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Leaves in forests assimilate carbon from the atmosphere and woody components store the net production of that assimilation. Separate structure measurements of leaves and woody components advance the monitoring and modeling of forest ecosystem functions. This dissertation provides a method to determine, for the first time, the 3-D spatial arrangement and the amount of leafy and woody materials separately in a forest by classification of lidar returns from a new, innovative, lidar scanner, the Dual-Wavelength Echidna® Lidar (DWEL). The DWEL uses two lasers pulsing simultaneously and coaxially at near-infrared (1064 nm) and shortwave-infrared (1548 nm) wavelengths to locate scattering targets in 3-D space, associated with their reflectance at the two wavelengths. The instrument produces 3-D bispectral "clouds" of scattering points that reveal new details of forest structure and open doors to three-dimensional mapping of biophysical and biochemical properties of forests. The three parts of this dissertation concern calibration of bispectral lidar returns; retrieval of height profiles of leafy and woody materials within a forest canopy; and virtual reconstruction of forest trees from multiple scans to estimate their aboveground woody biomass. The test area was a midlatitude forest stand within the Harvard Forest, Petersham, Massachusetts, scanned at five locations in a 1-ha site in leaf-off and leaf-on conditions in 2014. The model for radiometric calibration assigned accurate values of spectral apparent reflectance, a range-independent and instrument-independent property, to scattering points derived from the scans. The classification of leafy and woody points, using both spectral and spatial context information, achieved an overall accuracy of 79±1% and 75±2% for leaf-off and leaf-on scans, respectively. Between-scan variation in leaf profiles was larger than wood profiles in leaf-off seasons but relatively similar to wood profiles in leaf-on seasons, reflecting the changing spatial heterogeneity within the stand over seasons. A 3-D structure-fitting algorithm estimated wood volume by modeling stems and branches from point clouds of five individual trees with cylinders. The algorithm showed the least variance for leaf-off, woody-points-only data, validating the value of separating leafy and woody points to the direct biomass estimates through the structure modeling of individual trees.

