Clasificaciones y Anรกlisis de Mezclas Espectrales
Clasificaciones y Anรกlisis de Mezclas Espectrales
Ejercicios para hoy 1. Correcciones atmosféricas 2. Análisis de Componentes Principales (PCA) y la Transformación de la Fracción de Ruido Mínimo (MNF Transform) 3. Clasificación no supervisada 4. Conversión de raster a vector 5. Colección de muestras 6. Clasificación supervisada 7. Despliegue en N dimensiones 8. Análisis de mezclas espectrales (SMA) 9. Filtración de los resultados
Cobertura de la tierra - 2008*
多Porque necesitamos hacer clasificaciones?
*Para validar
El problema Después de casi 40 años de percepción remota ambiental desde el lanzamiento de LandsatLandsat-1 en el 23 de julio de 1972, todavía existe el problema de identificar cobertura de la tierra (delineación e cuantificación) fácilmente y con exactitud
Belize City
False color LandSat TM mosaic courtesy of NASA / USGS
Source: TBD
Simulated true color LandSat TM image courtesy of NASA / USGS; image captured 28 March 2000
Simulated false color LandSat TM image courtesy of NASA / USGS; image captured 28 March 2000
Aerial photo of interface between savanna and forest near Belize City, April 5, 2009
Date Date No. Ground Publication Condition c. 1990, c. 2005 1 2000 2
1992-93 1992-93
1998
3
1991-99
2002
4
5 6 7 8
2000
2002
2000, 2001, 2002, 2003, 2004, 2005 2000, 2003, 2004, 2005 2005
2008
2005
2004-06
2008
2006
9 10
2000, 2005, 2007, 2008
2008
Original Source
Published as Imagery Used Resolution Geographic Number of coverage, Classes original
Earth GeoCover LC Landsat TM, Satellite 1990 Landsat ETM+ Corporation USGS GLCC AVHRR "Central American Vegetation/L PROARCA and Cover AVHRR / CAPAS Classification and Conservation Status" "Central America World Bank Ecosystems Landsat TM Mapping Project" SPOT JRC GLC 2000 Vegetation UMD / NASA U. Ark. / SERVIR USGS ESA / MEDIAS France ESA / GeoVille
30m
global
11
1km
global
96
1km
regional
25
30m
regional
196
1km
global
23
MOD44B
MODIS
500m
global
N/A
SERVIR MesoClass
MODIS
500m
regional
6
MODIS
500m
regional
9
GlobCover project
MERIS
300m
global
24*
DIVERSITY project
MERIS*
300m
regional
12
Mosaico temporal de imรกgenes de 2009, con menos que 2% de nubosidad
Source: p. 22-25, GOFCGOFC-GOLD REDD Sourcebook, Nov. 2009 edition
Source: Goodenough et al (2002): “Report on the Evaluation and Validation of EOEO-1 for Sustainable Development (EVEOSD) Project”
Image Processing: Corrections Radiometric calibration Preferably in ENVI
Mosaic, as necessary In ERDAS
Atmospheric correction
Dark object subtraction
In ERDAS
In ERDAS
Intra--scene calibration Intra
Histogram matching
El sistema ideal de percepción remota (1) Fuente uniforme de energía
(4) Súper sensor
(2) Atmosfera sin interferencia (3) Interacciones únicas con el superficie de la tierra Fuente: M. Vasquez (2006)
Interacciones de Energ铆a con la Atmosfera
Scattering Refracci贸n Absorci贸n
Fuente: B. Howell (2006)
Energía en el Objeto Radiacion Incidente puede ser… • Reflejada • Transmitida • Absuelto (y reEmitida)
I=R+T+A
Fuente: B. Howell (2006)
Usos de Percepción Remota
• Actualizar o reemplazar mapas existentes • Determinar áreas de categorías conocidas • Hacer inventarios de tipos de cobertura • Documentar cambios entre periodos • Medir condiciones en una área • Medidas cuantitativa de propiedades
Fuente: B. Howell (2006)
Centroamérica: 50 imágenes cubren 50 millón de ha, =25 GB de datos para procesar
Entonces, desde L4 en 1984 hasta al presente, hay ~769 escenas disponible para cualquier área (23 imágenes por año por L5 y L7)
Senderos de LandSat para CentroamĂŠrica
Los senderos fijos en el sistemas de referencia de Landsat
Resultados de una búsqueda en Glovis para El Salvador – 262 imågenes con menos de 30% de nubosidad, entre enero de 2007 y febrero de 2009
Landsat TM False color mosaic 2828-0303-2000
Land cover map 2004 (Source: BTFS)
Identifying Forest Cover w/ Satellite Imagery Landsat TM False color mosaic 2828-0303-2000
Land cover map 2004 (Source: BTFS)
1. Green is forest 2. To be able to extract the green is to be able to extract the forest cover
Landsat TM False color mosaic 2828-0303-2000
Forest cover map 2004 (Source: BTFS)
El Sistema de Visualización y Monitoreo (SERVIR)
Mesoamerica’s Earth Observation & Forecasting Platform
Terra Aqua
•El Sistema de Sistemas de la Observación de la Tierra (GEOSS)
Fires Red Tides Data ingest from EDOS EOS and Central
Land Cover / Use Change
LandSat MODIS SRTM AMSR-E IKONOS ASTER
Test-bed at NASA MSFC
Users
Web Interface www.servir.net
Environmental Monitoring & Decision Support Products
Operational Node at CATHALAC Panama
Central American Government agencies Impacts NGOs, researchers Educators, etc. Emergency Response Policy Changes Corridor Preservation Thematic Species Preservation Sustained Development Areas Improved livelihoods Agriculture Biodiversity Climate Ecosystems Energy Disasters Health Water Weather
•La Carta Internacional sobre el Espacio y Grandes Catástrofes •El Sistema de Información Ambiental Mesoamericano (SIAM)
SERVIR tiene herramientas muy relevantes al tema de monitoreo de REDD
SERVIR Disaster Response (2004(2004-)
1.
