Robust estimates of forest above-ground biomass (AGB) are needed in order to constrain the uncertainty in regional and global carbon budgets, predictions of global climate change and remote sensing efforts to monitor large scale changes in forest cover and biomass. Estimates of AGB and their associated uncertainty are also essential for international forest-based climate change mitigation strategies such as REDD+. Mangrove forests are widely recognised as globally important carbon stores. Continuing high rates of global mangrove deforestation represent a loss of future carbon sequestration potential and could result in significant release into the atmosphere of the carbon currently being stored within mangroves. The main aims of this thesis are 1) to provide information on the current AGB stocks of mangrove forests in Kenya at spatial scales relevant for climate change research, forest management and REDD+ and 2) to evaluate and constrain the uncertainty associated with these AGB estimates. This thesis adopted both a ground-based statistical approach and a remote sensing based approach to estimating mangrove AGB in Kenya. Allometric equations were developed for Kenyan mangroves using mixed-effects regression analysis and uncertainties were fully propagated (using a Monte Carlo based approach) to estimates of AGB at all spatial scales (tree, plot, region and landscape). In this study, species and site effects accounted for a large proportion (41%) of the total variability in mangrove AGB. The generic biomass equation produced for Kenyan mangroves has the potential for broad application as it can be used to estimate the AGB of new trees where there is no pre-existing knowledge of the specific species-site allometric relationship. The 95% prediction intervals for landscape scale estimates of total AGB suggest that between 5.4 and 7.2 megatonnes (Mt) of AGB is currently held in Kenyan mangrove forests. An in-depth evaluation of the relative contribution of various components of uncertainty (measurement, parameter and residual uncertainty) to the magnitude of the total uncertainty of AGB estimates was carried out. This evaluation was undertaken using both the mixed-effects regression model and a standard ordinary least squares (OLS) regression model. The exclusion of measurement uncertainty during the biomass estimation process had negligible impact on the magnitude of the uncertainty regardless of spatial scale or tree size. Excluding the uncertainty due to species and site effects (from the mixed-effects model) consistently resulted in a large reduction (~ 70%) in the overall uncertainty. Estimates of the uncertainty produced by the OLS model were unrealistically low which is illustrative of the general need to account for group effects in biomass regression models. L-band Synthetic Aperture Radar (SAR) was used to estimate the AGB of Kenyan mangroves. There was an observable relationship (R2 = 0.45) between L-band HH and AGB with HH backscatter found to decrease as a function of increasing AGB. There was no significant relationship found between L-band HV and AGB. The negative relationship between HH and AGB in this study can possibly be attributed to enhanced backscatter at lower AGB due to strong double-bounce and direct surface scattering from short stature/open forests and attenuation of the SAR signal at higher AGB. The SAR-derived estimate of total AGB for Kenyan mangroves was 5.32 Mt ± 18.6%. However, due to the unexpected nature of the HH-AGB relationship found in this study the SAR-derived estimates of mangrove AGB in this study should be considered with caution.

There is a lack of precise and universally accepted approach in the quantification of carbon sequestered in aboveground woody biomass using remotely sensed data. Drafting of the Kyoto Protocol has made the subject of carbon sequestration more important, making the development of accurate and cost-effective remote sensing models a necessity. There has been much work done in estimating aboveground woody biomass from spectral data using the traditional multiple linear regression analysis approach and the Finnish k-nearest neighbor approach, but the accuracy of these methods to estimate biomass has not been compared. The purpose of this study is to compare the ability of these two methods in estimating above ground biomass (AGB) using spectral data derived from Landsat ETM+ imagery.

