Mixed-Grass Prairie Canopy Structure and Spectral Reflectance Vary with Topographic Position

Managers of the nearly 0.5 million ha of public lands in North and South Dakota, USA rely heavily on manual measurements of canopy height in autumn to ensure conservation of grassland structure for wildlife and forage for livestock. However, more comprehensive assessment of vegetation structure could be achieved for mixed-grass prairie by integrating field survey, topographic position (summit, mid and toeslope) and spectral reflectance data. Thus, we examined the variation of mixed-grass prairie structural attributes (canopy leaf area, standing crop mass, canopy height, nitrogen, and water content) and spectral vegetation indices (VIs) with variation in topographic position at the Grand River National Grassland (GRNG), South Dakota. We conducted the study on a 36,000-ha herbaceous area within the GRNG, where randomly selected plots (1 km2 in size) were geolocated and included summit, mid and toeslope positions. We tested for effects of topographic position on measured vegetation attributes and VIs calculated from Landsat TM and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data collected in July 2010. Leaf area, standing crop mass, canopy height, nitrogen, and water content were lower at summits than at toeslopes. The simple ratio of Landsat Band 7/Band 1 (SR71) was the VI most highly correlated with canopy standing crop and height at plot and landscape scales. Results suggest field and remote sensing-based grassland assessment techniques could more comprehensively target low structure areas at minimal expense by layering modeled imagery over a landscape stratified into topographic position groups

Mixed-Grass Prairie Canopy Structure and Spectral Reflectance Vary with Topographic Position

Managers of the nearly 0.5 million ha of public lands in North and South Dakota, USA rely heavily on manual measurements of canopy height in autumn to ensure conservation of grassland structure for wildlife and forage for livestock. However, more comprehensive assessment of vegetation structure could be achieved for mixed-grass prairie by integrating field survey, topographic position (summit, mid and toeslope) and spectral reflectance data. Thus, we examined the variation of mixed-grass prairie structural attributes (canopy leaf area, standing crop mass, canopy height, nitrogen, and water content) and spectral vegetation indices (VIs) with variation in topographic position at the Grand River National Grassland (GRNG), South Dakota. We conducted the study on a 36,000-ha herbaceous area within the GRNG, where randomly selected plots (1 km2 in size) were geolocated and included summit, mid and toeslope positions. We tested for effects of topographic position on measured vegetation attributes and VIs calculated from Landsat TM and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data collected in July 2010. Leaf area, standing crop mass, canopy height, nitrogen, and water content were lower at summits than at toeslopes. The simple ratio of Landsat Band 7/Band 1 (SR71) was the VI most highly correlated with canopy standing crop and height at plot and landscape scales. Results suggest field and remote sensing-based grassland assessment techniques could more comprehensively target low structure areas at minimal expense by layering modeled imagery over a landscape stratified into topographic position groups

Monitoring soil carbon will prepare growers for a carbon trading system

California growers could reap financial benefits from the low-carbon economy and cap-and-trade system envisioned by the state's AB 32 law, which seeks to lower greenhouse gas emissions statewide. Growers could gain carbon credits by reducing greenhouse gas emissions and sequestering carbon through reduced tillage and increased biomass residue incorporation. First, however, baseline stocks of soil carbon need to be assessed for various cropping systems and management practices. We designed and set up a pilot soil carbon and land-use monitoring network at several perennial cropping systems in Northern California. We compared soil carbon content in two vineyards and two orchards (walnut and almond), looking at conventional and conservation management practices, as well as in native grassland and oak woodland. We then calculated baseline estimates of the total carbon in almond, wine grape and walnut acreages statewide. The organic walnut orchard had the highest total soil carbon, and no-till vineyards had 27% more carbon in the surface soil than tilled vineyards. We estimated wine grape vineyards are storing significantly more soil carbon per acre than almond and walnut orchards. The data can be used to provide accurate information about soil carbon stocks in perennial cropping systems for a future carbon trading system

Monitoring soil carbon will prepare growers for a carbon trading system

California growers could reap financial benefits from the low-carbon economy and cap-and-trade system envisioned by the state's AB 32 law, which seeks to lower greenhouse gas emissions statewide. Growers could gain carbon credits by reducing greenhouse gas emissions and sequestering carbon through reduced tillage and increased biomass residue incorporation. First, however, baseline stocks of soil carbon need to be assessed for various cropping systems and management practices. We designed and set up a pilot soil carbon and land-use monitoring network at several perennial cropping systems in Northern California. We compared soil carbon content in two vineyards and two orchards (walnut and almond), looking at conventional and conservation management practices, as well as in native grassland and oak woodland. We then calculated baseline estimates of the total carbon in almond, wine grape and walnut acreages statewide. The organic walnut orchard had the highest total soil carbon, and no-till vineyards had 27% more carbon in the surface soil than tilled vineyards. We estimated wine grape vineyards are storing significantly more soil carbon per acre than almond and walnut orchards. The data can be used to provide accurate information about soil carbon stocks in perennial cropping systems for a future carbon trading system

Object-based crop identification using multiple vegetation indices, textural features and crop phenology

Crop identification on specific parcels and the assessment of soil management practices are important for agro-ecological studies, greenhouse gas modeling, and agrarian policy development. Traditional pixel-based analysis of remotely sensed data results in inaccurate identification of some crops due to pixel heterogeneity, mixed pixels, spectral similarity, and crop pattern variability. These problems can be overcome using object-based image analysis (OBIA) techniques, which incorporate new spectral, textural and hierarchical features after segmentation of imagery. We combined OBIA and decision tree (DT) algorithms to develop a methodology, named Object-based Crop Identification and Mapping (OCIM), for a multi-seasonal assessment of a large number of crop types and field status.In our approach, we explored several vegetation indices (VIs) and textural features derived from visible, near-infrared and short-wave infrared (SWIR) bands of ASTER satellite scenes collected during three distinct growing-season periods (mid-spring, early-summer and late-summer). OCIM was developed for 13 major crops cultivated in the agricultural area of Yolo County in California, USA. The model design was built in four different scenarios (combinations of three or two periods) by using two independent training and validation datasets and the best DTs resulted in an error rate of 9% for the three-period model and between 12 and 16% for the two-period models. Next, the selected DT was used for the thematic classification of the entire cropland area and mapping was then evaluated applying the confusion matrix method to the independent testing dataset that reported 79% overall accuracy. OCIM detected intra-class variations in most crops attributed to variability from local crop calendars, tree-orchard structures and land management operations. Spectral variables (based on VIs) contributed around 90% to the models, although textural variables were necessary to discriminate between most of the permanent crop-fields (orchards, vineyard, alfalfa and meadow). Features extracted from late-summer imagery contributed around 60% in classification model development, whereas mid-spring and early-summer imagery contributed around 30 and 10%, respectively. The Normalized Difference Vegetation Index (NDVI) was used to identify the main groups of crops based on the presence and vigor of green vegetation within the fields, contributing around 50% to the models. In addition, other VIs based on SWIR bands were also crucial to crop identification because of their potential to detect field properties like moisture, vegetation vigor, non-photosynthetic vegetation and bare soil. The OCIM method was built using interpretable rules based on physical properties of the crops studied and it was successful for object-based feature selection and crop identification.The research of J.M. Peña-Barragan was granted by the Fulbright-MEC postdoctoral program, financed by the Secretariat of State for Research of the Spanish Ministry for Science and Innovation.Peer reviewe