Programa Reducción de Emisiones de la Deforestación y … · 2019-09-29 · Programa Reducción...
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Programa Reducción de Emisiones de la Deforestación y Degradación de Bosques en Centroamérica y República Dominicana (REDD – CCAD – GIZ): Interfaz de Manejo Simplificado para el Sistema de Alerta Temprana PN 08.2211.4-001.00 Consultor: Jeffrey R. Jones. Para: IMPLEMENTACIÓN DEL SISTEMA REGIONAL DE MONITOREO DE DEFORESTACION Y DEGRADACIÓN DE BOSQUES PARA CENTROAMÉRICA Y REPÚBLICA DOMINICANA. Contrato No.: 83115498 Agencia COSTA RICA Informe de Avance: Producto 1: 17 agosto de 2012 Productos 1. Interfaz Versión 2 con visualizadores y nuevos métodos 2. Articulo científico
Transcript of Programa Reducción de Emisiones de la Deforestación y … · 2019-09-29 · Programa Reducción...
Programa Reducción de Emisiones de la Deforestación y Degradación
de Bosques en Centroamérica y República Dominicana
(REDD – CCAD – GIZ):
Interfaz de Manejo Simplificado para el Sistema de Alerta Temprana
PN 08.2211.4-001.00
Consultor: Jeffrey R. Jones.
Para: IMPLEMENTACIÓN DEL SISTEMA REGIONAL DE MONITOREO DE
DEFORESTACION Y DEGRADACIÓN DE BOSQUES PARA
CENTROAMÉRICA Y REPÚBLICA DOMINICANA.
Contrato No.: 83115498 Agencia
COSTA RICA
Informe de Avance: Producto 1: 17 agosto de 2012
Productos 1. Interfaz Versión 2 con visualizadores y nuevos métodos 2. Articulo científico
Tabla de contenido
1. Interfaz VCF Versión 2 con visualizadores y nuevos métodos
a. Visualizador VCF y Visualizador Amenaza Forestal
i. Capacidades
ii. Métodos
1. VisualizarVCF
2. AmenazaImg_Event
3. Secuencia_VCF_Event
2. Eliminación de áreas no clasificadas
a. Bagging ('Bootstrap Aggregation') y promediar resultados
b. Comparación de promedio y desviación estándar
i. Método meantop3
c. Detección y control de áreas nubosos
i. reliability0count01_11.tif
ii. RegionRel3CloudStack.tif
iii. Reliability3SummaryRegion3Freq.tif
iv. RegionRel3CloudMaski.tif
3. Exportación de archivo VCF_promedio.tif en Alerta Temprana
4. Artículo científico
a. MODIS Based REDD+ Regional Monitoring System for Forest Cover
Reporting of Central America and the Dominican Republic
b. 'International Journal of Forestry Research'
i. Tópico especial
5. Anexos
a. Articulo 'MODIS Based REDD+ Regional Monitoring System for Forest
Cover Reporting of Central America and the Dominican Republic'
Interfaz VCF Versión 2
Un limitante de la Interfaz Grafico Versión 1 para VCF-GIZ fue su dificultad en
comunicar resultados a audiencias no técnicas, especialmente de la secuencia de datos
VCF. En un principio la Interfaz fue creada para eliminar el uso de la línea de comando
y llevar el procesamiento de datos MODIS para la creación de la imagen VCF a un
ambiente más reconocible tipo Windows. El desarrollo de la Versión 2 tuvo como
objetivo un método de presentación de datos para el usuario y para la divulgación de
resultados.
El uso de la secuencia VCF para la presentación y el análisis de comportamiento forestal
agrega un componente dinámico al análisis que aprovecha de la capacidad de rápida
actualización de MODIS/VCF. La secuencia VCF presenta cambios anuales en la
densidad de cobertura de bosque con una discriminación fina de estados de densidad. La
secuencia VCF ayuda a visualizar dinámicas complejas de cobertura forestal, producto de
intervención humana al igual que eventos naturales. Esta dinámica no ha sido accesible
con el monitoreo en el pasado por los largos periodos de actualización de datos, que
suelen ser de 5 a 10 años entre actualizaciones de mapas de cobertura forestal. Sin
embargo, la dinámica de cambio, y los datos que se utilizan para describirla, llegan a
llenar un vacío muy importante en el monitoreo para efectos de REDD o REDD+.
En un inicio el acceso a la visión dinámica de la secuencia VCF fue limitado a los
usuarios de ENVI; aunque ENVI representa la tecnología de punta en el análisis de
imágenes (es un software muy ampliamente difundido dentro de NASA), su alto costo y
la tradición de uso de ArcGIS y ERDAS resulta en pocas licencias para el uso de este
paquete. Aunque otros paquetes de software tienen la capacidad de desplegar las
imágenes VCF, les falta herramientas como el 'perfil Zeta' que ayuda en la comprensión
de las secuencias VCF.
Figure 1: Tres ventanas de la opción 'Visualizar VCF'
La Versión 2 de la Interfaz VCF se dirige principalmente a la presentación de los
resultados únicos del VCF, que son la presentación de tendencias de cambio de densidad
en el mediano plazo, y la clasificación de bosque específicamente en cuanto a sus
características dinámicas de VCF.
A la Interfaz VCF Versión 1 se agregó dos botones; 'Visualizar VCF' y 'Visualizar
Amenazas' (Figura 1). Estos botones inician tres ventanas de información sobre el VCF.
En el caso de Visualizar VCF, las tres ventanas son 1) una ventana general de la región 2)
una ventana magnificada alrededor de un punto seleccionado con el mouse sobre la
ventana general y 3) una ventana de Perfil Zeta, que presenta todos los valores de la
secuencia VCF.
De las tres ventanas de la Versión 2, 'Grafico Secuencia VCF' es la más interesante.
Cada valor VCF es el porcentaje de cobertura forestal del pixel en un año particular.
Cuando se visualiza como secuencia ilustra procesos de cambio (o no cambio) con una
claridad que presta para una mayor comprensión de los procesos actuales. Por ejemplo,
la secuencia ilustra la importancia de la recuperación forestal en la dinámica de
cobertura; donde la metodología IPCC enfoca en buscar áreas de bosque que cambian a
áreas no-bosque, la secuencia VCF presenta dinámicas más complejas generadas por la
recuperación del bosque.
En la Figura 1 en la ventana 'Grafico Secuencia VCF' se presenta una secuencia donde
un bosque sufre una reducción de su cobertura de hojas, y se recupera en forma repetida.
El eje vertical de la ventana Grafico Secuencia VCF describe la densidad de bosque, y el
eje horizontal marca el paso de los años; la línea solida traza los cambios observados en
los valores VCF, y la línea intermitente describa la línea de tendencia calculada en base
de los valores observados. Esta secuencia ocurre en los lechos de un rio, entonces podría
ser resultado de crecidas de agua en el cauce del rio, o un proceso de manejo de los
sedimentos cerca al río con fines agrícolas o pecuarias. Lo que llama la atención es la
capacidad de recuperación del bosque, que después de sufrir la defoliación, repone hojas
en un año siguiente para seguir como bosque vivo. El Perfil Zeta dibuja esta dinámica de
una manera que nunca se ve en monitoreo con actualizaciones menos frecuentes.
Las ventanas regionales y de magnificación presentan los datos VCF como imagen de
'falso color', donde las intensidades de RGB (rojo, verde y azul) se deriva de los valores
de VCF en el primer, quinto y último año de la secuencia. La presentación a color da una
impresión general de la condición del bosque. Los áreas más claros son los que tienen
valores altos en cada año que compone RGB; es decir, son áreas de alta densidad de
arboles cuya densidad no cambia mucho durante el periodo. Los colores más oscuros
indican VCF bajos, por ejemplo en zonas agrícolas o en zonas de inundación donde crece
poca vegetación (vea especialmente las zonas de lagunas en la costa de la moskitia).
