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Miguel G. Velasco

Publications and source records attributed to Miguel G. Velasco.

6 recordsLinked to original sources
USGS2016

Exploiting differential vegetation phenology for satellite-based mapping of semiarid grass vegetation in the southwestern United States and northern Mexico

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.

Arizona↗
USGS2016

Evaluating 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 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.

International Journal of Advanced Remote Sensing a↗
USGS2016

MODIS 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 (MSAVI 2 ) of four dominant vegetation types over a 13-year period (2002 &ndash; 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.

International Journal of Advanced Remote Sensing a↗
USGS2002

Monitoring dust storms and mapping landscape vulnerability to wind erosion using satellite and ground-based digital images

Wind-induced dust emission in the southwestern United States is important regionally because of its impact on human health and safety and its influence on ecosystem dynamics. Factors that control dust emission include wind velocity, sediment availability, and surface conditions (e.g., vegetation type and degree of cover, surface crusts and armoring, and soil moisture - Gillette and Passi, 1988; Gillette and Hanson 1989; Marticorena and others, 1997). Emission of dust from the land surface is a process of degradation that depletes fine-grained minerals needed for optimum vegetation growth, creates potentially hazardous air quality for humans on a local and regional scale, and can affect climate on a regional and global scale. Future climatic change may lead to increased aridification of southwestern deserts, reducing protective vegetation and enhancing dust emissions, thereby increasing the impacts of dust in this region. It is currently not well understood how climate change in the Southwest will affect dust emission and, in turn, how dust emission will affect climate and human health. As part of a study of landscape vulnerability to wind erosion and the potential impacts of dust, we are investigating remotely sensed satellite, airborne, and ground-based image data to determine their ability to detect and monitor active dust storms, as well as to map areas vulnerable to wind erosion. A main objective has been to investigate the use of high temporal resolution digital images collected by satellite and a long-term, ground-based digital camera station, along with wind data collected at our field sites, to detect, monitor, and analyze the location, size, frequency, duration, and transport patterns of dust storms in the Mojave Desert of the southwestern United States. Generally, many current methods and instruments are not developed to the level required to routinely detect and monitor dust storms or to develop models that accurately predict total dust flux and emission rates. Improving these capabilities is critical to generating baseline datasets for assessments of landscape vulnerability to future climate change. On going modeling efforts are attempting to identify meteorological parameters and soil surface roughness parameters needed to predict the vulnerability of various geomorphic substrates to wind erosion. Datasets to calibrate model results are difficult to collect and typically not available. Results of our studies, incorporating digital image maps and new methods using remotely sensed images to identify and monitor dust sources, will be useful for the calibration of dust-emission models of the Mojave Desert and other arid environments. At the same time, we are exploring new methods to map the amount, sizes, and spatial distribution of particles at the surface to enable detailed wind-erosion vulnerability mapping at a regional scale.

California↗
USGS1996

City of Flagstaff Project: Ground Water Resource Evaluation, Remote Sensing Component

Many regions, cities, and towns in the Western United States need new or expanded water resources because of both population growth and increased development. Any tools or data that can help in the evaluation of an area's potential water resources must be considered for this increasingly critical need. Remotely sensed satellite images and subsequent digital image processing have been under-utilized in ground water resource evaluation and exploration. Satellite images can be helpful in detecting and mapping an area's regional structural patterns, including major fracture and fault systems, two important geologic settings for an area's surface to ground water relations. Within the United States Geological Survey's (USGS) Flagstaff Field Center, expertise and capabilities in remote sensing and digital image processing have been developed over the past 25 years through various programs. For the City of Flagstaff project, this expertise and these capabilities were combined with traditional geologic field mapping to help evaluate ground water resources in the Flagstaff area. Various enhancement and manipulation procedures were applied to the digital satellite images; the results, in both digital and hardcopy format, were used for field mapping and analyzing the regional structure. Relative to surface sampling, remotely sensed satellite and airborne images have improved spatial coverage that can help study, map, and monitor the earth surface at local and/or regional scales. Advantages offered by remotely sensed satellite image data include: 1. a synoptic/regional view compared to both aerial photographs and ground sampling, 2. cost effectiveness, 3. high spatial resolution and coverage compared to ground sampling, and 4. relatively high temporal coverage on a long term basis. Remotely sensed images contain both spectral and spatial information. The spectral information provides various properties and characteristics about the surface cover at a given location or pixel (that is, vegetation and/or soil type). The spatial information gives the distribution, variation, and topographic relief of the cover types from pixel to pixel. Therefore, the main characteristics that determine a pixel's brightness/reflectance and, consequently, the digital number (DN) assigned to the pixel, are the physical properties of the surface and near surface, the cover type, and the topographic slope. In this application, the ability to detect and map lineaments, especially those related to fractures and faults, is critical. Therefore, the extraction of spatial information from the digital images was of prime interest in this project. The spatial information varies among the different spectral bands available; in particular, a near infrared spectral band is better than a visible band when extracting spatial information in highly vegetated areas. In this study, both visible and near infrared bands were analyzed and used to extract the desired spatial information from the images. The wide swath coverage of remotely sensed satellite digital images makes them ideal for regional analysis and mapping. Since locating and mapping highly fractured and faulted areas is a major requirement for ground water resource evaluation and exploration this aspect of satellite images was considered critical; it allowed us to stand back (actually up about 440 miles), look at, and map the regional structural setting of the area. The main focus of the remote sensing and digital image processing component of this project was to use both remotely sensed digital satellite images and a Digital Elevation Model (DEM) to extract spatial information related to the structural and topographic patterns in the area. The data types used were digital satellite images collected by the United States' Landsat Thematic Mapper (TM) and French Systeme Probatoire d'Observation de laTerre (SPOT) imaging systems, along with a DEM of the Flagstaff region. The USGS Mini Image Processing Sy

Open-File Report↗