MSCAGR (Plant Production) / Department of Plant Production / Agricultural production under rain-fed conditions is largely dependent on the availability of water stored in the soil during rainfall events. The production of cassava (Manihot esculenta Crantz) under rain-fed conditions in the north-eastern part of South Africa is constrained by low and erratic rainfall events. Improving cassava production in the area requires the use of cassava varieties which are efficient in the use of limited soil moisture. The current climate change and increasing population growth on the planet will place more pressure on agriculture to produce more food using less water. Therefore, previously under-researched and underutilised crop like cassava could be used to bridge the food gap in the future. Although the crop currently occupies low levels of utilisation in South Africa and it is cultivated by smallscale farmers in the Low-veld of Mpumalanga, Limpopo and Kwazulu-Natal provinces using landraces with no improved varieties available in the country. Information on the actual pattern of water extraction, water use and water use efficiency of cassava landraces grown in the dry environments of South Africa is limited. Therefore, the objective of the study was to determine the differences in water use efficiency, gaseous exchange and yield among four cassava landraces grown under rain-fed conditions. Two field experiments were conducted during the wetter (2016/2017) and drier (2017/2018) cropping season at the University of Venda's experimental farm. The trials were laid in a Randomized Complete Block Design (RCBD) consisting of four cassava landraces (ACC#1, ACC#2, ACC#3, and ACC#4) replicated three times. Mature cassava stem cuttings of 30 cm long, were planted manually at a spacing of 1 m x 1 m in both seasons. Each experimental unit consisted of six plant rows of 6 m length (36 m2) and 8 rows of 8 m length (64 m2) in the 2016/17 and 2017/2018 cropping season, respectively. The experiments were under rain-fed conditions without fertilizer additions and the plots were kept weed-free throughout the experimental period. Data collected in the field included soil moisture content, gaseous exchange parameters (net leaf ܥܱଶ uptake, stomatal conductance, and intracellular carbon dioxide concentration), chlorophyll content index (CCI), maximum photochemical quantum yield of PSII (Fv/Fm), effective quantum yield of PSII (ФPSII) and photosynthetic active radiation (PAR). Yield and yield components (root length (cm), root girth (cm), number of storage roots and mean root weight (g plant-1), root yield and aboveground biomass), as well as water use efficiency (WUE), were determined at harvest. Soil moisture content was measured at seven-day interval from sowing until harvest using a neutron probe. Soil moisture data were used to determine crop water use using the water balance approach. There was no variation in the root yield and yield components amongst the landraces in 2017/2018 cropping season but, genotypes affected aboveground biomass, root girth, number of roots per plant and root yield in 2016/2017 cropping season. There was a significant difference (P<0.01) in number of roots (per plant) 81% and 62% greater in ACC#3 and ACC#2 (6.7 & 6.0, respectively) compared with ACC#1 and ACC#4, which both recorded 4 roots per plant. Similarly, root girth was greater in ACC#3 (17.8 cm) and ACC#2 (18.2 cm) compared to ACC#1 (14.1 cm) and ACC#4 (12.9 cm), which were statistically the same. In contrast, total biomass (P<0.01) and root yield (P<0.05) were greater in ACC#3 (20.7 and 11.9 t ha-1, respectively) and ACC#1 (22.0 and 11.3 t ha-1, respectively) compared to ACC#2 and ACC#4 with root yields of 10.2 and 9.5 t ha-1, biomass of 17.1 and 16.3 t ha-1, respectively. Although the genotype x cropping season interaction did not affect root yield and yield components, root yield (by 33.8%; 2.7 t ha-1) and yield components were greater in the wetter compared to the drier season as expected. Water use efficiency of root yield (WUErt) and water use efficiency of biomass production (WUEb) varied with landraces in season I from 37.0 kg ha-1 mm-1 (ACC#4) to 46.60 kg ha-1 mm-1 (ACC#3), and between 71.30 kg ha-1 mm-1 (ACC#2) and 86.0 kg ha-1 mm-1 (ACC#1), respectively. Landraces did not differ in their water use and soil moisture extraction in both seasons but differed in season. However, there was a significant positive correlation between water use efficiency of root yield (WUErt) (0.963***) and water use efficiency of biomass production (WUEb) (0.847***). WUE of biomass production was greater in the drier than the wetter season partly because of dry matter accumulation per evapotranspiration within the landraces. Photosynthesis did not vary with landraces, however, stomatal conductance varied with landraces from 0.08 mmol m-2 s-1 (ACC#4) to 0.2 mmol m-2 s-1 (ACC#2). In contrast, ACC#1 and ACC#3 recorded the same value of stomatal conductance, which is 0.1 mmol m-2 s-1. The effective quantum yield of PSII photochemistry (ΦPSII) did not vary with landraces but the maximum photochemical quantum yield of PSII (Fv/Fm) varied with landraces from 0.652 (ACC#4) to 0.792 (ACC#3) in season II. The proportion of intercepted radiation was affected by landraces in 2017/2018 cropping season. Highest proportion of intercepted radiation was observed in ACC#3 and the lowest in ACC#2. Proportion of intercepted radiation varied with landraces from 22.62% (ACC#2) to 86.45% (#ACC#3). There were significant genotypic variations in chlorophyll content recorded in both season. Chlorophyll content varied with landraces from 33.1 CCI (ACC4) to 55.4 CCI (#ACC3) in the 2016/2017, and in 2017/2018 cropping season chlorophyll content varied with landraces from 36.9 CCI (ACC4) to 78.7 CCI (#ACC3). The highest genotypic variation in chlorophyll content was observed in ACC#3, whilst the lowest chlorophyll content was recorded in ACC#4 in both seasons. / NRF

In addition to causing an increase in mean temperatures, climate change is also altering precipitation regimes across the globe. General circulation models project both latitude-dependent changes in precipitation mean and increases in precipitation variability. These changes in water availability will impact terrestrial primary productivity, the fixation of carbon dioxide into organic matter by plants. In my thesis, I addressed the following three questions: 1.) What will be the relative effect of changes in the mean and standard deviation of annual precipitation on mean annual primary production? 2.) Which ecosystems will be the most sensitive to changes in precipitation? 3.) Will increases in production variability be disproportionately greater than increases in precipitation variability? I gathered 58 time series of annual precipitation and aboveground net primary production (ANPP) from long-term ecological study sites across the globe. I quantified the sensitivity of ANPP at each site to changes in precipitation mean and variance. My results indicated that mean ANPP is about 40 times more sensitive to changes in precipitation mean than to changes in precipitation variance. I showed that semi-arid ecosystems such as shortgrass steppe in Colorado or typical steppe in Inner Mongolia may be the most sensitive to changes in precipitation mean. At these sites and several others, a 1% change in mean precipitation may result in a change in ANPP that is greater than 1%. To address how increases in interannual precipitation variability will impact the variability of ANPP, I perturbed the variability of observed precipitation time series and evaluated the impact of this perturbation on predicted ANPP variability. I found that different assumptions about the precipitation-ANPP relationship had different implications for how increases in precipitation variability will impact ANPP variability. Increases in ANPP variability were always directly proportional to increases in precipitation variability when ANPP was modeled as a simple linear or a lagged function of precipitation. However, when ANPP was modeled as a nonlinear, saturating function of precipitation, increases in ANPP variability were disproportionately low compared to increases in precipitation variability during wet years but disproportionately high during dry years. My thesis addresses an existing research gap regarding the long-term impact of increases in interannual precipitation variability on key ecosystem functioning. I showed that increases in precipitation variability will have negligible impacts on ANPP mean and have disproportionately large impacts on ANPP variability only when ANPP is a concave down, nonlinear function of precipitation. My work also demonstrates the importance of the precipitation-ANPP relationship in determining the magnitude of impacts to ANPP caused by changes in precipitation. Finally, my thesis highlights the potential for considerable changes in ANPP variability due to increases in precipitation variability.

