Unmixing AVHRR Imagery to Assess Clearcuts and Forest Regrowth in Oregon

Advanced Very High Resolution Radiometer imagery provides frequent and low-cost coverage of the earth, but its coarse spatial resolution (approx. 1.1 km by 1.1 km) does not lend itself to standard techniques of automated categorization of land cover classes because the pixels are generally mixed; that is, the extent of the pixel includes several land use/cover classes. Unmixing procedures were developed to extract land use/cover class signatures from mixed pixels, using Landsat Thematic Mapper data as a source for the training set, and to estimate fractions of class coverage within pixels. Application of these unmixing procedures to mapping forest clearcuts and regrowth in Oregon indicated that unmixing is a promising approach for mapping major trends in land cover with AVHRR bands 1 and 2. Including thermal bands by unmixing AVHRR bands 1-4 did not lead to significant improvements in accuracy, but experiments with unmixing these four bands did indicate that use of weighted least squares techniques might lead to improvements in other applications of unmixing

Cartografía de malas hierbas en cultivos de maíz mediante imágenes hiperespectrales aeroportadas (AHS)

El presente trabajo aborda la cartografía de las malas hierbas Sorghum halepense, Xanthium strumarium y Abutilon theophrasti en cultivos de maíz mediante técnicas de teledetección hiperespectral. Se ha utilizado una imagen adquirida por el sensor aeroportado AHS (Airborne Hyperspectral Scanner) con una resolución espacial en el nadir de 2,5 m y 80 bandas espectrales desde 0,43 hasta 12,5µm. La imagen fue adquirida en mayo de 2007, coincidiendo con el momento óptimo para la aplicación del herbicida, sobre una zona cultivada de maíz en la finca experimental La Poveda situada al SE de la Comunidad de Madrid. Se aplicaron diversas correcciones geométricas y radiométricas, incluida la conversión a reflectividades, que se llevó a cabo mediante un ajuste empírico basado en mediciones espectrales realizadas sobre el terreno simultáneamente a la adquisición de la imagen. La técnica de Análisis de Mezclas Espectrales (ALME) nos permitió obtener un mapa de cobertura de cada una de las malas hierbas analizadas así como información sobre las proporciones de cada cubierta (malas hierbas y maíz/suelo) en cada píxel. La validación realizada para la especie S. halepense utilizando como referencia los perímetros de los rodales obtenidos con GPS mostró que sólo un 16,8 % de la superficie ocupada por esta especie no fue discriminada a partir de la imagen.El presente trabajo ha sido realizado en el marco del proyecto “Ecología espacio-temporal y teledetección de malas hierbas en cultivos de maíz” AGL2005-06180-C03-01 financiado por el Ministerio de Ciencia e Innovación.Peer reviewe

LINKING MULTIVARIATE OBSERVATIONS OF THE LAND SURFACE TO VEGETATION PROPERTIES AND ECOSYSTEM PROCESSES

Remotely sensed images from satellites and aircrafts, as well as regional networks and monitoring stations such as eddy flux towers, are collecting large volumes of multivariate data that contain information about the land surface and ecosystem processes. To derive from these systems information and knowledge relevant to how the Earth system functions and how it is changing, we need tools that to filter and mine the large data streams currently being acquired at different spatial and temporal scales. A challenge for Earth System Science lies in accurately identifying and portraying the relationships between the measurements at the sensor and quantity o f interest (i.e. ecosystem process or land surface property)

