Tribal Land Vegetation and Watershed Modeling Active
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.
Database of Trends in Vegetation Properties and Climate Adaptation Variables on the San Carlos Apache Reservation and Upper Gila River Watershed (1935-2021)
Below are publications associated with this project.
Riparian vegetation response amid variable climate conditions across the Upper Gila River watershed: Informing Tribal restoration priorities
Remote sensing analysis of vegetation at the San Carlos Apache Reservation, Arizona and surrounding area
Evaluating and monitoring forest fuel treatments using remote sensing applications in Arizona, U.S.A.
Vegetative response to water availability on the San Carlos Apache Reservation
MODIS derived vegetation index for drought detection on the San Carlos Apache Reservation
Estimating forest and woodland aboveground biomass using active and passive remote sensing
Evaluating lidar point densities for effective estimation of aboveground biomass
Vegetation burn severity mapping using Landsat-8 and WorldView-2
- Overview
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.
- Data
Database of Trends in Vegetation Properties and Climate Adaptation Variables on the San Carlos Apache Reservation and Upper Gila River Watershed (1935-2021)
We apply a research approach that can inform riparian restoration planning by developing products that show recent trends in vegetation conditions identifying areas potentially more at risk for degradation and the associated relationship between riparian vegetation dynamics and climate conditions. The vegetation is characterized using a series of remote sensing vegetation indices developing using - Publications
Below are publications associated with this project.
Riparian vegetation response amid variable climate conditions across the Upper Gila River watershed: Informing Tribal restoration priorities
Riparian systems across the Southwest United States are extremely valuable for the human and ecological communities that engage with them. However, they have experienced substantial changes and stresses over the past century, including non-native vegetation expansion, vegetation die-offs, and increased fire activity. Vegetation management approaches, such as ecological restoration, may address somAuthorsRoy Petrakis, Laura M. Norman, Barry R. MiddletonRemote sensing analysis of vegetation at the San Carlos Apache Reservation, Arizona and surrounding area
Mapping of vegetation types is of great importance to the San Carlos Apache Tribe and their management of forestry and fire fuels. Various remote sensing techniques were applied to classify multitemporal Landsat 8 satellite data, vegetation index, and digital elevation model data. A multitiered unsupervised classification generated over 900 classes that were then recoded to one of the 16 generalizAuthorsLaura M. Norman, Barry R. Middleton, Natalie R. WilsonEvaluating and monitoring forest fuel treatments using remote sensing applications in Arizona, U.S.A.
The practice of fire suppression across the western United States over the past century has led to dense forests, and when coupled with drought has contributed to an increase in large and destructive wildfires. Forest management efforts aimed at reducing flammable fuels through various fuel treatments can help to restore frequent fire regimes and increase forest resilience. Our research examines hAuthorsRoy Petrakis, Miguel L. Villarreal, Zhuoting Wu, Robert Hetzler, Barry R. Middleton, Laura M. NormanVegetative response to water availability on the San Carlos Apache Reservation
On the San Carlos Apache Reservation in east-central Arizona, U.S.A., vegetation types such as ponderosa pine forests, pinyon-juniper woodlands, and grasslands have significant ecological, cultural, and economic value for the Tribe. This value extends beyond the tribal lands and across the Western United States. Vegetation across the Southwestern United States is susceptible to drought conditionsAuthorsRoy Petrakis, Zhuoting Wu, Jason McVay, Barry R. Middleton, Dennis G. Dye, John M. VogelMODIS derived vegetation index for drought detection on the San Carlos Apache Reservation
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 tyAuthorsZhuoting Wu, Miguel G. Velasco, Jason McVay, Barry R. Middleton, John M. Vogel, Dennis G. DyeEstimating forest and woodland aboveground biomass using active and passive remote sensing
Aboveground biomass was estimated from active and passive remote sensing sources, including airborne lidar and Landsat-8 satellites, in an eastern Arizona (USA) study area comprised of forest and woodland ecosystems. Compared to field measurements, airborne lidar enabled direct estimation of individual tree height with a slope of 0.98 (R2 = 0.98). At the plot-level, lidar-derived height and intensAuthorsZhuoting Wu, Dennis G. Dye, John M. Vogel, Barry R. MiddletonEvaluating lidar point densities for effective estimation of aboveground biomass
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 dAuthorsZhuoting Wu, Dennis G. Dye, Jason M. Stoker, John M. Vogel, Miguel G. Velasco, Barry R. MiddletonVegetation burn severity mapping using Landsat-8 and WorldView-2
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 World- View-2 data to generate canopy loss maps, and muAuthorsZhuoting Wu, Barry R. Middleton, Robert Hetzler, John M. Vogel, Dennis G. Dye