El botón de 'Visualizar Amenazas' presenta una interpretación de los datos VCF que sale
del análisis 'Alerta Temprana'. Al igual que 'Visualizar VCF' abre tres ventanas, 1) de
vista general, 2) de magnificación y 3) de leyenda. El mapa de amenazas es una
combinación de diferentes informaciones de la Alerta Temprana; 0) agua y áreas fuera de
la región del estudio Centroamérica y República Dominicana, 1) tierra (área sin bosque),
2) otros bosques, 3) área de bosque con VCF estable, 4) bosque con VCF inestable, y 5)
Alerta Temprana. Los colores de la leyenda están programados para asociar con estas 6
clases, entonces el mapa de amenazas puede ser actualizado y desplegado siempre que
utiliza códigos 0 a 5; el cambio de colores o las definiciones de la leyenda requieren una
modificación del código base del modulo.
Las categorías del mapa están definidos desde las mascaras de tierra y de bosques de la
región, generado en base de los mapas forestales provistos por el proyecto REDD-
CCAD-GIZ, de donde salen las categorías 'agua' y 'tierra'. Las categorías de bosque se
generan en base de los valores del promedio VCF, el pendiente de la tendencia de la
secuencia VCF para cada pixel, y la desviación estándar de los valores de cada pixel. Las
definiciones de estas clases son;
– Bosque VCF estable – desv < 10, abs(pend) < 3, VCF Promedio > 70
– Bosque VCF inestable – desv > 10, abs(pend) < 3
– Alerta temprana – desv < 10, abs(pend) < 3, ΔVCF>50
– Otros bosques (que no caen en las categorías estable o inestable)
Figure 2 Visualizar Amenazas
El mensaje que presenta el mapa de amenazas es que los bosques existen en diferentes
condiciones y calidades. Hay mucho del bosque de la región que está sufriendo
degradación que no llega al punto de deforestación (tomando como referencia una
densidad de 30% de cobertura de copa), pero que puede representar la pérdida de más de
50% del contenido de carbono del bosque. Los bosques con VCF inestable tienen
variaciones grandes en el VCF entre los diferentes años, tal como se presenta en la Figura
1. Estos bosques pierden volumen de carbono, y pierden capacidad de secuestro de
carbono, por la reducción o la eliminación de su capacidad fotosintética para ciertos
periodos, e inclusive pueden estar en peligro de daños más severos.
'Bootstrap aggregation' y la eliminación de brechas en VCF causadas por nubes
El aspecto menos satisfactorio de las coberturas VCF son los efectos de áreas con
coberturas casi permanentes de nubes. En la Figura 3 se compara imágenes de
Talamanca, en Costa Rica; un VCF final en Lat/long, comparada con una imagen
compuesta de 16 días en proyección Sinusoidal (por eso las imágenes tienen formas y
orientaciones un poco distintos) del mes de junio. En la imagen VCF hay zonas que
reportan poca o ninguna cobertura de bosque en las montañas del lado Caribe de
Talamanca. Es una zona conocida por sus bosques extensos. Sin embargo, se ve
claramente la persistencia de nubes en ciertos puntos de la imagen compuesta que
corresponden a 'brechas' en la cobertura VCF.
Varias estrategias fueron examinadas para detectar y eliminar estas brechas, empezando
con cambiar el modelo del árbol de decisión, siguiendo a una selección de pixeles basada
en características estadísticas, y finalmente en el análisis de índices de calidad de pixeles.
Figure 3 VCF y MOD13Q1 Talamanca, Costa Rica
La primera estrategia fue la implementación del árbol de decisión tipo M5 con 'Bootstrap
Aggregation', con la hipótesis que la aplicación de un árbol de decisión más sofisticada
ayudaría a resolver el problema de pocos datos en ciertos áreas. El modelo del árbol de
decisión utilizado en el VCF-GIZ es el 'Replicating Tree', sin uso de poda de ramas del
árbol. El modelo M5 permite poda para eliminar clases de poca probabilidad. Además,
el 'Bootstrap Aggregation' (Bagging) genera una serie de arboles de decisión variando los
puntos de inicio del análisis y finalmente saca un promedio de todas las clasificaciones
generadas.
Los resultados del M5 con Bagging no llegaron a la altura que se esperaba. Primero,
requiere un procesamiento mucho más largo por la estructura del modelo; asigna pixeles
a clases, y cada clase tiene una fórmula para integrar datos de diferentes métricas para
generar un resultado final, mientras que el Replicating Tree requiere un solo paso de
clasificación y asignación del valor final. Además, la aplicación del bagging requiere la
repetición de la clasificación entera entre
10 y 30 veces. ENVI no soporta M5
entonces fue necesario desarrollar un
modulo especial para el análisis. Aun
así, muchas clasificaciones duraba
muchas horas en finalizar para una sola
tessera.
Obviamente, con una clasificación
exitosa no habrá argumento en contra al
uso del sistema más tardado en ejecutar.
Sin embargo, el resultado en general del
método fue una homogenización de
valores, por el efecto de promediar
resultados de muchas iteraciones del
árbol de decisión.
El resultado del M5 con Bagging se
presenta en la figura 4 para la misma
zona que la figura 3 elimina algunos de
los valores bajos en las zonas de nubes, pero igualmente sube los valores de cobertura
aun en zonas agrícolas del Valle de la Estrella y Changuinola, que les hacen parecer con
mas cobertura de bosque. En vista del resultado pobre de varias clasificaciones, se
decidió abandonar la aplicación del modelo M5.
Otra estrategia fue por medio del análisis estadístico. Dentro del modulo de la Alerta
Temprana se insertó unas rutinas para detectar pixeles dudosos, y corregirlos. La bondad
de este método es que el
programa Alerta Temprana ya
está hecha, y los datos
estadísticos estaban
disponibles como parte del
procesamiento de la Alerta.
El criterio de selección
aplicada fue una comparación
del valor promedio del pixel
(Promedio VCF de la
secuencia de capas) con las
desviación estándar del mismo
pixel. En la Figura 5 se
presenta el perfil de valores
VCF para una zona nubosa.
Los valores alternan entre
valores muy altos a valores casi 0, ya que cubierto de nubes la zona devuelva una firma
espectral sin árboles. Como prueba de contaminación de nubes cada pixel fue evaluada a
Figure 4 Resultado de M5 con Bagging
Figure 5 Perfil Zeta Zona Nubosa Talamanca
determinar si la desviación estándar superaba el promedio VCF; esta estrategia no logra
identificar todas las zonas nubosas, entonces la corrección termina incompleta.
En la Figura 6 se presenta la corrección a la derecha, que demuestra una reducción en los
áreas de nubes, y mantiene las zonas agrícolas correctamente, pero deja sin cambiar la
mayoría de las brechas de nubes.
Esta metodología se basaba en líneas de código para la identificación de las brechas, y
otro código que extrae el promedio de los 3 valores más altos en la zona de nube. Si la
brecha pasa mucho cubierto de nubes que van a generar VCF casi 0, entonces los valores
más reales son los más altos. La rutina meantop3 recibe el vector de valores de la
secuencia VCF para el pixel identificado como nuboso, aplica un 'bubble sort' para
identificar los valores más altos, y devuelva el promedio de los 3 valores más altos.
function meantop3, origpoints, npoints, lastlayer
done = 0
nowpoints = make_array(npoints+1, /integer)
nowpoints[0:npoints-1] = origpoints
nowpoints[npoints] = lastlayer
while not done do begin
done = 1
for s = 1,npoints do begin
if nowpoints[s-1] lt nowpoints[s] then begin
tmpval = nowpoints[s-1]
nowpoints[s-1] = nowpoints[s]
nowpoints[s] = tmpval
done = 0
endif
endfor
endwhile
avgtop3 = byte(round((nowpoints[0] + nowpoints[1] + $
nowpoints[2])/3))
return, avgtop3
end
Figure 6 Corrección basada en desviación estándar y promedio
La rutina meantop3 se llama desde el interior de la rutina AlertaTemprana con el
siguiente código;
if reliabilityvec[i] eq 1 then begin
newval = meantop3(y, vcfvars2_limit+1, vcfvect_last[i])
vcfvect[i,vcfvars2_limit] = newval
endif else begin
vcfvect[i,vcfvars2_limit] = vcfvect_last[i]
endelse
Estas rutinas son de interés permanente, porque sirven para aplicar la corrección con
cualquier estrategia de definición de pixeles nublados, con solo que se pone en la forma
de un vector de píxeles en el archivo VCF.