Red tide event - El Salvador (June 2004)
2.
Flooding – Panama City, Panama (Sept. 2004)
3.
Flooding - Rio Sixaola, Costa Rica / Panama (Jan 2005)
4.
Hurricane Stan – Guatemala, Mexico, El Salvador (Oct .2005)
5.
Flooding – Colon Province, Panama (Nov. 2006)
6.
Fire - Mountain Pine Ridge Forest Reserve, Belize (May 2007)
7.
Hurricane Dean – Mexico / Belize (Aug. 2007)
8.
Hurricane Felix – Nicaragua / Honduras (Sept. 2007)
9.
Tropical Storm Noel – Dominican Republic (Oct. 2007)
10. Tropical Storm Olga – Dominican Republic (Dec. 2007) 11. Turrialba Volcano – Costa Rica (April 2008) 12. Tropical Storm Arthur – Belize (June 2008) 13. Hurricane Gustav – Haiti / Dominican Rep. (August 2008) 14. Hurricane Hanna – Haiti (Sept. 2008) 15. Hurricane Ike – Haiti (Sept. 2008) 16. Landslide – Huahua Michoacán, Mexico (Oct. 2008) 17. Tropical Depression 16 – Belize / Guatemala / Honduras (Oct. 2008) 18. Flooding – Costa Rica / Panama (Nov. 2008) 19. Landslide – Alta Verapaz, Guatemala (Jan. 2009) 20. Earthquake – San Jose metropolitan area, Costa Rica (Jan. 2009) 21. Fire – Volcan Santo Tomas, Quetzaltenango, Guatemala (Feb. 2009) 22. Flooding – Lago Enriquillo, Dominican Republic (Feb. 2009)
Extracción de Objetos • Clasificación • Índices / ratios
• • • •
Supervisada No supervisada NDVI NBR / NDBR
• Umbrales de bandas (“band thresholding”) • Análisis de mezclas espectrales
• Varios – Sombra, Suelo, Vegetación; Sombra, Suelo, Vegetación verde, vegetación no fotosintética
Pasos básicos en clasificación de imágenes: 1) Estudio y organización de los datos 2) Aplicación de un algoritmo de clasificación 3) Validación
Estudio y organización de los datos Estudio ¿Que esta en la escena / imagen? ¿ Cuales bandas están disponible? ¿ Cuales preguntas necesitan respuestas? ¿ Se puede responder a las preguntas con la imagen? ¿ Hay suficiente información para distinguir que esta en la escena? Organización de los datos ¿ Cuantas clusters se puede re-organizar en espacio n? ¿ Como son los limites de los clusters? ¿ Los clusters corresponde a los clases deseados?
Clasificación Clasificación es el proceso de asignar los pixeles a clases homogéneos basado en análisis de las estadísticas de valores de reflectancia en uno o mas bandas. Clasificación es el proceso de derivar information clases informativos / utiles de clases espectrales. Se nombran los procesos de clasificación como supervisada o no supervisada basado en la metodología para “entrenar” el classifier (clasificador?). Con datos de ‘entrenamiento,’ los dos sistemas utilicen la misma forma de operación (división basada en las estadísticas).