The goal of quantifying the woody cover and biomass of tropical savannas, woodlands and forests using satellite data is becoming increasingly important, but limitations in current scientific understanding reduce the utility of the considerable quantity of satellite data currently being collected. The work contained in this thesis reduces this knowledgegap, using new field data and analysis methods to quantify changes using optical, radar and LiDAR data. The first paper shows that high-resolution optical data (Landsat & ASTER) can be used to track changes in woody vegetation in the Mbam Djerem National Park in Cameroon. The method correlates a satellite-derived vegetation index with field-measured canopy cover, and the paper concludes that forest encroached rapidly into savanna in the region from 1986-2006. Using the same study area, but with radar remote sensing data from 1996 and 2007 (ALOS PALSAR & JERS-1), the second paper shows that radar backscatter correlates well with field-measured aboveground biomass (AGB). This dataset confirms the woody encroachment within the park; however, in a larger area around the park, deforestation dominates. The AGB-radar relationships described above are expanded in the next paper to include field plots from Budongo Forest (Uganda), the Niassa Reserve (north Mozambique), and the Nhambita Community Project (central Mozambique). A consistent AGB-radar relationship is found in the combined dataset, with the RMSE for predicted AGB values for a site increasing by <30 %, compared with a site-specific equation, when using an AGB-radar equation derived from the three other sites. The study of the Nhambita site is extended in the following paper to assess the ability of radar to detect change over short time periods in this environment, as will be needed for REDD (Reducing Emissions from Deforestation and Degradation). Using radar mosaics from 2007 and 2009, areas known (from detailed ground data) to have been degraded decreased in AGB in the radar change detection, whereas areas of agroforestry and forest protection showed small increases.

In the last decade, advances in sensor and computing technology are revolutionary. The latest-generation of hyperspectral and synthetic aperture radar ((SAR) instruments have increased their spectral, spatial, and temporal resolution. Consequently, the data sets collected are increasing rapidly in size and frequency of acquisition. Remote sensing applications are requiring more computing resources for data analysis. High performance computing (HPC) infrastructure such as clusters, distributed networks, grids, clouds and specialized hardware components, have been used to disseminate large volumes of remote sensing data and to accelerate the computational speed in processing raw images and extracting information from remote sensing data. In previous research we have shown that we can improve computational efficiency of a hyperspectral image denoising algorithm by parallelizing the algorithm utilizing a distributed computing grid. In recent years, computational cloud technology is emerging, bringing more flexibility and simplicity for data processing. Hadoop MapReduce is a software framework for distributed commodity computing clusters, allowing parallel processing of massive datasets. In this project, we implement a software application to map forest aboveground biomass (AGB) with normalized difference vegetation indices (NDVI) using Landsat Thematic Mapper’s bands 4 and 5 (ND45). We present observations and experimental results on the performance and the algorithmic complexity of the implementation. There are three research questions answered in this thesis, as follows. 1) How do we implement remote sensing algorithms, such as forest AGB mapping, in a computer cloud environment? 2) What are the requirements to implement distributed processing of remote sensing images using the cloud programming model? 3) What is the performance increase for large area remote sensing image processing in a cloud environment? / Graduate / 0799 / 0984

Measurements of soil moisture are a crucial component for understanding the global water and carbon cycle, weather forecasting, climate models, drought prediction, and agriculture production. Active and passive microwave radar instruments are currently in use for remote sensing of soil moisture. Signals of Opportunity (SoOp) based remote sensing has recently emerged as a complementary method for soil moisture remote sensing. SoOp reuses general digital communication signals allowing the reuse of allocated wireless communication signal bands for science measurements. This thesis developed a tower based SoOp instrument implementing frequencies in the P-Band and S-Band. Two field campaigns were conducted using this new instrument during the summers of 2017 and 2018 at Purdue's Agronomy Center for Research and Education.

Tropical forests are major components of Earth’s carbon stocks, but their diversity and structural complexity pose major challenges for making accurate estimates of their above ground biomass (AGB). Palms, in particular, are prominent and unique components of many tropical forests that have anatomical and physiological differences from dicot trees which affect their height - diameter allometry and consequently, our ability to accurately estimate their AGB. We focused on improving height estimates and AGB models for a highly abundant palm, Prestoea montana, in the Luquillo Forest Dynamics Plot, Puerto Rico. We measured stem height (Hstem), diameter at breast height (DBH) and basal diameter (DB) for 1215 individual palms. Although palms do not develop secondary xylem, we found a strong relationship both between Hstem:DBH and Hstem:DB for P. montana which indicates that its mechanical H:D scaling exhibits similar mechanical constraints of dicotyledonous trees. Additionally, we provide evidence that P. montana’s H:D allometry is mediated by several sources of environmental heterogeneity including slope, elevation, and neighborhood crowding (as a proxy for local competition). We applied our H:D allometric model to hindcast AGB dynamics in the Luquillo Forest Dynamics Plot. Finally, we demonstrated that neighborhood crowding has a negative effect on P. montana’s growth. Our study enables improved  estimates in Puerto Rico and provides novel insight to the growth dynamics of palms in tropical forests.