Como última estrategia para identificar y corregir brechas por nubes se volvió a los
índices de calidad de pixel que se utiliza en la construcción de las compuestas de 32 días.
La construcción de las compuestas busca el mejor pixel, según varios criterios, pero no
excluye pixeles con nubes con la expectativa que contiene alguna información que podría
ser útil. Los índices de calidad de los pixeles están actualizados para indicar la calidad
del pixel escogido, y grabado en el archivo de la imagen compuesta.
Para el análisis de nubes se utilizaba la banda Reliability, que tiene 3 valores relevantes; 0
para pixeles sin contaminación, 1 para pixeles con algo de contaminación, y 3 para
pixeles nubosos. Se evaluó dos valores; el 0 y el 3.
Todos los archivos de compuestas mensuales fueron resumido para llegar al dato de
cuantos meses de cada año tenia pixeles Reliability0, sin contaminación. Cuando se
compara la distribución de pixeles Reliability0 con las brechas de nubes no lleva una
relación muy
cercana.
El resumen de
los pixeles
Reliability3 da
mejor resultado.
Detecta que hay
pixeles hasta
con un máximo
de 105 meses
con pixeles
nublados (de los
120 meses en el
periodo
analizado), pero
que 95% de los
pixeles forestales tienen menos que 25 meses con pixeles nublados. En la figura 7 se ve
la buena correspondencia entre la concentración de altos números de pixeles nublados y
Figure 7 Comparacion VCF con Reliability3
las brechas en la imagen VCF. Como ejemplo, el pixel en el centro del cuadro rojo tiene
58 días nublados del total de 120 meses. En la Figura 8, se aprecia la distribución de la
máscara de pixeles nublados con un umbral de 25 pixeles nublados.
Figure 8 Mascara Reliability3 Umbral 25 Días
En contraste, en la zona del canal de Panamá los pixeles nublados son menos frecuentes.
En una aparente contradicción, la distribución de pixeles Reliability0 clasifica la zona del
Canal muy similar a Talamanca, mientras que Reliability3 demuestra que la zona del
Canal en particular tiene una cobertura de nubes reducida.
Figure 9 Mascara nubes Canal de Panama
Esta aparente contradicción se debe a la existencia de mas pixeles parcialmente
contaminadas en la zona del Canal, que todavía permiten en análisis VCF a pesar de su
relativamente baja calidad.
Conclusión Interfaz
La incorporación de los visualizadores de VCF y de Amenazas cumple con varios
objetivos. Primero, provee una manera inmediata de uso de los datos VCF, ya que la
interfaz se construye bajo una modalidad de IDL-ENVI que permite su distribución y uso
con instalaciones IDL sin licencia; es decir, se puede instalar una copia 'no licenciada' y
gratis de ENVI en una computadora y utilizar la Interfaz VCF en ese ambiente. El
usuario estará limitado en que no puede utilizar las opciones del programa ENVI o IDL
en forma independiente y no puede editar el código del programa en IDL, pero sí puede
ejecutar el archivo compilado de la Interfaz sin restricción. Segundo, facilita la
comprensión de los datos VCF por parte de los técnicos capacitados en el programa
REDD-CCAD-GIZ, ya que pueden revisar datos y compararlos con otros datos de sus
ministerios sin la licencia ENVI ni con un conocimiento profundo de los programas
ENVI o IDL. Tercero, la Interfaz permite la presentación de datos VCF a decisores para
apoyar sus conclusiones o recomendaciones en cuanto al manejo de asuntos REDD.
La leyenda del mapa de amenazas a bosques requiere una revisión 'política', ya que en la
categoría de VCF inestable se unen áreas en proceso de variación por actividad humana,
y zonas naturales con variaciones en VCF por causa de inundaciones. Un caso en
particular es el noreste del Peten de Guatemala, que contiene un bosque inundable con
relativamente poca intervención humana y una gran variabilidad en el VCF. Hay que
buscar una terminología que aclara que no toda variación en el VCF es por causa
humana.
Para mejorar el aspecto visual de las imágenes VCF, es necesario aplicar una corrección
para zonas nubosas. El análisis de valores Reliability3 sugiere que una máscara con un
umbral de 25 meses nubosos en la década identifica zonas cuyo valor VCF está
claramente afectado por las nubes. La precisión de los pixeles bajo la máscara de nubes
puede mejorarse por medio del análisis de la secuencia VCF y la aplicación de valores
del último año en que un valor realista VCF fue posible; esta corrección obedece a la
misma lógica que la construcción de las compuestas de 32 días, en el sentido que se
extiende el periodo de buscada de meses a años en vista de las condiciones
meteorológicas. Como sugerencia, se puede construir unas capas índices para los VCF
que indica cuales son las zonas de máscara nubes, y cuál es la frecuencia con que se
obtiene datos razonables para su corrección.
Artículo científico
En vista del interés en dar publicidad al Proyecto REDD-CCAD-GIZ, se han hecho varias
presentaciones del sistema de monitoreo en talleres y capacitaciones. Los sitios de estas
presentaciones han sido en el IICA, el CATIE, el CENAT (todos en Costa Rica) y una
presentación en Quebec, Montreal, Canadá.
En octubre 2011 el sistema de monitoreo fue presentado en una conferencia en Laval
University en Quebec, Canadá. El titulo de la conferencia fue 'Taller Internacional sobre
la Forestería Comunitaria en el Marco de REDD+'. Los organizadores de la conferencia
buscaron por medio de contactos en el CATIE alguien para hacer una presentación de un
sistema regional de monitoreo, entonces se hizo la presentación 'Sistema Regional VCF
para el Monitoreo de Deforestación y Degradación de Bosques para Centroamérica y la
República Dominicana', que incluía una descripción del proyecto REDD-CCAD-GIZ
como el contexto del desarrollo del sistema.
En el mes de noviembre después de la conferencia, una nota fue circulada por los
organizadores del interés de unir las presentaciones de la conferencia en un edición
especial de la revista 'International Journal of Forestry Research' sobre el tópico especial
'REDD+ Mechanism in Developing Countries'. Este aviso fue compartido con GIZ, pero
no se llegó a ninguna conclusión sobre el desarrollo de tal articulo inmediatamente. En
abril y mayo se iniciaron discusiones sobre el interés en dar seguimiento a la invitación,
entonces un tiempo fue reservado en el contexto de esta consultoría.
En la página web de la revista se publicó la siguiente información, en adición a las
invitaciones personales extendidas por los editores, quienes todos participaron en el
Taller de Quebec: REDD+ Mechanism in Developing Countries
PDF Call for Papers | HTML Call for Papers Guest Editors: Damase Khasa, Alison Munson, Mariteuw Chimère Diaw, Nancy Gelinas, Nadine T. Laporte, Glenn Bush Manuscript Due: Friday, 3 August 2012 Publication Date: Friday, 21 December 2012
Al final, se definió generar el articulo 'MODIS Based REDD+ Regional Monitoring
System for Forest Cover Reporting of Central America and the Dominican Republic',
como esfuerzo conjunto entre Abner Jiménez, Laszlo Pancel y el consultor. Un plan de
articulo fue desarrollado por medio del intercambio de ideas escritas y algunas
conferencias Skype; el articulo fue terminado y entregado a los editores de la revista el 2
agosto. En este momento, sigue en el proceso de evaluación y edición para definir si se
incluye en la revista.