Fuente: B. Howell (2006)
Clasificación Pros: • Puede apoyar en la descripción de usos y cobertura de la tierra • Puede simplificar el proceso de detección de cambios • Con procesamiento posterior, puede generar polígonos con atributos para uso en un SIG
Contra: • Clasificación con ella misma, no va extraer información útil de la data • No hay clasificadores universales • NO es perfecto The Attainment of the Sanc Grael Dante Gabriel Rosetti
Fuente: B. Howell (2006)
• Necesita procesamiento posterior para llegar a una foto bonita
Histogramas de cada banda Fuente: B. Howell (2006)
Respuestas espectrales
Histogramas de cada banda Fuente: B. Howell (2006)
ASTER: Advanced Spaceborne Thermal Emission & Reflection Radiometer
Terra
Uno de los 5 sensores en el satélite Terra (lanzando en Dic. de 1999); debe estar encendido Similar que el sensor ‘Thematic Mapper’ de LandSat Tamaño de una escena es 1/9 de una escena de LandSat 14 bandas (3 de 15m / 6 de 30m / 5 de 90m) midiendo luz en longitudes de onda visible a infrarrojo termal
One of 5 sensors on the satellite Terra (launched in Dec. 1999); ‘on‘on-call’ Similar to the Thematic Mapper sensor on the LandSat satellites Swath about 1/9 the size of LandSat swath 14 bands (3 15m / 6 30m / 5 90m) measuring light from the visible to infrared thermal wavelengths
Respuestas espectrales
Histogramas de cada banda Fuente: B. Howell (2006)
Firmas espectrales de materiales comunes
Absorci贸n de clorofila
Dispersi贸n en las celdas
Absorci贸n de agua
Firmas espectrales
Fuente: B. Howell (2006)
Clasificaci贸n No Supervisada
Belize City
False color LandSat TM mosaic courtesy of NASA / USGS
14-Nov-1980
¯
Ladyville area
Belize’s International Airport
Belize City pop’n ~39,771
0
Population data from Belize CSO
1.5
3
6 km
False color LandSat MSS image courtesy of NASA / USGS
27-Dec-1989 Savanna clearing for shrimp farm development
ÂŻ
Diminishing mangrove forests
Coastal development (Buttonwood Bay & Bella Vista)
0
Population data from Belize CSO
1.5
3
6 km
False color LandSat TM image courtesy of NASA / USGS
28-Mar-2000 Expansive shrimp ponds
ÂŻ
Settlement on former mangrove forest (Vista del Mar) Cleared mangrove New beachfront properties
Expansion of north-side Belize City (Belama)
Expansion of south-side Belize City Coastal development 0
Population data from Belize CSO
1.5
3
6 km
False color LandSat TM image courtesy of NASA / USGS
12-Feb-2004
Nova Shrimp Farm at size of Belize City
Clearing of 100s of acres of mangrove for Port development
0
Population data from Belize CSO
ÂŻ
Land reclamation at the Marine Parade
1.5
3
6 km
False color LandSat ETM image courtesy of NASA / USGS
31-March-2007
Nova Shrimp Farm ceases operations
ÂŻ
Further wetland clearing at Belama Phase IV
0
Population data produced by extrapolating Belize CSO data
1.5
3
6 km
False color ASTER image courtesy of NASA / JAXA
1980
1989
1998
La Ciudad de Belice: 1980 hasta 2006
2000
2002
2006
27-Dec-1989
Belize City pop’n ~42,518
Population data interpolated from Belize CSO data
False color LandSat TM image courtesy of NASA / USGS
27-Dec-1989
Approx. area: 2,089 acres (845 ha.)
Population data interpolated from Belize CSO data
Belize City pop’n ~42,518
False color LandSat TM image courtesy of NASA / USGS
2-Feb-2006
Belize City pop’n ~64,700
Population data extrapolated from Belize CSO data
False color ASTER image courtesy of NASA / JAXA
2-Feb-2006
Approx. area: 3,382 acres (1,369 ha.)
Population data extrapolated from Belize CSO data
Belize City pop’n ~64,700
False color ASTER image courtesy of NASA / JAXA
Antes: Belize City en 1980 • ~39,771 habitantes • 1,706 acres (6.9 km2) • Densidad de 5,756 personas / km2 • Densidad nacional de 6 personas / km2 Reciente: Belize City en 2007 • ~66,422 habitantes • 3,449 acres (14 km2) • Densidad de 4,758 personas / km2 • Densidad nacional de 13 personas / km2
• • •
El área ha doblado entre 1980 y 2007 El crecimiento anual era ~106 acres / 43 ha. La mayoría de la expansión de 705 ha fue deforestación de manglares y destrucción de humedales False color ASTER image courtesy of NASA / JAXA
Tendencias del crecimiento de la poblaci贸n y expansi贸n urbana en la Ciudad de Belice 70,000
4,000
3,500
60,000
3,000 50,000
P o p u la tio n
40,000 2,000
30,000 1,500 20,000 1,000 10,000
500 Area
0 14-Nov-80
27-Dec-89
15-Sep-98
28-Mar-00 Date
19-Sep-02
Population
2-Feb-06
0 31-Mar-07
A r e a (a c r e s )
2,500
Tendencias de expansi贸n urbana (cambios anuales) 180
160
448 acres cortadas
Annual expansion rate (acres / yr)
140
120
143 acres cortadas 100
80
60
654 acres cortadas
40
383 acres cortadas
20
0
1981
1990
1999 2000
2003
Expansi贸n de 705 ha entre nov 48 acres cortadas / reclamadas de 1980 y marzo de 2007 67 acres cortadas / reclamadas
2006
Rates of Urban Expansion & Population Density Date
Area Acres
Mar-07 Feb-06 Sep-02 Mar-00 Sep-98
3,449 3,382 3,334 2,886 2,743
Ha.