MODELO LINEAR DE MISTURA ESPECTRAL EM IMAGEM DE MODERADA RESOLUÇÃO ESPACIAL

The concept of spectral mixture offers a wide range of applications in theRemote Sensing area. The application of this concept, however, requires theprior estimation of the component’s (endmembers) spectral response. Thislatter requirement can be achieved by different methods, as reported in theliterature, such as techniques for the detection of pure pixels, use of spectrallibraries, and field radiometric measurements. Among those, the most oftenused is the pure pixel approach. In this approach, the components’ spectralreflectances are estimated by means of pixels covered entirely by a singlecomponent. This approach offers the advantage of allowing the extraction ofthe required spectral reflectance directly from the image data. This approach,however, becomes increasingly unfeasible as the spatial resolution of theimage data decreases, due to the larger ground area covered by a single pixel.In this study we propose a methodology to estimate the spectral reflectance foreach component class in moderate spatial resolution image data, by applyingthe linear mixing model (MLME), and higher spatial resolution image data asauxiliary data. It is expected that this methodology will provide a morepractical way to implement the spectral mixture approach to moderateresolution image data, allowing in this way the expansion of the informationabout the components’ proportions across larger areas, up-scaling informationin regional and global studies. Experiments were carried out using CCD (20m ground resolution) and IRMSS (80 m ground resolution) and WFI (260 mground resolution) CBERS-2 image data, as medium and moderate spatialresolution data, respectively. The spectral reflectances for the components inthe IRMSS and WFI CBERS-2 spectral bands are estimated by applying theproposed methodology. The reliability of the proposed methodology wasassessed by both analyzing scatter plots for CBERS-2 data and by comparingthe fraction images produced by image data sets of the sensors analyzed.O conceito de mistura espectral apresenta várias aplicações na área desensoriamento remoto. Esta abordagem requer, entretanto, o conhecimento apriori da resposta espectral dos membros de referência. Existem, na literatura,diferentes propostas para estimar esta resposta, como por exemplo, o uso depixels puros, o uso de bibliotecas espectrais e a realização de medidasradiométricas de campo. Na prática, a abordagem via pixel puro tem sido amais comumente empregada, por utilizar dados disponíveis na própriaimagem. Esta abordagem vai, entretanto tornando-se gradativamenteimpraticável, na medida em que a resolução espacial dos dados decresce,devido às dimensões da área coberta no terreno por cada pixel. Como soluçãoa este problema, é proposta neste estudo uma metodologia para fins deestimação de refletâncias espectrais em dados de imagens de moderadaresolução espacial, empregando o modelo linear de mistura espectral (MLME)e dados de imagens de resolução espacial média, na qualidade de dadosauxiliares. Objetiva-se desta forma facilitar a utilização das técnicas demistura espectral em estudos regionais, nos quais imagens de moderadaresolução espacial são freqüentemente as mais adequadas. A metodologia proposta foi testada utilizando-se dados dos sensores CCD (20 m) e IRMSS(80 m) e WFI (260 m) a bordo da plataforma CBERS-2, na qualidade dedados de média e moderada resolução espacial, respectivamente. Asrefletâncias espectrais para as classes membros de referência foram estimadaspara as bandas espectrais do IRMSS e WFI CBERS-2 por meio dametodologia proposta. A confiabilidade da abordagem proposta foi avaliadapor meio de diagramas de espalhamento para os dados CBERS-2 e tambémpela comparação entre as imagens-fração, produzidas a partir dos conjuntos dedados de imagem dos sensores analisados

Mapping regional land cover and land use change using MODIS time series

Coarse resolution satellite observations of the Earth provide critical data in support of land cover and land use monitoring at regional to global scales. This dissertation focuses on methodology and dataset development that exploit multi-temporal data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to improve current information related to regional forest cover change and urban extent. In the first element of this dissertation, I develop a novel distance metric-based change detection method to map annual forest cover change at 500m spatial resolution. Evaluations based on a global network of test sites and two regional case studies in Brazil and the United States demonstrate the efficiency and effectiveness of this methodology, where estimated changes in forest cover are comparable to reference data derived from higher spatial resolution data sources. In the second element of this dissertation, I develop methods to estimate fractional urban cover for temperate and tropical regions of China at 250m spatial resolution by fusing MODIS data with nighttime lights using the Random Forest regression algorithm. Assessment of results for 9 cities in Eastern, Central, and Southern China show good agreement between the estimated urban percentages from MODIS and reference urban percentages derived from higher resolution Landsat data. In the final element of this dissertation, I assess the capability of a new nighttime lights dataset from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band (DNB) for urban mapping applications. This dataset provides higher spatial resolution and improved radiometric quality in nighttime lights observations relative to previous datasets. Analyses for a study area in the Yangtze River Delta in China show that this new source of data significantly improves representation of urban areas, and that fractional urban estimation based on DNB can be further improved by fusion with MODIS data. Overall, the research in this dissertation contributes new methods and understanding for remote sensing-based change detection methodologies. The results suggest that land cover change products from coarse spatial resolution sensors such as MODIS and VIIRS can benefit from regional optimization, and that urban extent mapping from nighttime lights should exploit complementary information from conventional visible and near infrared observations