京都大学 / 0048 / 新制・課程博士 / 博士(地域研究) / 甲第19833号 / 地博第189号 / 新制||地||66(附属図書館) / 32869 / 京都大学大学院アジア・アフリカ地域研究研究科東南アジア地域研究専攻 / (主査)教授 竹田 晋也, 教授 岩田 明久, 准教授 古澤 拓郎, 教授 神﨑 護 / 学位規則第4条第1項該当 / Doctor of Area Studies / Kyoto University / DGAM

Monitoring carbon stock has emerged as a critical environmental problem among several worldwide organizations and collaborations in the context of global warming and climate change. This study seeks to provide a remote sensing solution based on three types of data, to explore the feasibility and reliability of estimating aboveground biomass (AGB) in order to improve the efficiency of monitoring carbon stock. The study attempted to investigate the potential of using Google Earth Engine (GEE), and the combinations of different datasets from Sentinel-1 (SAR), Sentinel-2 multispectral imagery, and LiDAR data to estimate AGB, by using the random forest algorithm (RF). Two models were proposed: the first one (Model 1) detected the AGB temporal changes from 2016 to 2021 in Southern Sweden; while the second one (Model 2) focused on Hultsfred municipality and studied the influence of different variables including the canopy height. Besides, six experimental groups of variables were tested to determine the performance of using different types of remote sensing data. We validated these two models with the observed AGB, and the findings showed that the combination of SAR polarization, multisprectral bands, vegetation indices able to estimate AGB for Model 1. In addition, Model 2 showed that further using the canopy height data can further improve the estimation.  We also found out that the spectral bands from Sentinel-2 contributed the most to AGB estimation for Model 1 in terms of: bands B3 (Green), B4 (Red), B5 (Red edge), B11 (SWIR), B12 (SWIR); and, vegetation indices of RVI, DVI, and EVI. On the other hand, for Model 2, B1(Ultra blue), B4 (Red), EVI, SAVI, and the canopy height are the most crucial variables for estimating AGB. Besides, the radar backscatter values using VV and VH modes from Sentienl-1 were both important for Models 1 and 2. For Model 1, the experimental group with the best accuracy was the group that used all variable combinations from Sentinel-1 and 2, and its   was 0.33~0.74. For Model 2, the group that used all the variables, in addition to the canopy height performed the best, where its   is 0.91. These therefore showed the benefit of integrating different remote sensing data sources.  In conclusion, this study showed the potential of using RF and GEE to estimate AGB in Southern Sweden. Furthermore, this study also shows the possibility of handling large dataset for a large scale area, at the resolution of 10 m, and producing time series AGB maps from 2016 to 2021. This can help enhance our understanding of AGB temporal changes and carbon stock detection in Southern Sweden, that can provide valuable insights for forest management and carbon monitoring.

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The importance of bottomland hardwood (BLH) forests to support the economy through timber production and carbon sequestration is acknowledged; however, their full potential is yet to be explored. This study developed variable density yield models for BLH oak-gum-cypress forests along the US Gulf Coast and Lower Mississippi River Delta. The models, with an adjusted R2 of 98% for cubic foot growing stock volume and 77% for Doyle board foot sawlog volume, are expected to be valuable tools for landowners and managers seeking to make informed decisions about BLH forest management. A carbon stock model was also developed, and carbon sequestration was explored based on basal area increment. The results showed potential for carbon sequestration with an average carbon stock of 30.56 tons/acre and a maximum average discounted present value of carbon accumulation of $15.94/ton/acre/year. This provides valuable information to managers and landowners willing to participate in carbon credit markets.

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