1,396 1,369 1,349 1,168 1,110
Pop’n 2
Km
13.96 13.69 13.49 11.68 11.10
66,422 64,128 56,700 49,050 47,947
Pop’n Density (people 2 / km )
4,758 4,684 4,203 4,199 4,320
Change From previous period (acres)
67 48 448 143 654
Avg. change per year from Previous period (acres)
Period of Change
Major Drivers of Land Cover Conversion in Period
57.3
20062007
Belama Phase IV
13.7
20022006
Land reclamation
179.2
20002002
Development of Port
95.3
19982000
General expansion
72.7
19891998
Belama Phases I-III Buttonwood Bay, general expansion N/A
Dec-89
2,089
845
8.45
42,518
5,032
383
42.6
19801989
Nov-80
1,706
691
6.91
39,771
5,756
N/A
N/A
N/A
Aerial photo by Emil A. Cherrington
Unsupervised Classification Basic Iterative Clustering Algorithm (K-Means)
Fuente: B. Howell (2006)
•
Place K points into the feature space containing the samples to be clustered. These points represent initial group centroids.
•
Assign each sample to the group that has the closest centroid.
•
When all samples have been assigned, recalculate the positions of the K centroids.
•
Repeat Steps 2 and 3 until the centroids no longer move.
Unsupervised Classification Basic Iterative Clustering Algorithm (K-Means)
Fuente: B. Howell (2006)
•
Place K points into the feature space containing the samples to be clustered. These points represent initial group centroids.
•
Assign each sample to the group that has the closest centroid.
•
When all samples have been assigned, recalculate the positions of the K centroids.
•
Repeat Steps 2 and 3 until the centroids no longer move.
Unsupervised Classification Basic Iterative Clustering Algorithm (K-Means)
Fuente: B. Howell (2006)
•
Place K points into the feature space containing the samples to be clustered. These points represent initial group centroids.
•
Assign each sample to the group that has the closest centroid.
•
When all samples have been assigned, recalculate the positions of the K centroids.
•
Repeat Steps 2 and 3 until the centroids no longer move.
Unsupervised Classification Basic Iterative Clustering Algorithm (K-Means)
Fuente: B. Howell (2006)
•
Place K points into the feature space containing the samples to be clustered. These points represent initial group centroids.
•
Assign each sample to the group that has the closest centroid.
•
When all samples have been assigned, recalculate the positions of the K centroids.
•
Repeat Steps 2 and 3 until the centroids no longer move.
Unsupervised Classification Improving clustering
Fuente: B. Howell (2006)
•
“Real world” sample distributions are much more likely to be irregularly shaped with cluster axes rotated to each other.
•
These distributions are better characterized by parametric statistics.
•
Simple n-dimensional space segregation is more likely to assign pixels to incorrect clusters.
Unsupervised Classification Improving clustering
Fuente: B. Howell (2006)
•
“Real world” sample distributions are much more likely to be irregularly shaped with cluster axes rotated to each other.
•
These distributions are better characterized by parametric statistics.
•
Simple n-dimensional space segregation is more likely to assign pixels to incorrect clusters.
Unsupervised Classification Improving clustering
Fuente: B. Howell (2006)
•
“Real world” sample distributions are much more likely to be irregularly shaped with cluster axes rotated to each other.
•
These distributions are better characterized by parametric statistics.
•
Simple n-dimensional space segregation is more likely to assign pixels to incorrect clusters.