El articulo en formato Word esta anexo a este informe al igual que el anuncio de la
edición especial.
Resumen
Durante este periodo se enfocó en tres tareas;
1) La generación del la Versión 2 de la Interfaz VCF, con una capacidad de desplegar
datos VCF sin requerir licencias de ENVI
2) La evaluación de alternativas para identificar las brechas en los valores VCF donde
hay cobertura persistente de nubes (para distinguirlas de zonas donde realmente no hay
cobertura forestal) y la identificación de una estrategia para remplazar los valores bajos
de las brechas con valores más realistas.
3) La creación de un artículo científico documentando el uso del Sistema de Monitoreo
VCF-GIZ en el contexto de REDD+
Materiales Anexos
1. Artículo Científico: JimenezJonesPancelMODISMonitoringCADR2.docx
2. Descripción de la edición especial para artículo:
IntJForResRMDC22march12AM.pdf
3. Recibido de articulo de editores de revisa (correo electronico)
4. Mascara Reliability3 Umbral 25: RegionRel3CloudMaski.tif
5. Modulo compilado VCF_InterfaceV2.sav
Anexo 1: Artículo Científico: JimenezJonesPancelMODISMonitoringCADR2.docx
MODIS Based REDD+ Regional Monitoring System for Forest Cover Reporting of Central America and
the Dominican Republic Abner J. Jiménez
1; Jeffrey R. Jones
2; Laszlo Pancel
3 1. Pasante de Doctorado en Tecnología de la Información Geográfica de la Universidad de Alcalá, Spain, y Especialista Sectorial del Programa Regional REDD-CCAD/GIZ. Email: [email protected]; [email protected] 2. Consultant, Regional REDD-CCAD/GIZ Program. Email: [email protected] 3. Asesor Principal, Programa REDD-CCAD/GIZ, San Salvador, El Salvador. email: [email protected] ABSTRACT REDD and REDD+ monitoring place special demands on remote sensing techniques. Forest degradation or enhancement need to be detected with annual or more frequent updates. The high temporal resolution of MODIS data compensates for its moderate spatial resolution by enabling the creation of monthly VCF (Vegetation Continuous Field) images, based on normalized phenological metrics which permit the differentiation of degradation from phenological cycling. Annual VCF images are analyzed to detect patterns of forest canopy change using basic statistics of annual values for individual pixel positions. A classification of pixel statistics distinguishes the class of Stable Non-Intervened Forest from Stable Intervened Forest, representing untouched natural forest stands, and forest stands experiencing a variety of human interventions, respectively. These classes cover 20% and 50% of the region's forest area. The larger area of Stable Intervened Forest mark regions where human efforts to enhance forest structure might be implemented and monitored for recognition under REDD guidelines. Key words: REDD, REDD+, MRV, MODIS, VCF, Monitoring, Central America, Dominican Republic 1. INTRODUCTION
Requirements for forest monitoring have changed significantly with the
introduction of REDD and REDD+ (Reduction of Emissions from Degradation and
Deforestation), with their focus on primary forest carbon, and an expanded
definition including secondary forest carbon, respectively. Both are referred to
hereafter as REDD, where the primary concern is the quantity of carbon, rather than
amounts of available lumber. REDD has also increased the expected tempo of forest
cover monitoring (MRV in REDD terminology - Monitoring, Reporting and
Validation), where results are required in a usable format on a monthly, or at most, a
yearly basis as a mechanism to document and justify monetary compensation for
carbon sequestration (GOFC-GOLD, [5]). Significantly, REDD also requires
information on forest degradation, the loss of carbon within standing forests, which,
as noted by Herold et.al. [11] represents nearly 10 times the carbon loss caused by
outright deforestation. The human population is the primary agent of different
forms of forest degradation and their extents, and its activities must be considered
in understanding and addressing degradation. Forest monitoring objectives require
information on multiple scales; broad region wide detection of current trends, and
detailed local information to confirm the existence and nature of the trends; the
MODIS VCF focus will focus on broad region wide data.
The MODIS based VCF methodology addresses several key problems of MRV,
especially timely coverage, and information on degradation. The MODIS satellite
constellation was launched expressly to monitor earth surface conditions, featuring
daily coverage and a high level of spatial and spectral accuracy. Early in MODIS'
implementation a research process was begun to improve on the AVHRR legacy-
monitoring-methods through the introduction of the VCF metric (Vegetation
Continuous Field - Hansen et.al. [6]). VCF represented a departure from earlier
forest monitoring efforts which focused on forest classification rather than carbon
measurements, because this was expected to provide more realistic information
regarding the status of carbon sinks in order to draw conclusions regarding Green
House Gas emissions and sequestration.
In spite of its lower spatial resolution, MODIS is a powerful tool for forest
monitoring because of its high temporal resolution. The smallest pixel size for
MODIS products is a nominal 250m, nearly 10 times the 30m of a LANDSAT pixel,
but the daily MODIS coverage greatly increases the probability of finding high
quality pixels to use for regular analysis. Comparisons of MODIS products in the
analysis of tropical forest coverage in Central America finds them to duplicate
results of higher resolution products with reasonable levels of accuracy (Hayes
2008). Similar comparisons, specifically for VCF, in Brazil, Indonesia and Africa
arrive at similar conclusions (Hansen et.al. [7-9]).
MODIS products have been used to construct the relatively simple Disturbance
Index (DI) based on MODIS EVI (Enhanced Vegetation Index) and LST (Land surface
temperature) as an efficient method for detecting remote disturbances without field
level monitoring (Mildrexler [15]). Coops [1] successfully applied DI to detect fires
in remote Canadian forests, and a similar approach is used in the The Global Forest
Disturbance Alert System[22].
VCF expands on the capability of DI by not only indicating locations of disturbances,
but also the magnitude of variations from year to year. Information on magnitude of
annual variation in forest cover offers key insights into the health of forests, and the
types of disturbances that might be affecting them.
Population pressure is an important motor in the changes of forest cover, although
in the past analyses of land cover change focused on replacement of forest by
farmland or pastures. Central American farmers interact with forests in more
complex ways than simply cutting them down, and these interactions can be seen to
a certain extent through VCF monitoring.
Deforestation in Central America and the Dominican Republic broadly follows the
trend in population increase. A population increase of over 30% between 1990 and
2005 was accompanied by a decrease and a degradation in forest cover, especially
in the largest countries of the region, as forests are slowly cut down or occupied by
land-seeking farmers.
Table 1: Population by country and by year: 1990 - 2005
Country 1990 1995 2000 2005 Change
Belice 186 214 242 270 145.16%
Costa Rica 3076 3475 3925 4322 140.51%
El Salvador 5110 5669 6276 6875 134.54%
Guatemala 8908 10004 11225 12700 142.57%
Honduras 4879 5654 6485 7347 150.58%
Nicaragua 3960 4477 4957 5483 138.46%
Panamá 2411 2670 2948 3228 133.89%
República Dominicana 7066 7705 8396 9100 128.79%
Source: ECLAC, 2009 [3]
Table 2: Forest Cover and Forest Cover Change 1990 - 2005
Area in forest: Thousands of Has.