Linear Unmixing of Hyperspectral Signals via Wavelet Feature Extraction

A pixel in remotely sensed hyperspectral imagery is typically a mixture of multiple electromagnetic radiances from various ground cover materials. Spectral unmixing is a quantitative analysis procedure used to recognize constituent ground cover materials (or endmembers) and obtain their mixing proportions (or abundances) from a mixed pixel. The abundances are typically estimated using the least squares estimation (LSE) method based on the linear mixture model (LMM). This dissertation provides a complete investigation on how the use of appropriate features can improve the LSE of endmember abundances using remotely sensed hyperspectral signals. The dissertation shows how features based on signal classification approaches, such as discrete wavelet transform (DWT), outperform features based on conventional signal representation methods for dimensionality reduction, such as principal component analysis (PCA), for the LSE of endmember abundances. Both experimental and theoretical analyses are reported in the dissertation. A DWT-based linear unmixing system is designed specially for the abundance estimation. The system utilizes the DWT as a pre-processing step for the feature extraction. Based on DWT-based features, the system utilizes the constrained LSE for the abundance estimation. Experimental results show that the use of DWT-based features reduces the abundance estimation deviation by 30-50% on average, as compared to the use of original hyperspectral signals or conventional PCA-based features. Based on the LMM and the LSE method, a series of theoretical analyses are derived to reveal the fundamental reasons why the use of the appropriate features, such as DWT-based features, can improve the LSE of endmember abundances. Under reasonable assumptions, the dissertation derives a generalized mathematical relationship between the abundance estimation error and the endmember separabilty. It is proven that the abundance estimation error can be reduced through increasing the endmember separability. The use of DWT-based features provides a potential to increase the endmember separability, and consequently improves the LSE of endmember abundances. The stability of the LSE of endmember abundances is also analyzed using the concept of the condition number. Analysis results show that the use of DWT-based features not only improves the LSE of endmember abundances, but also improves the LSE stability

Empleo de la Teledetección en el análisis de la deforestación tropical : el caso de la reserva forestal de Ticoporo (Venezuela)

En este trabajo se ensaya una metodología sencilla de análisis multitemporal para el\ud seguimiento del proceso de deforestación en la Reserva de Ticoporo, Venezuela. Para llevar a cabo este estudio se ha utilizado una serie de fotografías aéreas (año 1962) y cuatro imágenes de satélite, procedentes de diversos sensores de alta resolución espacial (Landsat MSS y TM y SPOT-HRV), de los años 1972, 1989, 1993 y 1997. De la fotointerpretación del primer documento\ud se obtuvo un mapa de formaciones de vegetación, que posteriormente se reagrupó a dos únicas categorías, zonas forestales y agrícolas. Las imágenes se clasificaron en estas mismas categorías mediante el establecimiento de umbrales de sus respectivos índices de vegetación (NDVI). Se realizó una tabulación cruzada de cada par de imágenes clasificadas de fechas\ud consecutivas (1962-1972, 1972-1989, 1989-1993, y 1993-1997), así como de la primera y última fecha, para extraer las zonas de cambio y las estables durante ese intervalo de tiempo. La deforestación experimentada en esta zona puede cifrarse en unas 80.000 ha, lo que supone el 60% del área de estudio. La técnica se mostró de gran utilidad para el seguimiento de este\ud fenómeno, pudiendo ser utilizada por los órganos de gestión para paliar los efectos negativos asociados a los procesos de deforestación. Por último, se lleva a cabo un sencillo análisis de la evolución del patrón espacial del área de estudio en ese periodo. El análisis de los cambios\ud experimentados en las manchas (patches) del área de estudio, contabilizando el número de\ud polígonos, su densidad, tamaño promedio y diversidad, muestran una tendencia al aumento de la diversidad espacial (mayor fragmentación, pues el espacio original se parcela), pese a la pérdida de diversidad vegetal (reducción de las cubiertas forestales).This study presents a simple multi-temporal analysis methodology to monitor the deforestation\ud process in the Tipocoro Reserve (Venezuela). A series of air photos (1962) plus four satellite\ud images from 1972, 1989, 1993 and 1997 were used.\ud A vegetation type map was obtained from photo-interpretation, which was later reclassified\ud into just two categories: forests and crops. The satellite images were also classified into these two\ud same categories by establishing thresholds for each of their vegetation indices (NDVI). A cross\ud tabulation was carried out for each pair of classified images in chronological order (1962-1972,\ud 1972-1989, 1989-1993, and 1993-1997) plus a pair corresponding to the first and last date (1962-\ud 1997), in order to determine changes during this time period. The total deforestation can be\ud estimated in approximately 80,000 ha, which accounts for 60% of the study area.\ud The method used proved to be very useful for deforestation monitoring purposes and can be\ud implemented by forestry management officials to control its effects.\ud Finally, a simple analysis was carried out to study the spatial trends in the study area throughout\ud this period. Changes in the study area’s patches were analysed taking into account the number\ud of polygons, density, average size and diversity. Results showed that spatial diversity increased\ud (higher fragmentations, since the original area is parceled out), in spite of the decrease in\ud vegetation diversity resulting from losses in forested cover area

Assesment of biomass and carbon dynamics in pine forests of the Spanish central range: A remote sensing approach