Unsupervised Classification ISODATA (Iterative Self-Organizing Data Analysis Technique)
Fuente: B. Howell (2006)
•
Operates in the same iterative fashion as K-Means with three significant differences…
•
Uses parametric statistics to describe clusters and determine nearest centroids
•
New clusters can formed by splitting “elongated” clusters into multiples
•
Clusters with centroids that are “too close” can be lumped together
•
Because of these differences, K becomes a “desired” number of final classes, not an absolute
Unsupervised Classification ISODATA (Iterative Self-Organizing Data Analysis Technique)
Fuente: B. Howell (2006)
•
Operates in the same iterative fashion as K-Means with three significant differences…
•
Uses parametric statistics to describe clusters and determine nearest centroids
•
New clusters can formed by splitting “elongated” clusters into multiples
•
Clusters with centroids that are “too close” can be lumped together
•
Because of these differences, K becomes a “desired” number of final classes, not an absolute
Unsupervised Classification ISODATA (Iterative Self-Organizing Data Analysis Technique)
Fuente: B. Howell (2006)
•
Operates in the same iterative fashion as K-Means with three significant differences…
•
Uses parametric statistics to describe clusters and determine nearest centroids
•
New clusters can be formed by splitting “elongated” clusters into multiples
•
Clusters with centroids that are “too close” can be lumped together
•
Because of these differences, K becomes a “desired” number of final classes, not an absolute
Unsupervised Classification ISODATA (Iterative Self-Organizing Data Analysis Technique)
Fuente: B. Howell (2006)
•
Operates in the same iterative fashion as K-Means with three significant differences…
•
Uses parametric statistics to describe clusters and determine nearest centroids
•
New clusters can formed by splitting “elongated” clusters into multiples
•
Clusters with centroids that are “too close” can be lumped together
•
Because of these differences, K becomes a “desired” number of final classes, not an absolute
Unsupervised Classification ISODATA (Iterative Self-Organizing Data Analysis Technique)
Fuente: B. Howell (2006)
•
Operates in the same iterative fashion as K-Means with three significant differences…
•
Uses parametric statistics to describe clusters and determine nearest centroids
•
New clusters can formed by splitting “elongated” clusters into multiples
•
Clusters with centroids that are “too close” can be lumped together
•
Because of these differences, K becomes a “desired” number of final classes, not an absolute
a) ISODATA initial distribution of five hypothetical mean vectors using ±1σ standard deviations in both bands as beginning and ending points. b) In the first iteration, each candidate pixel is compared to each cluster mean and assigned to the cluster whose mean is closest in Euclidean distance. c) During the second iteration, a new mean is calculated for each cluster based on the actual spectral locations of the pixels assigned to each cluster, instead of the initial arbitrary calculation. This involves analysis of several parameters to merge or split clusters. After the new cluster mean vectors are selected, every pixel in the scene is assigned to one of the new clusters. d) This split– split–merge– merge–assign process continues until there is little change in class assignment between iterations (the T threshold is reached) or the maximum number of iterations is reached (M (M).
Jensen, 2005
a) Distribution of 20 ISODATA mean vectors after just one iteration using Landsat TM band 3 and 4 data of Charleston, SC. Notice that the initial mean vectors are distributed along a diagonal in two--dimensional feature space two according to the Âą2Ďƒ standard deviation logic discussed. b) Distribution of 20 ISODATA mean vectors after 20 iterations. The bulk of the important feature space (the gray background) is partitioned rather well after just 20 iterations. Jensen, 2005
Jensen, 2005
Plot of the Charleston, SC, Landsat TM training statistics for five classes measured in bands 4 and 5 displayed as cospectral parallelepipeds. The upper and lower limit of each parallelepiped is Âą1Ďƒ. The parallelepipeds are superimposed on a feature space plot of bands 4 and 5.
Jensen, 2005
ISODATA Clustering Logic
Jensen, 2005
Classification Based on ISODATA Clustering
Jensen, 2005
Clasificaci贸n Supervisada
Clasificación supervisada 1) estimación de similitud espectral 2) asociación de tipos espectrales con clases útiles SUPOSICION BASICO Objetos de interés tienen “firmas espectrales” claros *Esto no siempre es el caso A veces hay que modificar la data para que sea real - combinaciones de bandas - imágenes multi-temporales
Clases, áreas de muestras, y mímicos Clase: Unido deseado, en la forma de un cluster espectral 2 atributos: identidad y firma espectral Área de muestra: una región de una imagen que es un buen ejemplo de un clase; se utilice para definir clústeres espectrales de una clase especifica; se define la identidad con foto-interpretación o información del campo Mímicos: otros unidos de mapeo con clústeres similares
Clasificación supervisada Supervised classification involves imposing a priori information classes on a landscape. Implicit in the supervised classification process is the notion that the spectral data of members of a class will have similar statistical characteristics and that those characteristics can be visually discerned and manually segregated by a human. The process of creating a class structure and determining the statistical characteristics of each class is called training. Training is the method by which a classifier “learns” the appearance of individual classes. For the classifier to be successful in properly assigning pixels to a class, the training samples must be “pure” (consisting of class members only).