Deforestation
Country/Year 1990 2000 2005 2010
Belice 1586 1489 1441 1393 -13.85%
Costa Rica 2564 2376 2491 2605 1.57% El Salvador 377 332 309 287 -31.36%
Guatemala 4748 4208 3938 3657 -29.83%
Honduras 8136 6392 5792 5192 -56.70%
Nicaragua 4514 3814 3464 3114 -44.96%
Panamá 3792 3369 3310 3251 -16.64%
República Dominicana 1972 1972 1972 1972 0.00%
Region 27689 23952 22717 21471 -28.96%
Source: FAO, 2010 [4]
The weakness of the region in the development of urban employment opportunities
in manufacturing or services historically has contributed to deforestation in Central
America and the Dominican Republic. The countries of the region for most of the
last century were internally perceived as frontier societies, with uninhabited forest
lands available for those with the initiative to occupy them (Jones [14]), a condition
that continues today on an individual level even though most governments have
prohibited or discouraged deforestation for land settlement . Heckadon [10]
documents the example of Panama in a period when official policies began to
discourage land clearance at the same time that individual farmers continued to
view forest clearance as a legitimate path to financial security. Although a part of
the land clearance may be carried out directly or indirectly by large land holders,
land clearance represents an economically attractive option for poor farmers, both
in the exploitation of forest resources, and also as a means to access farmland for
self-capitalization (Sewastynowycz [21]).
An important element of tropical deforestation by small farmers is its partial and
heterogeneous nature. A variety of climatic and topographical conditions make the
complete clearance of forests uncommon, creating what have come to be recognized
as ‘agroforestry systems’ where trees coexist with a variety of productive activities.
The forest cover associated with these systems varies between 10% in pastures and
cropland to more than 80% in coffee and cacao, and offer an attractive livelihood to
poor farmers even when lands remain partially forested. From a carbon
standpoint, these systems duplicate, at least partially, the characteristics of forest
systems (Rapidel et.al. [18]).
VCF provides a tool for the detection and quantification of forest degradation for
Central America and the Dominican Republic. The occupation of forests by the
region’s growing population does not result in immediate deforestation, but rather a
mosaic of cleared areas interspersed with dense forest stands and agroforestry
systems of different kinds, which maintain many aspects of forest cover but make a
major contribution to changes in the carbon stock of the region through the
elimination of some forest species and the introduction of more homogeneous
ecosystems based on crops such as coffee or cacao. As a result, forests are rarely
completely eliminated from Central American landscapes, but are reduced to small
patches or low densities to accommodate other activities.
The availability of a variety of satellite based imagery products, and the existence of
National Forest Inventories, permits the development of multi-scale monitoring
systems which use different data sources for complementary functions in the
monitoring process. This is implementation is described for the national forest
monitoring system of Canada in Wulder et.al [24]. Forest inventory plots provide
the most accurate assessments of carbon concentrations, but these are logistically
limited to extremely small areas and/or long update cycles. Detailed imagery
products such as aerial photography, LIDAR and LANDSAT, represent a bridge
between inventory plots and regional evaluations, through their ability to detect and
generalize the extent of forest classes over significantly larger areas the inventory
assessments. Detailed imagery products suffer a similar logistical limitation to
inventory plots, in that the relatively high costs of collecting and analyzing these
data lead to relatively long update cycles. The large pixel size of MODIS permits the
evaluation of extremely large areas (Mildrexler [15]), but at the same time limits the
accuracy of ground observations both spatially and spectrally, as many pixels
represent mixed ground coverage types. MODIS products are recognized as a key
element in the detection of short term events that affect inventory but which cannot
be captured through the tools of field level inventories or detailed imagery.
MODIS quick-update products improve temporal response at the cost of increased uncertainty in locations of phenomena, or their exact dimensions, requiring the use of complementary higher resolution imagery to improve precision. A MODIS pixel covering approximately 5ha. with its VCF dropping from 90% to 80% might indicate either a degradation of 10% over the entire 5has, or the complete deforestation of 0.5ha, and no change in the remaining 4.5ha. The use of aerial photography or other higher resolution imagery will be required to distinguish what observed changes mean at the most detailed level, with the benefit that the number of images to be reviewed is greatly reduced by having an exact location to evaluate.
2. BACKGROUND
The “Sourcebook of methods and procedures for monitoring and reporting
anthropogenic greenhouse gas emissions and removals caused by deforestation,
gains and losses of carbon stocks in forests remaining forests, and forestation”
(GOFC-GOLD [5]) clearly identifies on its first page the monitoring needs, and
monitoring challenges, for the implementation of REDD+ goals:
“At current status of negotiation five forest-related activities have been listed to be
implemented as mitigation actions by developing countries, namely: reducing
emissions from deforestation (which implies a land-use change) and reducing
emissions from forest degradation, conservation of forest carbon stocks, sustainable
management of forest land, Enhancement of forest carbon stocks (all relating to
carbon stock changes and GHG emissions within managed forest land use). . . The
book emphasizes the role of satellite remote sensing as an important tool for
monitoring changes in forest cover ”
The Sourcebook recognizes several different methods for evaluating forest change.
One is the comparison of classified images (GOFC-GOLD [5]: p2-15); classified
images are developed on the basis of careful field work, and cover change is
evaluated through a comparison of classifications from different time periods. The
output identifies how many and where pixels have changed from one class to
another, especially from forest to a non-forest class. This strategy is used to assess
global carbon stocks based on classifications of land cover, to capture categorical
differences between major types of land cover (Saatchi et.al. [20], Pan [17]).
Through a combination of land class information and forest inventory data from a
variety of forests, an initial estimate of carbon existence can be derived. This
analysis demonstrates the power of remote sensing to create global scale
summaries of information, which can incorporate the most recent information on
carbon concentrations from identified forest classes, and generate a dramatic
worldwide view of carbon stocks.
A major theoretical obstacle confronts the production of the forest class images
used in the above analysis; this same obstacle is relevant for the entire activity of
REDD forest change evaluation. Forest classifications inevitably are faced with
border errors, where a gradual decrease in forest density creates doubt as to the
exact location of the forest/non-forest division; this is a normal characteristic of
natural systems whose vegetation changes are gradual, and are seldom as clear cut
as would appear on a land classification map. The same problem confronts the
analysis of forests themselves, because forest densities are not homogeneous even
in their interiors. Variations in soils, water availability, exposure, etc, lead to
differences in density within a single type of forest, which do not change the overall
forest type, but change the density of carbon within it.
The application of VCF values to forest classification maps permits a more realistic
view of carbon density than a simple classification. In an assessment of REDD
monitoring capabilities for Non-Annex 1 countries, Romijn et.al. [19] identify MODIS
management and VCF as one of the evaluation criteria to estimate country
capabilities. REDD+ monitoring requires a more nuanced and dynamic view of
forest cover which can be provided by VCF and MODIS. It is necessary to recognize
variations within existing forests as a baseline for documenting subtle differences in
forest quality resulting from both degradation processes, and mitigation efforts in
each individual country.
The high temporal density of the MODIS sensor provides a basis for the solution to
the problems posed by REDD+ monitoring (GOFC-GOLD [5]: 2-21). Daily data can
be composited into cloud ‘free’ images of periods appropriate for the climatic
conditions of the study area (Huttich et.al. [13]). The advantage of the compositing
strategy is that imagery is available throughout the annual cycle, providing a
balance to potential errors caused by phenological cycling when monitoring is
attempted with a single date image.
The method for MODIS data management cited in the Sourcebook is the VCF
methodology developed by academic researchers in coordination with the MODIS
team (DeFries et.al [2], Hansen et.al. [6]). This methodology is based on a series of 8
to 12 composite images throughout the year, and a set of metrics which highlight
different phases of the yearly phenological cycle, normalized for annual variations
in climate.
VCF coverage output has been adopted as a MODIS product, issued with the
designation MOD44B in conjunction with the University of Maryland. These data
have been updated completely first for 2001 through 2005, and more recently, for
2005 through 2010 at a pixel size of 250m. These represent very high quality
products produced through the application of the combined resources of the
Department of Geography, Earth System Science Interdisciplinary Center, and the
Institute for Advanced Computer Studies, of the University of Maryland.