Herbage and cattle production in semi-arid regions are primarily controlled by climate variation particularly rainfall variability and secondarily by disturbances such as drought, grazing and fire. These factors interact at different spatial and temporal scales in a complex manner difficult to observe or comprehend and, reduce availability and quality of herbage and cattle productivity. Variables for quantifying rangeland productivity are thus rarely available and unreliable yet options for sustainable management are limited. Grazing experiments have provided useful insight about ecological and management factors involved in rangeland functioning, but they have limited scope to deal with high environmental variation. This highlights the need for a systems approach for monitoring rangeland and cattle productivity at the appropriate spatial and temporal scales to enable productivity to be maximised whilst risk to climate variation is minimised. This study explored two broad objectives: to determine the ranch-scale impacts of rainfall variability and drought on herbaceous aboveground biomass (AGB) using optical remote sensing; and to parameterise, evaluate and apply a systems model, the Sustainable Grazing Systems (SGS) whole farm model to complement grazing experiments in assessing the effects of grazing strategies on beef cattle production. To determine rainfall variability impacts, twenty regression models were firstly developed between measured herbaceous AGB and, classical and extended multispectral vegetation indices (MVIs) derived from a Landsat 8 image. End-of-season herbaceous AGB was predicted with high accuracy (r2 range = 0.55 to 0.71; RMSE range = 840 to 1480 kgha-1). The most accurate model was used to construct a regression between rainfall and AGB derived from peak-season Landsat images available between 1992 and 2017. Standardised precipitation index and standardised anomalies of herbaceous AGB production were then used in a convergence of evidence approach to determine the response of AGB to rainfall variability and drought intensity. Total wet season rainfall revealed high variability (33 to 41 % CV) and subsequent herbaceous AGB production were 18 to 35 % more variable. Spatial heterogeneity of AGB production across herbaceous communities were high and deviated from mean AGB by 51 to 69 %. Landscape-level temporal variation of AGB production remained stable despite the increase of climate variability experienced in the region in the past 50 years. Climate inputs and parameter sets for upper-, mid- and foot- slope land types and key grass species, Urochloa mosambicensis and Eragrostis curvula were developed by integrating spatial data with previous soil surveys and extensive reviews of published experiments. A simulation experiment was conducted between 1992 and 2017 for all combinations of land types and grass species to analyse the extent of improvement resulting from parameter adjustments. The SGS model predicted the growth pattern known for grasses native to dry regions of southern Africa. The model represented measured herbaceous biomass moderately well (r2 = 0.57), at low average error (RMSE, 820 kg DM ha-1) despite huge discrepancies in summary statistics for measured (mean, 3877 kg DM ha-1) and simulated (mean, 3071 kg DM ha-1) biomass and residuals. Model predictions were also significantly correlated with remotely sensed AGB (r2 = 0.46) at reasonable overall performance error (RMSE, 981 kg DM ha-1). The integrated workflow developed for parameterising and calibrating the SGS pasture-simulation model can benefit model users in data-constrained environments. Animal growth parameters specific to Brahman weaner steers were defined in the SGS model to enable evaluation of impacts of recommended (10 haLU-1) and other three stocking rates (7, 15 and 20 haLU-1) and multi-paddock grazing systems (2-, 3- and 4- paddocks per herd) on rangeland productivity. Overall, there were no observable differences in herbage production and dry matter intake irrespective of stocking rate and multi-paddock grazing system. But stocking rate effects on animal production were more pronounced compared to multi-paddock grazing systems. To maximise cattle productivity in semi-arid rangelands, management should be emphasised on manipulation of stocking rates over multi-paddock grazing systems. Keywords Rangeland monitoring, climate risk, sustainability, animal productivity, grazing strategies / Thesis (PhD (Animal Production Management))--University of Pretoria, 2020. / National Research Foundation of South Africa / University of Pretoria Department of Research and Innovation Support / Animal and Wildlife Sciences / PhD / Unrestricted

