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Abstract:We developed and evaluated a methodology for subpixel discrimination and large-area mapping of the perennial warm-season (C 4 ) grass component of vegetation cover in mixed-composition landscapes of the southwestern United States and northern Mexico. We describe the methodology within a general, conceptual framework that we identify as the differential vegetation phenology (DVP) paradigm. We introduce a DVP index, the Normalized Difference Phenometric Index (NDPI) that provides vegetation type-specific information at the subpixel scale by exploiting differential patterns of vegetation phenology detectable in time-series spectral vegetation index (VI) data from multispectral land imagers. We used modified soil-adjusted vegetation index (MSAVI 2 ) data from Landsat to develop the NDPI, and MSAVI 2 data from MODIS to compare its performance relative to one alternate DVP metric (difference of spring average MSAVI 2 and summer maximum MSAVI 2 ), and two simple, conventional VI metrics (summer average MSAVI 2 , summer maximum MSAVI 2 ). The NDPI in a scaled form (NDPI s ) performed best in predicting variation in perennial C 4 grass cover as estimated from landscape photographs at 92 sites (R 2 = 0.76, p < 0.001), indicating improvement over the alternate DVP metric (R 2 = 0.73, p < 0.001) and substantial improvement over the two conventional VI metrics (R 2 = 0.62 and 0.56, p < 0.001). The results suggest DVP-based methods, and the NDPI in particular, can be effective for subpixel discrimination and mapping of exposed perennial C 4 grass cover within mixed-composition landscapes of the Southwest, and potentially for monitoring of its response to drought, climate change, grazing and other factors, including land management. With appropriate adjustments, the method could potentially be used for subpixel discrimination and mapping of grass or other vegetation types in other regions where the vegetation components of the landscape exhibit contrasting seasonal patterns of phenology.

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 (MSAVI 2) of four dominant vegetation types over a 13-year period (2002-2014) on the San Carlos Apache Reservation in Arizona, US. MSAVI 2 anomalies were used to identify adverse impacts of drought on vegetation, characterized as mean MSAVI 2 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 MSAVI 2 for specific vegetation types with precipitation data at the same time step, and found a lag time of roughly two months for the peak MSAVI 2 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 snowmelt. MSAVI 2 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 MSAVI 2 can provide drought monitoring to inform land management decisions, especially on vegetated tribal land areas where in situ precipitation data are limited.

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. 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)/m 2 , 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/m 2. Landsat 8-based aboveground biomass estimates produced errors larger than the lowest lidar point density of 0.5 point/m 2 , 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/m 2 , 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.

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 generalized vegetation/land cover classes using the Southwest Regional Gap Analysis Project (SWReGAP) map as a guide. A supervised classification was also run using field data collected in the SWReGAP project and our field campaign. Field data were gathered and accuracy assessments were generated to compare outputs. Our hypothesis was that a resulting map would update and potentially improve upon the vegetation/land cover class distributions of the older SWReGAP map over the 24;000 km 2 study area. The estimated overall accuracies ranged between 43% and 75%, depending on which method and field dataset were used. The findings demonstrate the complexity of vegetation mapping, the importance of recent, highquality-field data, and the potential for misleading results when insufficient field data are collected.

Since the late 1800s, pinyon–juniper woodland across the western U.S. has increased in density and areal extent and encroached into former grassland areas. The San Carlos Apache Tribe wants to gain qualitative and quantitative information on the historical conditions of their tribal woodlands to use as a baseline for restoration efforts. At the San Carlos Apache Reservation, in east-central Arizona, large swaths of woodlands containing varying mixtures of juniper (Juniperus spp.), pinyon (Pinus spp.) and evergreen oak (Quercus spp.) are culturally important to the Tribe and are a focus for restoration. To determine changes in canopy cover, we developed image analysis techniques to monitor tree and large shrub cover using 1935 and 2017 aerial imagery and compared results over the 82-year interval. Results showed a substantial increase in the canopy cover of the former savannas, and encroachment (mostly juniper) into the former grasslands of Big Prairie. The Tribe is currently engaged in converting juniper woodland back into an open savanna, more characteristic of assumed pre-reservation conditions for that area. Our analysis shows areas on Bee Flat that, under the Tribe’s active restoration efforts, have returned woodland canopy cover to levels roughly analogous to that measured in 1935.