The implementation of the GIZ-VCF Monitoring and Early Warning address the
needs of REDD forest cover monitoring. The goal of monitoring cannot be limited
simply to the differentiation of forest classes, but must be able to provide
information on gradual spatial and temporal changes within classes, and the implied
GHG emissions changes. This goal is especially pertinent with regard to the above
referenced actions of “reducing emissions from forest degradation” and the
recapture of emissions through the “enhancement of carbon stocks”, outcomes to be
expected from the implementation of community based REDD projects. These goals,
highlighted and specifically identified in the first pages of the Sourcebook, require
an approach to monitoring which reports changes in forest density across time and
space.
3. METHODS AND DATA
MODIS - Characteristics and specifications
MODIS satellites were launched with the intention of providing global forest cover
information, to continue, and improve, on the information collected through the
earlier AVHRR program. The use of MODIS for national level forest monitoring
strikes a discordant note with national institutions accustomed to the use of
LANDSAT for monitoring purposes, in part because of the differences in pixel
resolution, and partly in response to the inability to interpret new information
generated by the MODIS data set. Nevertheless, through the use of composited
images based on daily data, MODIS provides significant improvement in satellite
data coverage, and introduces new opportunities for forest monitoring through its
increased temporal density.
The MODIS satellite constellation continued the global surface monitoring effort
initiated with AVHRR data to determine surface temperature conditions and
vegetation. AVHRR satellites monitored four, five and six bands of data in its
successive versions (NOAA [16]) which limited capabilities with regard to surface
cover monitoring. Beginning in 1981 AVHRR permitted the first continuous, global
view of vegetation condition with frequent updates, in a globally consistent format,
specifically through the use of NDVI at a 1000m resolution. The MODIS program
greatly expanded the available data by generating 36 bands including sources of
data for terrestrial, oceanic and atmospheric monitoring at a nominal resolution of a
250m, and incorporated as one of the primary products the generation of NDVI to
continue the sequence initiated by AVHRR (Zhan [25]).
An advantage of the lower MODIS resolution is the ability to observe all of Central
America and the Dominican Republic with only 4 images, easily viewable and
available through GLOVIS (USGS Global Visualization Viewer)(USGS [23]).
Preparation and processing time for the entire region is greatly reduced over a
similar undertaking with higher resolution images; processing is further facilitated
by the availability of preprocessed MODIS Data Products which include composite
images for different time periods. Of special interest is the 16 day composite
MOD13Q1 which provides vegetation indices and 4 spectral bands at 250m
resolution (Huete et.al. [12]). The reduced data processing requirements permit the
generation of new data sets very quickly, bringing results much closer to ‘real time’
(less than 4 weeks from the end of the data period) than would be possible with a
higher resolution system.
Monthly composite files have MODIS quality indicators described by Huete et.al.
[12], which provide a basis for pixel quality evaluation. Sixteen day composite files
are compared with special emphasis on MODIS quality bits, replacing pixels when
there is a clear superiority of one over the other. Composite images of longer
duration (32 vs 16 days) have a higher likelihood of good quality pixels being found
for cloudy areas.
Focus on phenological cycle
The VCF methodology seeks to identify the density of forest cover. The
methodology calls for the development of a set of ‘metrics’ tied to maxima and
minima of band values throughout the course of a year.
The quantity of tree leaves in a pixel is a major component in the values reflected to
the satellite sensor. The quantity of leaves is not constant, since it varies according
to the stage of the phenological cycle; trees frequently shed leaves during summer
droughts, and replace when rainfall increases. In order to normalize the detection
of forest cover for annual variations in onset dates of rainy and dry periods, metrics
must be constructed which are tied to the phenological cycle, rather than to the
calendric cycle; rains may begin in March of one year, and May of another.
VCF metrics are constructed to detect periods of maxima and minima which reflect
stages in the phenological cycle (Hansen [6]). One set of statistics is developed at
the period of maximum NDVI, and another at the period of maximum temperature,
corresponding to periods of maximum leaf coverage, and minimum leaf coverage,
respectively. This approach minimizes the possibility of false reports of change
caused by the comparison of different stages in the phenological cycle in succeeding
years.
In addition to the maxima and minima for phenologically defined stages, VCF
metrics also capture the length and intensity of the dry season and the rainy season.
Multimonth averages are compared for phenological maxima and minima. A total of
86 metrics are collected, which are complemented by elevation and rainfall data.
The VCF value produced is normalized for climatic variations throughout the year,
and by incorporating information of lengths of dry and wet seasons also tends to
normalize interannual differences.
Sequence Statistics
The analysis of forest change dynamics is carried out on the sequence of VCF layers
for the years 2001 through 2010. The analysis of change is carried out by a pixel by
pixel mathematical analysis, to determine characteristics of each pixel sequence.
Statistics collected on the pixel by pixel analysis are mean, standard deviation, and
slope, based on, in this case, the 10 yearly pixels from each corresponding layer.
Pixels are grouped by their behavior with respect to these statistics. Mean values
r
e
f
l
e
c
Figure 3 Distribution of Standard Deviations
Figure 1 Distribution of Means
Figure 2 Distribution of Slopes
t a general level of forest cover, without providing information as to the stability of
the observed formations. A slope is calculated for each pixel set, to reflect the
general tendency of the forest cover, along with a standard deviation based on
residual values around the line of the calculated slope.
The histogram of means shows that he majority of land cover falls below 50% forest
density. In the 0 – 20% range abrupt changes may reflect distinct agroforestry
systems while denser forest systems show gradual changes in the histogram
reflecting the variety of forest types and distributions, with distinct peaks at 30%
and 90% forest cover.
Slopes of pixel data were calculated after eliminating null data (cases where no VCF
classification was achieved). Predictably, the distribution of slopes clusters around
0, which indicates stable land cover patterns, and a slight skew toward negative
slopes, indicating processes of degradation or deforestation.
Similar to the previous calculation, the standard deviation is calculated after
eliminating null values. The standard deviation identifies how much variability in
VCF values are seen over the decade. Sixty-nine percent of the non-zero standard
deviations (zero values only occur where there are no data) are greater than the
value of 10.
Data Anomalies and Forest Disturbance
MODIS VCF data generates many anomalies. Some are common to all kinds of
remotely sensed imagery, and others arise from the attempt to utilize the high
temporal density of the MODIS data set. A close analysis of these anomalies reveals
that many are ‘anomalies’ only from the perspective of LANDSAT type image
processing, and in fact present a new enriched perspective on forest dynamics in the
tropics.
Anomalies common to all remotely sensed data are typically atmospheric effects.
Figure 10 Detail of Fire Anomaly
More notable are clouds, along with their deep shadows which produce values very
different from non-shadowed pixels of the same land cover. In this same category
fall haze, jet contrails, smoke, or any other phenomenon which decreases the
transparency of the atmosphere.
Anomalies specific to MODIS-VCF analysis highlight a new set of transitory
phenomena. The most striking anomaly is the periodic ‘disappearance’ of forest
cover, or its degradation, quickly followed by ‘reforestation’ in the space of one or
two years. Two mechanisms have been identified which create this anomaly.
The first, and most common cause of rapid forest disappearance and re-appearance
is fire. In Figure 4, three data sets are compared; the 2006 MODIS coverage of fire
occurrence for the entire year, the 2010 land use classification for Central America
carried out by CATHALAC (Centro de Agua para los Tropicos Humedos de
Latinoamerica y el Caribe), and a sequence of 10 annual VCF images from 2001 to
2010, all focused on central Honduras. The three images are geographically linked,
showing that the point of the anomaly, identified by the VCF profile in the bottom
center of the image (where each data point corresponds to a VCF value for a single
year), coincides with a pine forest which experienced burning over an area of some
500ha during 2006. In Honduras, forests are used as a pasture area for cattle, and
burning is a common practice to eliminate ticks, which create health problems for
the cattle. Burning also serves to ‘renew’ grasses, burning off old tough stems which
are then replaced with new growth. The intention of the burning is not land
clearance, but the destruction of dry grasses and pests which live in the vegetation.