Future climate projections suggest an increase in average temperature as well as a decrease in average winter rainfall across the south-west Australian floristic region (SWAFR). These adverse future climatic conditions will amplify the intensity and frequency of disturbance events such as drought and fire. Mediterranean forests within the SWAFR are prone to drought and fire disturbance and have acquired resilience through the selection of drought and fire tolerable species. However, shifts in the magnitude of these disturbance events could increase the recovery period required for recruitment, causing a shift in forest structure and decreasing the resilience of these ecosystems to future disturbances. In this study, we investigated above-ground biomass (AGB) accumulation of understorey plants at sites within the Northern Jarrah Forest (NJF) that have experienced different degrees of drought and fire intensity. We found that within a disturbance event, sites experiencing either more severe drought and fire intensities on average accumulated substantially more understorey AGB than sites subjected to both low drought and moderate fire intensities. This suggests that understorey species within the SWAFR gain a competitive advantage in high drought and fire severity conditions and are highly tolerant to drought. However, the increase in understorey AGB accumulation also suggests a shift in overall forest structure to more dense, compact, low-ground small stems, which is known to increase fire probability. An increase in fire probability shortens the time period between fire intervals and can detrimentally affect forest recovery, especially in drought conditions. Therefore, these changes may shift ecosystems within the SWAFR to a state of non-equilibrium and reduce resilience to future disturbance events.

This study assessed the utility of an object-oriented approach to deciduous and coniferous forest volume and above ground biomass estimation, based solely on small-footprint, multiple return lidar data. The study area is located in Appomattox Buckingham State Forest in the Piedmont physiographic province of Virginia, U.S.A, at 78°41’ W, 37°25’ N. Vegetation is composed of various coniferous, deciduous, and mixed forest stands. The eCognition segmentation algorithm was used to derive objects from a lidar-based canopy height model (CHM). New segment selection criteria, based on between- and within-segment CHM variance, and average field plot size, were developed. Horizontal point samples were used to measure in-field volume and biomass, for 2-class (deciduous-coniferous) and 3-class (deciduous-coniferous-mixed) forest schemes. Per-segment lidar distributional parameters, e.g., mean, range, and percentiles, were extracted from the lidar data and used as input to volume and biomass regression analysis. Discriminant classification was performed using lidar point height and CHM distributions. There was no evident difference between the two-class and three-class approaches, based on similar adjusted R2 values. Two-class forest definition was preferred due to its simplicity. Two-class adjusted R2 and root mean square error (RMSE) values for deciduous volume (0.59; 51.15 m3/ha) and biomass (0.58; 37.41 Mg/ha) were improvements over those found in another plot-based study for the same study area. Although coniferous RMSE values for volume (38.03 m3/ha) and biomass (17.15 Mg/ha) were comparable to published results, adjusted R2 values (0.66 and 0.59) were lower. This was attributed to more variability and a narrower range (6.94 - 350.93 m3/ha) in measured values. Classification accuracy for discriminant classification based on lidar point height distributions (89.2%) was a significant improvement over CHM-based classification (79%). A lack of modeling and classification differences between average segment sizes was attributed to the hierarchical nature of the segmentation algorithm. However, segment-based modeling was distinctly better than modeling based on existing forest stands, with values of 0.42 and 62.36 m3/ha (volume) and 0.46 and 41.18 Mg/ha (biomass) for adjusted R2 and RMSE, respectively. Modeling results and classification accuracies indicated that an object-oriented approach, based solely on lidar data, has potential for full-scale forest inventory applications. / Ph. D.

The study aims at estimation and mapping the amount of carbon allocated in above ground biomass of wood and in organo-mineral soil horizon at sites where reclamation and spontaneous succession took place on spoil heaps after coal mining. Several categories of data have been used to meet the objectives, namely ground field measurements, laboratory analyses of soil samples, airborne hyperspectral data from VNIR region, and airborne LiDAR scanning data. The digital imagery analysis, GIS modeling and multivariation statistical methods were applied in data assessment. The results show that there is a 7 600 tons of carbon allocated in above ground wood biomass in the area of 209 ha, and 8 100?12 200 tons in the soil A horizon in the region of the same size. The results proofed: 1/ statistically significant negative relationships (p < 0,01) between slope and amount of soil carbon, where higher negative correlation was for broad leaved species; 2/ statistically significant difference (p < 0,05) between amount of soil carbon under broad leaved and needle classes and under different species, the highest between soils under Alnus sp. and Pinus sp.; 3/ statistically significant relationships (p < 0,05) between the amount of carbon allocated in the aboveground wood biomass and that in the soil A horizon under the needle leaved class and under the spontaneous wood vegetation.