Fuente: B. Howell (2006)
Clasificación supervisada • Filosofía y estrategia de entrenamiento manual • Seleccionar las bandas insumos con mayor información • Mostrar combinaciones de bandas con mayor contraste entre clases • No seleccionar muestras que no son miembros del clase de interés • Examinar histogramas para determinar si las muestras son buenas (separables) • Seleccionar un rango de muestras de un clase, y combinarlos antes de la clasificación
Fuente: B. Howell (2006)
Parallelepiped
Maximum Likelihood
Minimum Distance
Hybrid
Classifiers Parallelepiped • Determines class membership using parallelepipeds (n-dimensional “boxes” in feature space) • Advantages: • Very fast • Can classify 100% of candidates • Makes good-looking output • Disadvantages: • Candidates that fall outside any parallelepiped remain unclassified • Candidates that fall in overlaps are assigned to the “first” parallelepiped • Poorly matched to normal data distributions
Fuente: B. Howell (2006)
Classifiers Minimum Distance • Determines class membership by measuring distance from class centroid • Advantages: • Fast • More accurate* classification than Parallelepiped • Better than Parallelepiped for handling “real world” data distributions • Disadvantages: • Candidates that fall outside distance limits remain unclassified • Candidates that fall in overlaps are assigned by an operator defined rule • Imperfectly matched to normal data distributions *assuming you know how to make it so
Fuente: B. Howell (2006)
Classifiers Maximum Likelihood • Determines class membership using parametric statistics • Advantages: • Very well matched to normal data distributions • More accurate classification* than Parallelepiped or Minimum Distance • Candidates that fall into overlaps are assigned based on likelihood of membership • Disadvantages: • Candidates that fall outside any parallelepiped remain unclassified • Accuracy heavily dependent on normal data distributions *assuming you know how to make it so
Fuente: B. Howell (2006)
Classifiers Hybrid • Determines class membership using parametric and nonparametric techniques • Advantages: • Fast and very accurate* • Perform first-order classifications using Parallelepipeds • Perform second-order classification on outliers and overlaps using distance or likelihood rule • Disadvantages: • Virtually every mistake that can be made using Parallelepiped, Minimum Distance, and Maximum Likelihood classifiers can be achieved in a single operation *assuming you know how to make it so
Fuente: B. Howell (2006)
Análisis de Mezclas Espectrales •Imágenes multiespectrales miden spectra integrada en cada píxel •Cada píxel contiene materiales diferentes, cada con su firma espectral diferente •Varios tipos de spectra usualmente están mezclados. Esos son mezclas. •Otros tipos no mezcla mucho. Fuente: A. Gillespie, la Universidad de Washington
Usualmente, el numero de clases (‘endmembers’) útiles para datos de Landsat es 4-5 Puede ser 8-10 para datos híper-espectrales Hay muchos componentes espectrales en varias escenas, pero usualmente no mezclan, entonces no son útiles.
Análisis de mezclas espectrales es útil porque – 1) Genera imágenes de fracciones que se puede entender fácilmente 2) Reducción en la dimensionalidad de los datos sin botar mucha información útil 3) Identificación de efectos topográficos para mas estable información para análisis en SIG
Sombra
100
10 90 20
80
30
70
40
60
50 50
Soil Soil
60
40
70
30
80
20
90
10
100
10
20
Vegetaci贸n verde Fuente: UW ESS 421 (2004)
30
40
50
% suelo
60
70
80
90
100
Suelo
Fuente: Lu et al (2002)
Imagen de Landsat5 de un parte de la Reserva Forestal Nacional Gifford Pinchot de los EE.UU.
Fuente: UW ESS 421 (2004)
Mature regrowth
Old growth
Burned Immature regrowth
Broadleaf Deciduous
Grasses Clearcut
Fuente: UW ESS 421 (2004)
Claro = abundancia del objeto
Vegetacion no fotosintetica (NPV)
Vegetacion verde
Rojo = NPV Verde = veg. verde Azul = sombra Sombra
Fuente: UW ESS 421 (2004)
SMA can easily extract areas of bare soil
Interpreting spectral unmixing results Color in RG comp
% Soil
% Photosynthetic Vegetation
Description
1
Yellow
Very high (90-100%)
Very high (90-100%)
Cropland
2
Light green
Low-Very Low (0-30%)
High (80-100%)
Open forest
3
Light green
Medium (60-70%)
Medium (40-50%)
Shrubland
4
Dark green
Very low (0-10%)
Medium (40-80%)
Closed forest
5
Red
Very high (90-100%)
Low (10-20%)
Bare land / urban
6
Coffee brown
Low (10-30%)
Low-Medium (30-50%)
Mangrove scrub
7
Black
Very low (0-10%)
Low-Very Low (0-20%)
Water
Black
Very low (0-10%)
Low-Medium (20-40%)
Wetland
Color Code
CATHALAC (unpublished, Nov 2009)
Interpreting spectral unmixing results
Color in RG comp
% Soil
% Photosynthetic Vegetation
Description
1
Yellow
Very high (90-100%)
Very high (90-100%)
Cropland
2
Light green
Low-Very Low (0-30%)
High (80-100%)
Open forest
3
Light green
Medium (60-70%)
Medium (40-50%)
Shrubland
4
Dark green
Very low (0-10%)
Medium (40-80%)
Closed forest
5
Red
Very high (90-100%)
Low (10-20%)
Bare land / urban
6
Coffee brown
Low (10-30%)
Low-Medium (30-50%)
Mangrove scrub
7
Black
Very low (0-10%)
Low-Very Low (0-20%)
Water
Black
Very low (0-10%)
Low-Medium (20-40%)
Wetland
Color Code
CATHALAC (unpublished, Nov 2009)
1.