This low intensity fire eliminates a great deal of the tree foliage, and also covers the
landscape with soot making the forest 'disappear'. In the following rainy season,
trees set new leaves, and soot is washed from the landscape, so the forest becomes
visible once again. Sixty-seven percent of locations of the 8129 fires in forest areas
for 2006 correspond to areas showing this anomaly during the period 2001-2010;
the remaining 33% of fires occurred in areas reported as stable forest in 2006.
Of the remaining anomalies, 21% are found in flood plains and river beds. In flood
periods, rising waters lead to decreased visibility of forest vegetation and reduce
the apparent forest cover for a period of days or weeks. VCF values during the
period may drop dramatically, only to rise again in the following year.
Other causes of brief ‘deforestation’ anomalies are landslides, river bed scouring,
high winds (e.g. hurricanes), droughts and possibly insect attacks. While there can
be no doubt that hidden within the deforestation anomalies are cases of erroneous
data, it is clear that ‘anomalous’ patterns of change reflect real phenomena which
have been unrecognized due to the use of single data imagery for land cover
evaluation. An awareness and understanding of the forces which cause forest
anomalies will contribute to a deeper understanding of forest processes, and help
provide realistic approaches to degradation mitigation.
Stable Intervened and Non-Intervened Forests
The combination of the annual sequence statistics, mean, standard deviation and
slope, permit a classification of patterns of forest dynamics. Two patterns which
account for over 75% of all nationally identified forest aeas, are of special interest
(see Table 3). The patterns detected are:
- Stable, non-intervened forest; average forest cover above 60%, slope less than 3
(300 in the accompanying table) and a standard deviation less than 10.
- Stable, intervened forest; slope less than 3 and a standard deviation greater
than 10. Threatened forest.
Table 3: Forest dynamics as a percentage of total forest area, by country, 2011
Belize
Costa
Rica
República
Dominicana
El
Salvador Guatemala Honduras Nicaragua Panamá Total
1. Stable Intervened Forest 34.88% 53.23% 54.18% 64.31% 43.11% 51.07% 48.03% 52.73% 29.24%
2. Stable Non-intervened Forest 51.77% 26.56% 27.19% 20.97% 31.84% 33.36% 24.88% 20.69% 48.85%
3. Forest with constant reduction 8.02% 7.38% 6.28% 7.54% 15.51% 5.91% 13.70% 7.06% 9.68%
4. Forest with constant increase 1.37% 2.20% 5.50% 3.63% 1.64% 3.54% 2.31% 2.25% 2.56%
5. Highly intervened,
tendency toward
change 0.90% 5.09% 2.52% 0.89% 2.18% 2.18% 3.32% 8.98% 3.79%
6.Other 3.06% 5.55% 4.33% 2.66% 5.72% 3.94% 7.76% 8.29% 5.88%
Total
100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%
From the perspective of community forest management, the category of stable,
intervened forest is of special interest, as these are forests which most likely are the
result of a long term presence of human management practices. (The exceptions to
this generalization regarding highly intervened forests without change tendencies
are swamps and riverbeds, where regular, nearly annual, flooding occurs to cause
periodic fluctuations in the apparent forest cover.) This observation explains the
distribution of forests with high and low standard deviations (see Figure 5): the
largest areas of low standard deviations are seen in the most remote forest areas,
the coastal area of the Honduran and Nicaraguan Moskitia, south-central Belize, and
the lowlands of Petén. Forests found near areas of intense human activity are more
likely to have high standard deviations, although there are many patches of non-
intervened forest even in the more populous areas of the region.
Figure 11 Distribution of Stable Intervened and Non-Intervened Forests
It is important to draw the contrast between high and low standard deviation
forests. Low
standard deviation
forests suffer changes
in density, but at a
low level which
would be consistent
with changes in
rainfall totals,
occasional high winds
or hurricanes.
Apparent forest cover
does not change
dramatically. High
standard deviation
forests are defined
with standard
Figure 12 Detail of Distribution of Intervened Forests
deviations greater than 25, representing changes of more than 25% on average from
the mean tendency of forest cover; logically, some years will show more than 25%
change to reach an average of 25%. High standard deviation forests experience
intense human intervention, through the near complete loss of cover on a regular
basis; although these forests are intensely managed, and the objective is clearly not
one of deforestation.
A higher resolution view of the Guatemalan highlands (Figure 6), where there is a
tradition of community forest management, shows a complex pattern of intervened
and non-intervened forests. The linear features running northwest to southeast
emphasize the intensive geological folding which generated the irregular
topography of the region.
Early Warning
Early warning is the implicit goal of a monitoring system. With a delay of 4-6 weeks
in the presentation of monitoring results, the VCF based monitoring system is not
the ideal early warning platform; nevertheless, it has a much quicker response time
than forest inventory or forest monitoring systems in the past, especially in
identifying where most recent changes are ongoing across a large landscape.
The VCF based monitoring system is built on the analysis of year-long image
sequences, to capture phenological information, and distinguish when changes vary
from normal fluctuation. The proper operation of an early warning system is
plagued by ‘false positives’ of degradation, caused as a result of ‘normal’ decreases
in forest cover. Phenological variation makes it impossible to absolutely identify
degradation events in the short term, since forests may suffer temporary decreases
in leaf cover one or more times a year, which are indistinguishable in their early
stages from long term degradation events.
Even more challenging than false positives caused by the phenological cycle are fire
events such as those described above. A fire-caused decrease in tree cover might
represent the beginning of a deforestation or degradation process, or it may be one
more cycle in a homeostatic management cycle which temporarily decreases
vegetation as a means to improve the quantity and quality of vegetation over a
longer period. The final conclusion regarding the impact of a vegetation decrease
event may take years to define, especially when it occurs in the context of this kind
of forest management.
To reduce the probabilities of incorrect identification of degradation events, early
warning analysis is only carried out in stable forests, with low standard deviations
in VCF values. Sudden decreases in forest cover outside the normal range of
variability most likely represents a degradation event. Since early warning is
calculated on the basis of mean VCF values, forest with constantly changing VCF's,
even with low standard deviations, cannot be included because the overall trend
tends to obscure variations from the mean.
4. RESULTS AND DISCUSSION
VCF analysis provides increased depth to the evaluation of forest trends in Central
America and Dominican Republic. Within the optimistic statistic of a decrease in
deforested areas in the decade 2010-2011 (Table 4), a trend toward degradation of
primary forests can be distinguished, and an increase in forests suffering regular
human intervention.
Table 4 shows that the only category of forest to increase in the 2010-2011 was
stable intervened forest, while all other categories, including deforested land,
decreased.
Table 4: Type of forest dynamic %2010 %2011
Stable mean >= 60 and sdev <= 10 and abs(slope) <= 300 29.32% 27.03%
Stable intervened sdev > 10 and abs(b2)<= 300 44.96% 48.73%
Constant tendency to deforestation sdev <= 30 and slope<= -300
1.17% 0.73%
Stable agroforestry : mean >= 60 and mean >= 30 and sdev <= 10 and abs(slope)<= 300
1.37% 1.26%
Legally deforested <30% : mean < 30 23.18% 22.25%
TOTAL 100% 100%
Table 5: Early Warning by Trimesters 2010 - 2011
EARLY WARNING
Year 2010 Year 2011
I II III IV I II III IV
Total Warnings
24635 25095 29472 36963 17058 29733 36962 23182
New Warnings
24635 16236 17112 32440 4315 22202 20244 13419
Accumulated total
24635 40871 57983 90423 94738 116940 137184 150603
In the category of stable forests, trimestral reports of early warnings accumulate to
a total of more than 8000km (Table 5). The method for constructing the trimestral
warnings was to compare each trimester with the previous calendar year; each
trimester of 2010 was compared to trends through 2009. This method allowed
early persistent warnings to be repeated, if VCF values did not recover during the
trimester. The figures for each trimester are broken down by total warnings, total
warnings for the trimester which do not duplicate earlier warnings, and the
accumulated non-duplicated warnings over 8 trimesters, in pixels (figure 4).
