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The structural loss rates of standing dead trees (SDTs) affect a variety of processes of interest to ecologists and foresters, yet the decomposition of SDTs has been traditionally characterized by qualitative decay classes, reductions in wood density as decay progresses, and sampling schemes focused on estimating snag longevity. By establishing a methodology to accurately and efficiently quantify SDT structural loss over time, these estimated structural loss rates would improve the performance of a variety of models and potentially provide new insight as to the manner in which SDTs undergo degradation in various conditions. The specific objective of this study were: 1) utilize the TreeVolX algorithm to estimate the volume of 29 SDTs scanned with terrestrial lidar; 2) develop a novel, voxel-based change detection algorithm capable of providing automated structural loss estimates with multitemporal terrestrial lidar observations; and 3) estimate and characterize the structural loss rates of Pinus taeda and Quercus stellata in southeastern Texas. A voxel-based change detection methodology was developed to accurately detect and quantify structural losses and incorporated several methods to mitigate the challenges presented by shifting tree and branch positions as SDT decay progresses. The volume and structural loss of 29 SDTs, composed of Pinus taeda and Quercus stellata, were successfully estimated using multitemporal terrestrial lidar observations over elapsed times ranging from 71 to 753 days. Pine and oak structural loss rates were characterized by estimating the amount of volumetric loss occurring in 20 equal-interval height bins of each SDT. Results showed that large pine snags exhibited more rapid structural loss in comparison to medium-sized oak snags in southeastern Texas.

This chapter discusses the fate of allochthonous particulate organic carbon (C) (POC), either sorbed organic C (OC) onto clay particles, as detrital organic particles, and dissolved OC (DOC) in modern coastal blue C ecosystem (BCE). BCEs, or the C stored in coastal systems such as mangroves, salt marshes, and seagrass meadows, form some of the largest global C reserves, and are distributed throughout numerous latitudinal zones. Coastal tidal marshes are largely found in temperate zones but do extend into the tropics, mangroves are confined to tropical and sub-tropical regions due to their freeze intolerance, and seagrasses are broadly distributed from cold polar waters to the tropics. The fate of allochthonous blue C released into coastal waters and the open sea or sequestered in the coastal BCEs is highly specific to the coastal margin setting. Coastal aqueous POC and DOC forms constitute the major pool of organic matter providing C and energy to heterotrophs in estuaries.

Accurate measures of forest structural parameters are essential to forest inventory and growth models, managing wildfires, and modeling of carbon cycle. Terrestrial laser scanning (TLS) fills the gap between tree scale manual measurements and large scale airborne LiDAR measurements by providing accurate below crown information through non-destructive methods. This study developed innovative methods to extract individual tree height, diameter at breast height (DBH), and crown width of trees in East Texas. Further, the influence of scan settings, such as leaf-on/leaf-off seasons, tree distance from the scanner, and processing choices, on the accuracy of deriving tree measurements were also investigated. DBH was retrieved by cylinder fitting at different height bins. Individual trees were extracted from the TLS point cloud to determine tree heights and crown widths. The R-squared value ranged from 0.91 to 0.97 when field measured DBH was validated against TLS derived DBH using different methods. An accuracy of 92% (RMSE = 1.51 m) was obtained for predicting tree heights. The R-squared value was 0.84 and RMSE was 1.08 m when TLS derived crown widths were validated using field measured crown widths. Examples of underestimations of field measured forest structural parameters due to tree shadowing have also been discussed in this study. The results from this study will benefit foresters and remote sensing studies from airborne and spaceborne platforms, for map upscaling or calibration purposes, for aboveground biomass estimation, and prudent decision making by the forest management.

Above ground biomass (AGB) is a crucial ecological variable and has to be accurately estimated to understand potential changes of the climate system and to reduce uncertainties in the estimates of forest carbon budget. The overall goal of this research is to estimate tree level AGB change using multi-temporal terrestrial laser scanning (TLS) datasets for trees in East Texas. Specific objectives are to (1) develop models using TLS parameters to estimate tree level AGB; and (2) investigate different conceptual approaches for estimating AGB change. Since majority of the AGB estimation models are developed only using diameter at breast height (DBH), we investigated the potential of TLS by extracting various geometric and statistical parameters for tree level AGB estimation. National and regional level AGB estimation models were developed for loblolly pines. To estimate the change in AGB, three different approaches were followed. The best AGB estimation model for loblolly pines had DBH, height variance, and interquartile distance as independent variables. The best AGB estimation model for hardwoods included volume and crown width as independent variables. For AGB change of loblolly pines, direct modeling of AGB change with TLS data available for 2009 and 2012 provided the best results. An extensive literature review reveals that this is the first study to model the change in AGB using different innovative and conceptual approaches with multi-temporal TLS data. The results of our study indicate the capability of TLS to model the change in tree level AGB, with potential for reducing the amount of field work when using multi-temporal terrestrial TLS datasets. We believe that the results of this study will benefit forest management and planners for prudent decision making, and other remote sensing studies from airborne and spaceborne platforms, for map upscaling, data fusion, or calibration purposes.

In this paper, we construct a homogeneous spatio-temporal model to describe the variability of significant wave height over small regions of the sea and over short periods of time. Then, the model is extended to a non-homogeneous one that is valid for larger areas of the sea and for time periods up to ten hours. To validate the proposed model, we reconstruct the significant wave height surface under different scenarios and then compare it to satellite measurements and the C-ERA-40 field.

The majority of large-scale forest inventories use some form of fixed-area sampling. In recent books and scientific publications, the estimators for these surveys have been derived using two different paradigms. The first, which will be referred to as the tessellated plane paradigm, relies on the assumption that the area is divided into equal area contiguous blocks which constitutes an area sampling frame. These blocks can be formed using the pixels of a satellite image. Inference for this paradigm is drawn by considering the distribution of the finite number of possible estimates derived from the area frame. The other approach will be referred to as the continuous plane paradigm, which assumes random positioning of the sample plots. Inference for this paradigm can be drawn without relying on an area frame. The necessary assumptions for using either paradigm are compared when current forestry techniques, such as the use of open cluster plots and satellite imagery, are used. The tessellated population paradigm requires many more assumptions, some of which may be difficult to justify. Estimators of both forest area and basal area under these paradigms are contrasted and the strengths and weaknesses of both are compared. The primary advantage of the tessellated population paradigm is that more strata can be used to reduce the variance of the estimators. Both paradigms appear to be reasonable alternatives until some of the practical problems of forest inventories are considered. Examples include the difficulty associated with measuring plots that partially cover water and adjusting for changes in remote sensing technologies over time. In these situations, the tessellated population paradigm may be more difficult to implement because of the difficulty in estimating changes over time and properly accounting for linear and fragmented strata, such as rivers and lakes.

Topographic data sets are often composed of terrain samples arranged in irregular meshes. Many gradient estimation methods require the data to be arranged in a regular mesh; irregular meshes present a difficulty for them. This paper presents a novel method of estimating surface gradients from irregularly spaced inputs. The method is derived using directional derivatives and is shown to be a generalization of traditional finite difference methods. Analytical tests were used to evaluate the method, which was shown to be accurate and robust.