Tribal Land Vegetation and Watershed Modeling
San Carlos Apache Reservation covers 1.8 million acres in east-central Arizona, and has diverse ecosystems and vegetation types that support a natural resource-based economy.
Fire Burn Severity
We used remotely sensed data from the Landsat 8 and WorldView-2 satellites to estimate vegetation burn severity of the Creek Fire on the San Carlos Apache Reservation, where wildfire occurrences affect the Tribe’s crucial livestock and logging industries. Accurate pre- and post-fire canopy maps at high (0.5-meter) resolution were created from WorldView-2 data to generate canopy loss maps, and multiple indices from pre- and post-fire Landsat 8 images were used to evaluate vegetation burn severity. Normalized difference vegetation index based vegetation burn severity map had the highest correlation coefficients with canopy loss map from WorldView-2. Two distinct approaches – canopy loss mapping from WorldView-2 and spectral index differencing from Landsat 8, agreed well with the field-based burn severity estimates and are both effective for vegetation burn severity mapping. Canopy loss maps created with WorldView-2 imagery add to a short list of accurate vegetation burn severity mapping techniques that can help guide effective management of forest resources on the San Carlos Apache Reservation, and the broader fire-prone regions of the Southwest.
Aboveground Biomass Estimates with LiDAR
Light Detection and Ranging (lidar) is an increasingly popular Earth observing data type. Lidar is an active remote sensing system that can measure the three-dimensional (3-D) structural characteristics of Earth features, which are not directly captured by passive optical land imaging systems. The U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) was recently established to provide airborne lidar data coverage on a national scale. As part of a broader research effort of the USGS to develop an effective remote sensing-based methodology for the creation of an operational biomass Essential Climate Variable (Biomass ECV) data product, we evaluated the performance of airborne lidar data at various pulse densities against Landsat 8 satellite imagery in estimating above ground biomass for forests and woodlands in a study area in east-central Arizona, U.S. Airborne lidar and Landsat 8 derived metrics were used in linear regression analysis against field based biomass estimates. High point density airborne lidar data were randomly sampled to produce five lidar datasets with reduced densities ranging from 0.5 to 8 point(s)/m2, corresponding to the point density range of 3DEP to provide national lidar coverage over time. Lidar-derived aboveground biomass estimate errors showed an overall decreasing trend as lidar point density increased from 0.5 to 8 points/m2. Landsat 8-based aboveground biomass estimates produced errors larger than the lowest lidar point density of 0.5 point/m2, and therefore Landsat 8 observations alone were ineffective relative to airborne lidar for generating a Biomass ECV product, at least for the forest and woodland vegetation types of the Southwestern U.S. While a national Biomass ECV product with optimal accuracy could potentially be achieved with 3DEP data at 8 points/m2, our results indicate that even lower density lidar data could be sufficient to provide a national Biomass ECV product with accuracies significantly higher than that from Landsat observations alone.
Vegetation Response to Water Availability
A variety of vegetation indices derived from remotely sensed data have been used to assess vegetation conditions, enabling the identification of drought occurrences as well as the evaluation of drought impacts. Moderate Resolution Imaging Spectroradiometer (MODIS) Terra 8-day composite data were used to compute the Modified Soil Adjusted Vegetation Index II (MSAVI2) of four dominant vegetation types over a 13-year period (2002 – 2014) on the San Carlos Apache Reservation in Arizona, US. MSAVI2 anomalies were used to identify adverse impacts of drought on vegetation, characterized as mean MSAVI2 below the 13-year average. In terms of interannual variability, we found similar responses between grassland and shrubland, and between woodland and forest vegetation types. We compared MSAVI2 for specific vegetation types with precipitation data at the same time step, and found a lag time of roughly two months for the peak MSAVI2 values following precipitation in a given year. All vegetation types responded to summer monsoon rainfall, while shrubland and annual herbaceous vegetation also displayed a brief spring growing season following winter precipitation. MSAVI2 values of shrublands corresponded well with precipitation variability both for summer rainfall and winter snowfall, and can be potentially used as a drought indicator on the San Carlos Apache Reservation given its wide geographic distribution. We demonstrated that moderate temporal frequency satellite-based MSAVI2 can provide drought monitoring to inform land management decisions, especially on vegetated tribal land areas where in situ precipitation data are limited.