Figure 13 Loss of Primary Forest
warnings
Area accumulated warnings (km2)
1314.55
2180.92
3094.03
4825.06
5055.31
6240.04
7320.28
8036.33
Area new warnings (km2)
1314.55 866.37 913.11 1731.03 230.25 1184.72 1080.24 716.05
-
Figure 14 Cumulative Early Warning 2010 - 2011
The comparison of stable forest and early warnings gains credibility with the
accumulation of data. The same data appears in Figure 8, with stable forest for
March 2010 presented in green; the black area of the image is largely covered by
intervened forest, but indicative of human activity. This image shows Belize and its
coastline on the right, including the area of Belize City and Belmopan, and extends
into Guatemala on the left. Accumulated trimestral early warnings from June 2010
to December 2011 are presented in red, and clearly define a swath across Belize
being cleared as part of a rice expansion program supported by the government. A
large number of scattered early warnings appear throughout the image, indicating a
process of more individualized, and possibly clandestine forest degradations; since
early warnings are defined as a decline of 25% forest cover, these same areas may
simply be degraded, but remain under forest.
5. CONCLUSION
A VCF based regional monitoring system for REDD reporting contains tools to address changes (or non-changes) in forest cover required for complete Green House Gas reporting: "reducing emissions from deforestation (which implies a land-use change) and reducing emissions from forest degradation, conservation of forest carbon stocks, sustainable management of forest land, Enhancement of forest carbon stocks". Periodic updates of the percent forest cover image for the region are a basis for the observation of degradation or enhancement trends in forest cover. A breakdown of statistical patterns based on pixel by pixel multiyear trends identifies two broad forest categories; dense forest with little interannual variability in density values, and forests with high interannual variability.
The low variability forest represents non-intervened forest experiencing normal variations in density due to natural conditions. The stability of the VCF values permit the application of an Early Warning analysis to this type of forest, identifying moments when forest degradation begins, but requiring ground checking or more detailed photographic analysis to determine if this is an anthropogenic effect or a natural process. The latter category of high variability represents intervened, degraded forest, which can be evaluated in terms of average forest density to identify trends toward improved or degenerating forest cover. As noted by Herold [11], carbon loss through degradation may represent up to 10 times the carbon loss from simple deforestation, due to the large areas affected. In Central America and the Dominican Republic, stable natural forests represent only 20% of the regional forest area, while intervened forests represent nearly 50%. The ability to monitor levels of forest degradation, or the evolution of forest density in agroforestry systems, provides a necessary element in the compensation of broad community based forest management efforts. Changes in density can be used as a complement to ground point controls, to demonstrate the effectiveness of community management activities. In view of the potentially large area affected by this type of management, VCF monitoring of carbon concentrations in highly intervened forests stands to make very large contributions to the documentation of carbon sequestration efforts. ACKNOWLEDGMENT The development of the Central American MODIS Monitoring System was financed by Programa Reducción de Emisiones de la Deforestación y Degradación de Bosques en Centroamérica y República Dominicana (REDD - CCAD - GIZ: http://www.reddccadgiz.org/) from 2010 through 2012. REFERENCES 1. Coops, Nicholas C., Michael A. Wulder, Donald Iwanicka. 2009. "Large area
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2. DeFries, R. S., Hansen, M. C., Townshend, J. R. G., and Janetos, A. C., 2000, A
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Anexo 2: Descripción de la edición especial para artículo:
IntJForResRMDC22march12AM.pdf
International Journal of Forestry Research
Special Issue on REDD+ Mechanism in Developing Countries
Call for Papers Deforestation and forest degradation, through agricultural and animal husbandry activities,
infrastructure development, destructive logging, fires etc., account for nearly 6-17% of global
greenhouse gas emissions; in some years these emissions represent more than the entire
global transportation sector and are second only to the energy sector. It is now well
established that it will be essentially impossible to stabilize the climate within two degrees
Celsius without reducing emissions from deforestation and forest degradation. This reduction
must be in addition to increased Annex I countries’ mitigation pledges before 2020, which
are absolutely necessary in order to avoid the worst impacts of anthropogenic interference
with the climate system. Since the REDD (Reducing Emissions from Deforestation and
Forest Degradation) action plan was adopted at the 13th Conference of the Parties of the
United Nations Framework Convention on Climate Change (UNFCCC) (COP13) on 8
December 2007, in Bali, Indonesia, its scope has expanded to include not only forest
conservation activities, but also enhanced and sustainable forest management activities (the
so-called REDD+ agenda). REDD+ is a mechanism being negotiated through the UNFCCC
for mitigating climate change, reducing rural poverty and achieving conservation and
sustainable development of tropical forests. REDD+ promotes co-benefits such as non-
carbon benefits, ecosystem services, employment, livelihood, climate change adaptation, and
cultural services, depending on the social, ecological and institutional context in which it is
implemented.
We invite authors to submit original research and review articles that seek to define the
interaction between REDD+ and activities leading to community development. We are
interested in articles that explore all aspects of REDD+ as a mechanism for reducing rural
poverty and achieving conservation and sustainable management and development of tropical
forests. Potential topics include, but are not limited to: REDD+ and community forestry
REDD+, forest conservation and enhanced forest management
REDD+, advanced technologies and tools for measurement of carbon sequestration
REDD+, valorisation of timber and non-timber forest products for community sustainability and bioeconomy
REDD+, exchange of experience and strategies between states
REDD+, institutional and governance issues
REDD+ and financing mechanisms
REDD+ and capacity building
Before submission, authors should carefully read over the journal’s Author Guidelines, which
are located at http://www.hindawi.com/journals/ijfr/guidelines.html. Prospective authors
should submit an electronic copy of their complete manuscript through the journal
Manuscript Tracking System at http://mts.hindawi.com/ according to the following timetable:
Manuscript Due
Manuscript Due August 3, 2012 Manuscript Due
First Round of Reviews October 26, 2012 First Round of Reviews Publication Date December 21, 2012 Publication Date
Lead Guest Editor
Damase Khasa, Centre for Forest Research, Canada Research Chair in Forest and
Environmental Genomics and Institute of Systems and Integrative Biology, Canada;
Guest Editors Alison Munson, Centre for Forest Research, Université Laval, Québec, Canada;
Mariteuw Chimère Diaw, African Model Forest Network, African Model Forests Network
Secretariat, Cameroon; [email protected]
Nancy Gelinas, Department of Wood and Forest Sciences, Université Laval, Québec,
Canada; [email protected]
Dr. Nadine Laporte, The Woods Hole Research Center, USA ; [email protected]
Dr. Glenn Bush, The Woods Hole Research Center, 149 Woods Hole Road Falmouth, MA
02540–USA ; [email protected]
Anexo 3: Recibido de articulo de editores de revisa (correo electronico)
International Journal of Forestry Research [email protected] via amazonses.com
Aug 2
to me, abner.jimenez, laszlo.pancel
Dear Dr. Jones, The Research Article titled "MODIS Based REDD+ Regional Monitoring System for Forest Cover Reporting of Central America and the Dominican Republic," by Abner Josue Jimenez Galo, Jeffrey Jones and Laszlo Pancel has been received and assigned the number 805645. The special issue for which the paper is being processed is "REDD+ Mechanism in Developing Countries" An editor will be assigned to handle the review process of your manuscript, and he/she will inform you as soon as a decision is reached. All authors will receive a copy of all the correspondences regarding this manuscript. However, only the submitting author will be able to upload any revisions to the journal's Manuscript Tracking System. Thank you for submitting your work to International Journal of Forestry Research. Best regards, Fathya Sayed Editorial Office Hindawi Publishing Corporation http://www.hindawi.com
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