2.
4. Closed tree canopy (e.g. ‘mature’ forest): Should be defined principally by low soil exposure and moderate to high chlorophyll content
1. Bare land (e.g. urban areas): Should be defined by high soil exposure and no chlorophyll content Source: ITTO / JOFCA
3.
4.
3. Open tree canopy (e.g. ‘open’ forest): Should be defined principally by some soil exposure and high chlorophyll content
2. Low vegetation (e.g. cropland, shrubland): Should be defined by high chlorophyll content and some soil exposure b/c of usual low plant density (i.e. no canopy)
Spectral Mixture Analysis works with spectra that mix together to estimate mixing fractions for each pixel in a scene. Spectral Mixtures, green leaves and soil
Reflectivity, %
100 80
0% leaves 25% leaves
60
50% leaves 40
75% leaves 100% leaves
20 0 0
1
2
3
Wavelength, micrometers
Please note – wavelength scale is messed up Source: TBD
The extreme spectra that mix and that correspond to scene components are called spectral endmembers.
Forest Spectral Endmembers
Reflectivity, %
100 80 dry grass
60
leaves
40
soil
20 0 0
1
2
Wavelength, micrometers
3
Endmembers from one type of scene – forest, lake, desert – form a cohort.
In a forest, important endmembers may be leaves, wood, shade, and soil. In a desert, leaves may be less important, but there may several rock types. Source: TBD
Shade
We can use a ternary diagram used to show mixtures of forest endmembers.
100
10 90 20
80
30
70
40
60
50 50 60
Soil Soil
We will see a detailed example of this in a later lecture
40
70
30
80
20
90
10
100
GV
10
Source: TBD
20
30
40
50
percent soil
60
70
80
90
100
Soil
Mature regrowth
Old growth
Burned Immature regrowth
Broadleaf Deciduous
Grasses Clearcut
Source: TBD
Round 1: 1st set of samples, selected from PPI of all MNF components
1: Unconstrained All MNF components
2: Constrained – Weight 1 All MNF components
3: Constrained – Weight All MNF components
Round 1: 1st set of samples, selected from PPI of all MNF components
4: Constrained – Weight 50 All MNF components
5: Unconstrained ALI bands 3,4,5,6,8,9
6: Constrained – Weight ALI bands 3,4,5,6,8,9
Round 2: 2nd set of samples, selected from PPI of first 3 components
7: Constrained – Weight 1 ALI bands 4,5,6,8
8: Constrained – Weight 1,000 ALI bands 4,5,6,8
9: Constrained – Weight 1 MNF components 1,2,3
Round 2: 2nd set of samples, selected from PPI of first 3 components
10: Constrained – Weight 1 11: Constrained – Weight 100 MNF components 1,3 MNF components 1,3
12: Constrained – Weight MNF components 1,3
Round 2: 2nd set of samples, selected from PPI of first 3 components
13: Unconstrained MNF components 1,3
14: Constrained – Weight 1 All MNF components
15: Constrained – Weight
RGB NDVI composite: 20002000-2005 2005--2010
Color Black Red Green Blue Yellow Magenta Cyan White
2000 Low High Low Low High High Low High
2005 Low Low High Low High Low High High
2010 Low Low Low High Low High High High
Cambio de cobertura: 20002000-2010
Cambio de cobertura: 2000--2010 2000 Color Black Red Green Blue Yellow Magenta Cyan White
Key No change: forest Regenerated 2000-05; no change 2005-10 Cut 2000-05, regenerated 2005-10 No change 2000-05; cut 2005-10 No change 2000-05; regeneration 2005-10 Regenerated 2000-05, re-cut 2005-10 Cut 2000-05; no change 2005-10 No change: non-forest
Color Black Red Green Blue Yellow Magenta Cyan White
2000 High Low High High Low Low High Low
2005 High High Low High Low High Low Low
2010 High High High Low High Low Low Low
