machine (SVM) algorithm. The results showed that the SCS+C method reduces the topographic effect particularly in such a steep and forested terrain with classification accuracy improvement about 4 % which was statistically significantly with the McNemar test value Z and P measured 6.42 and 0.0001 on the corrected image classification 90.1 % accuracy compared to the uncorrected image 86.2 % for the test area. Thus, the topographic correction is suggested to be the main step of the data pre-processing stage in mountainous terrain before SVM image classification

Rapid Urban Growth in the Kathmandu Valley, Nepal: Monitoring Land Use Land Cover Dynamics of a Himalayan City with Landsat Imageries

abstract: The Kathmandu Valley of Nepal epitomizes the growing urbanization trend spreading across the Himalayan foothills. This metropolitan valley has experienced a significant transformation of its landscapes in the last four decades resulting in substantial land use and land cover (LULC) change; however, no major systematic analysis of the urbanization trend and LULC has been conducted on this valley since 2000. When considering the importance of using LULC change as a window to study the broader changes in socio-ecological systems of this valley, our study first detected LULC change trajectories of this valley using four Landsat images of the year 1989, 1999, 2009, and 2016, and then analyzed the detected change in the light of a set of proximate causes and factors driving those changes. A pixel-based hybrid classification (unsupervised followed by supervised) approach was employed to classify these images into five LULC categories and analyze the LULC trajectories detected from them. Our results show that urban area expanded up to 412% in last three decades and the most of this expansion occurred with the conversions of 31% agricultural land. The majority of the urban expansion happened during 1989–2009, and it is still growing along the major roads in a concentric pattern, significantly altering the cityscape of the valley. The centrality feature of Kathmandu valley and the massive surge in rural-to-urban migration are identified as the primary proximate causes of the fast expansion of built-up areas and rapid conversions of agricultural areas