San Carlos Apache Reservation covers 1.8 million acres in east-central Arizona, and has diverse ecosystems and vegetation types that support a natural resource-based economy.
Fire Burn Severity
We used remotely sensed data from the Landsat 8 and WorldView-2 satellites to estimate vegetation burn severity of the Creek Fire on the San Carlos Apache Reservation, where wildfire occurrences affect the Tribe’s crucial livestock and logging industries. Accurate pre- and post-fire canopy maps at high (0.5-meter) resolution were created from WorldView-2 data to generate canopy loss maps, and multiple indices from pre- and post-fire Landsat 8 images were used to evaluate vegetation burn severity. Normalized difference vegetation index based vegetation burn severity map had the highest correlation coefficients with canopy loss map from WorldView-2. Two distinct approaches – canopy loss mapping from WorldView-2 and spectral index differencing from Landsat 8, agreed well with the field-based burn severity estimates and are both effective for vegetation burn severity mapping. Canopy loss maps created with WorldView-2 imagery add to a short list of accurate vegetation burn severity mapping techniques that can help guide effective management of forest resources on the San Carlos Apache Reservation, and the broader fire-prone regions of the Southwest.
Aboveground Biomass Estimates with LiDAR
Light Detection and Ranging (lidar) is an increasingly popular Earth observing data type. Lidar is an active remote sensing system that can measure the three-dimensional (3-D) structural characteristics of Earth features, which are not directly captured by passive optical land imaging systems. The U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) was recently established to provide airborne lidar data coverage on a national scale. As part of a broader research effort of the USGS to develop an effective remote sensing-based methodology for the creation of an operational biomass Essential Climate Variable (Biomass ECV) data product, we evaluated the performance of airborne lidar data at various pulse densities against Landsat 8 satellite imagery in estimating above ground biomass for forests and woodlands in a study area in east-central Arizona, U.S. Airborne lidar and Landsat 8 derived metrics were used in linear regression analysis against field based biomass estimates. High point density airborne lidar data were randomly sampled to produce five lidar datasets with reduced densities ranging from 0.5 to 8 point(s)/m2, corresponding to the point density range of 3DEP to provide national lidar coverage over time. Lidar-derived aboveground biomass estimate errors showed an overall decreasing trend as lidar point density increased from 0.5 to 8 points/m2. Landsat 8-based aboveground biomass estimates produced errors larger than the lowest lidar point density of 0.5 point/m2, and therefore Landsat 8 observations alone were ineffective relative to airborne lidar for generating a Biomass ECV product, at least for the forest and woodland vegetation types of the Southwestern U.S. While a national Biomass ECV product with optimal accuracy could potentially be achieved with 3DEP data at 8 points/m2, our results indicate that even lower density lidar data could be sufficient to provide a national Biomass ECV product with accuracies significantly higher than that from Landsat observations alone.
Vegetation Response to Water Availability
A variety of vegetation indices derived from remotely sensed data have been used to assess vegetation conditions, enabling the identification of drought occurrences as well as the evaluation of drought impacts. Moderate Resolution Imaging Spectroradiometer (MODIS) Terra 8-day composite data were used to compute the Modified Soil Adjusted Vegetation Index II (MSAVI2) of four dominant vegetation types over a 13-year period (2002 – 2014) on the San Carlos Apache Reservation in Arizona, US. MSAVI2 anomalies were used to identify adverse impacts of drought on vegetation, characterized as mean MSAVI2 below the 13-year average. In terms of interannual variability, we found similar responses between grassland and shrubland, and between woodland and forest vegetation types. We compared MSAVI2 for specific vegetation types with precipitation data at the same time step, and found a lag time of roughly two months for the peak MSAVI2 values following precipitation in a given year. All vegetation types responded to summer monsoon rainfall, while shrubland and annual herbaceous vegetation also displayed a brief spring growing season following winter precipitation. MSAVI2 values of shrublands corresponded well with precipitation variability both for summer rainfall and winter snowfall, and can be potentially used as a drought indicator on the San Carlos Apache Reservation given its wide geographic distribution. We demonstrated that moderate temporal frequency satellite-based MSAVI2 can provide drought monitoring to inform land management decisions, especially on vegetated tribal land areas where in situ precipitation data are limited.