No change: forest Regenerated 2000-05; no change 2005-10 Cut 2000-05, regenerated 2005-10 No change 2000-05; deforested 2005-10 No change 2000-05; regeneration 2005-10 Regenerated 2000-05, re-cut 2005-10 Cut 2000-05; no change 2005-10 No change: non-forest
Ă?ndices
A 째x B
NDx
NDy
NDy
B con sol
Sombra
째x
A con sol NDx
Línea de ratio constante
x/y
B y
A x
y/z
A °x B
NDx
NDy
NDy
B con sol
Sombra
°x
A con sol NDx
DespuĂŠs: LandSat7 - 11 de mayo de 2007
°
Plumas de humo
Cicatrices
0 0.5 1
2
3
Miles 4
Antes: LandSat7 - 21 de marzo de 2006
째
0 0.5 1
2
3
Miles 4
°
2006
Procesamiento Digital Principio: Se puede aplicar metodologías para extraer info útil de imágenes satelitales
0 0.5 1
2
3
Miles 4
°
2007
Normalized Difference Vegetation Index (NDVI): ratio entre la luz infrarrojo cercano (NIR) y rojo (R), indicando vegetación en estrés Normalized Burn Ratio (NBR): ratio entre infrarrojo medio (SWIR) y infrarrojo cercano (NIR), para delinear cicatrices (muy similar a NDVI) 0 0.5 1
2
3
Miles 4
LandSat: 21 March 2006
째
NDVI: March 2006 High : 0.964912
Low : -0.957447
0 0.450.9
1.8
2.7
Miles 3.6
LandSat: 11 May 2007
째
Contamination por el humo
NDVI: May 2007 High : 0.964912
Low : -0.957447
0 0.450.9
1.8
2.7
Miles 3.6
Differencing 2007 NDVI against 2006 NDVI
째
NDVI difference High : 0.919945
Low : -1.04501
0 0.450.9
1.8
2.7
Miles 3.6
LandSat: 21 March 2006
째
Normalized Burn Ratio High : 0.978947
Low : -0.931034
0 0.450.9
1.8
2.7
Miles 3.6
LandSat: 11 May 2007
째
Normalized Burn Ratio High : 0.978947
Low : -0.931034
0 0.450.9
1.8
2.7
Miles 3.6
Differencing 2007 NBR against 2006 NBR
째
Normalized Difference Burn Ratio -1.22 - -0.42 -0.42 - -0.31 -0.31 - -0.18 -0.18 - 0 0-1
0 0.450.9
1.8
2.7
Miles 3.6
째
NDBR
째
NDVI Diff.
Normalized Difference Burn Ratio -1.22 - -0.42
NDVI difference
-0.42 - -0.31
High : 0.919945
-0.31 - -0.18 -0.18 - 0
Low : -1.04501 0-1
0 0.450.9
1.8
2.7
Miles 3.6
째
2006
0 0.5 1
2
3
Miles 4
0 0.450.9
1.8
2.7
Miles 3.6
째
2007
0 0.5 1
2
3
Miles 4
LandSat7: 11 de mayo de 2007
掳
Estimaci贸n: ~24,000 acres quemadas
0 0.450.9
1.8
2.7
Miles 3.6
Validaci贸n
c. 2000
c. 2009
Datos de la cclasificaci贸n clasificaci贸n
Matrices de confusion / Matrices de error
Datos de validaci贸n A B C D E F A
480
0
B
0
52
C D
0
0
0
16
E F
0
0
0
0
480
68
5 0
Sumas de columnas
0 20
0 0
Sumas de filas
0
485
0
72
Class
no mangroves
mangroves
water
User accuracy
Total Number
Producer accuracy
Total class accuracy
%
mangroves
1
59
10
70
21%
1%
50%
26%
no mangroves
1
75
5
81
24%
93%
55%
74%
water
0
2
186
188
55%
99%
93%
96%
Total
2
136
201
339
100%
50%
55%
93%
mangroves
no mangroves
water
Producer Accuracy
Class
77.3%
User accuracy
Total Number
Producer accuracy
Total class accuracy
%
mangroves
2
77
6
85
26%
2%
100%
51%
no mangroves
0
54
5
59
18%
92%
39%
65%
water
0
7
182
189
57%
96%
94%
95%
Total
2
138
193
333
100 %
100%
39%
94%
Producer Accuracy
71.5%
Fuentes de Mayor Información Libros (vea www.amazon.com) • Teledetección Ambiental (2002) – Emilio Chuvieco • Remote Sensing and Image Interpretation (2007) – Thomas Lillesand, Ralph Kiefer, Jonathan Chipman • Remote Sensing of the Environment (2006) – John R. Jensen • Remote Sensing for GIS Managers (2005) – Stan Aronoff En línea • TELEDET: http://www.teledet.com.uy/tutorial-imagenessatelitales/imagenes-satelitales-tutorial.htm
• NASA: http://rst.gsfc.nasa.gov/ • CATHALAC: servir@cathalac.org • GOOGLE / Wikipedia
Referencias / Reconocimientos Mucha de la información en este presentación fue adoptada de los siguientes fuentes: • Burgess Howell, NASA GSFC (2006) • Jason Tullis, University of Arkansas (2005) • Lecturas / materiales del curso ESS 421 y ESS 422 de la U. de Washington (2004)
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