Forests play a dynamic role in the terrestrial carbon (C) budget, by means of the biomass stock and C fluxes involved in photosynthesis and respiration. Remote sensing in combination with data analysis constitute a practical means for evaluation of forest implications in the carbon cycle, providing spatially explicit estimations of the amount, quality, and spatio-temporal dynamics of biomass and C stocks. Medium and high spatial resolution optical data from satellite-borne sensors were employed, supported by field measures, to investigate the carbon role of Mediterranean pines in the Central Range of Spain during a 25 year period (1984-2009). The location, extent, and distribution of pine forests were characterized, and spatial changes occurred in three sub-periods were evaluated. Capitalizing on temporal series of spectral data from Landsat sensors, novel techniques for processing and data analysis were developed to identify successional processes at the landscape level, and to characterize carbon stocking condition locally, enabling simultaneous characterization of trends and patterns of change. High spatial resolution data captured by the commercial satellite QuickBird-2 were employed to model structural attributes at the stand level, and to explore forest structural diversity

Global Forest Monitoring from Earth Observation

Covering recent developments in satellite observation data undertaken for monitoring forest areas from global to national levels, this book highlights operational tools and systems for monitoring forest ecosystems. It also tackles the technical issues surrounding the ability to produce accurate and consistent estimates of forest area changes, which are needed to report greenhouse gas emissions and removals from land use changes. Written by leading global experts in the field, this book offers a launch point for future advances in satellite-based monitoring of global forest resources. It gives readers a deeper understanding of monitoring methods and shows how state-of-art technologies may soon provide key data for creating more balanced policies

Empleo de la Teledetección en el análisis de la deforestación tropical : el caso de la reserva forestal de Ticoporo (Venezuela)

En este trabajo se ensaya una metodología sencilla de análisis multitemporal para el seguimiento del proceso de deforestación en la Reserva de Ticoporo, Venezuela. Para llevar a cabo este estudio se ha utilizado una serie de fotografías aéreas (año 1962) y cuatro imágenes de satélite, procedentes de diversos sensores de alta resolución espacial (Landsat MSS y TM y SPOT-HRV), de los años 1972, 1989, 1993 y 1997. De la fotointerpretación del primer documento se obtuvo un mapa de formaciones de vegetación, que posteriormente se reagrupó a dos únicas categorías, zonas forestales y agrícolas. Las imágenes se clasificaron en estas mismas categorías mediante el establecimiento de umbrales de sus respectivos índices de vegetación (NDVI). Se realizó una tabulación cruzada de cada par de imágenes clasificadas de fechas consecutivas (1962-1972, 1972-1989, 1989-1993, y 1993-1997), así como de la primera y última fecha, para extraer las zonas de cambio y las estables durante ese intervalo de tiempo. La deforestación experimentada en esta zona puede cifrarse en unas 80.000 ha, lo que supone el 60% del área de estudio. La técnica se mostró de gran utilidad para el seguimiento de este fenómeno, pudiendo ser utilizada por los órganos de gestión para paliar los efectos negativos asociados a los procesos de deforestación. Por último, se lleva a cabo un sencillo análisis de la evolución del patrón espacial del área de estudio en ese periodo. El análisis de los cambios experimentados en las manchas (patches) del área de estudio, contabilizando el número de polígonos, su densidad, tamaño promedio y diversidad, muestran una tendencia al aumento de la diversidad espacial (mayor fragmentación, pues el espacio original se parcela), pese a la pérdida de diversidad vegetal (reducción de las cubiertas forestales).This study presents a simple multi-temporal analysis methodology to monitor the deforestation process in the Tipocoro Reserve (Venezuela). A series of air photos (1962) plus four satellite images from 1972, 1989, 1993 and 1997 were used. A vegetation type map was obtained from photo-interpretation, which was later reclassified into just two categories: forests and crops. The satellite images were also classified into these two same categories by establishing thresholds for each of their vegetation indices (NDVI). A cross tabulation was carried out for each pair of classified images in chronological order (1962-1972, 1972-1989, 1989-1993, and 1993-1997) plus a pair corresponding to the first and last date (1962- 1997), in order to determine changes during this time period. The total deforestation can be estimated in approximately 80,000 ha, which accounts for 60% of the study area. The method used proved to be very useful for deforestation monitoring purposes and can be implemented by forestry management officials to control its effects. Finally, a simple analysis was carried out to study the spatial trends in the study area throughout this period. Changes in the study area’s patches were analysed taking into account the number of polygons, density, average size and diversity. Results showed that spatial diversity increased (higher fragmentations, since the original area is parceled out), in spite of the decrease in vegetation diversity resulting from losses in forested cover area