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This study presents new data on soil organic matter dynamics in relation to two forest types in a strongly seasonal lowland rainforest in Korup National Park, SW Cameroon. Organic matter dynamics at the start of the wet season were investigated by quantifying changes in the amount of the standing crop of surface organic matter and light fraction soil organic matter (LF.SOM) in five replicate plots with low (≤ 15%) basal area ectomycorrhizal trees ≥ 30 cm gbh (LEM forest type), and five with high (≥ 45 %) basal area ectomycorrhizal trees (HEM forest types). Soil samples were collected in the wet season from three soil depths: an organic-enriched layer (0 - 3 cm), 3 - 8 cm and >8 cm layers, in both HEM and LEM forest types, in 1993 (3 harvests), and in 1994 (8 harvests). LF.SOM and heavy fraction soil organic matter (HF.SOM) were separated from < 2 mm whole soil using tap water and/or saturated NaI solution (density 1.65 g cm^-3). Other pools of organic matter quantified included that in the > 2 mm soil fraction and in the < 2 mm sieved whole soil. LF.SOM accounted for 10 - 40 % soil organic matter, 7 - 34 % carbon, 2 - 28 % N and 2 - 25% P of the < 2 mm whole soil across forest types and down the soil profile. The > 2 mm soil fraction accounted for 14 - 21% carbon, 11 - 20 % N, and 6 - 22% P of total soil (i.e. <2 mm whole soil + > 2 mm soil fraction). The amount of LF.SOM was not significantly different between the two forest types at all depths. Both the amount of the standing crop of surface organic matter and LF.SOM declined as the wet season progressed. However, the amount of LF.SOM declined faster (55 %) in the LEM forest than in the HEM forest (22 %). On the other hand, the amount of HF.SOM increased over the same period. A vertical gradient in LF.SOM content was observed.

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No description available.

Satellite imagery such as Landsat has been in use for decades for many landscape and regional scale mapping applications, but has been too coarse for use in detailed forest inventories where stand level structural and compositional information is desired. Recently available high spatial resolution satellite imagery may be well suited to mapping fine-scale components of ecosystems, however, this remains an area of ongoing research. The first goal of this thesis was to assess the capacity of high spatial resolution satellite imagery to detect the variability in late seral coastal temperate rainforests in British Columbia, Canada. Using an object-based classifier, two hierarchical classification schemes are evaluated: a broad classification based on structural (successional) stage and a finer classification of late seral vegetation associations. The finer-scale classification also incorporates ancillary landscape positional variables (elevation and potential soil moisture) derived from Light Detection and Ranging (LiDAR) data, and the relative contribution of spectral, textural and landscape positional data for this classification is determined. Results indicate that late seral forests can be well distinguished from younger forests using QuickBird spectral and textural data. However, discrimination among late seral forest associations is challenging, especially in the absence of landscape positional variables. Classification accuracies were particularly low for rare forest associations. Given this finding, the objective of the third chapter was to explicitly examine the caveats of using high spatial resolution imagery to map rare classes. Classification accuracy is assessed in several different ways in order to examine the impact on perceived map accuracy. In addition, the effects on habitat extent and configuration resulting from post-classification implementation of a minimum mapping unit are examined. Results indicate that classification accuracies may vary considerably depending on the assessment technique used. Specifically, ignoring the presence of fine-scale heterogeneity in a classification during accuracy assessment falsely lowered the accuracy estimates. Further, post-classification smoothing had a large effect on the spatial pattern of rare classes. These findings suggest that routinely used image classification and assessment techniques can greatly impact mapping of rare classes.

Satellite imagery such as Landsat has been in use for decades for many landscape and regional scale mapping applications, but has been too coarse for use in detailed forest inventories where stand level structural and compositional information is desired. Recently available high spatial resolution satellite imagery may be well suited to mapping fine-scale components of ecosystems, however, this remains an area of ongoing research. The first goal of this thesis was to assess the capacity of high spatial resolution satellite imagery to detect the variability in late seral coastal temperate rainforests in British Columbia, Canada. Using an object-based classifier, two hierarchical classification schemes are evaluated: a broad classification based on structural (successional) stage and a finer classification of late seral vegetation associations. The finer-scale classification also incorporates ancillary landscape positional variables (elevation and potential soil moisture) derived from Light Detection and Ranging (LiDAR) data, and the relative contribution of spectral, textural and landscape positional data for this classification is determined. Results indicate that late seral forests can be well distinguished from younger forests using QuickBird spectral and textural data. However, discrimination among late seral forest associations is challenging, especially in the absence of landscape positional variables. Classification accuracies were particularly low for rare forest associations. Given this finding, the objective of the third chapter was to explicitly examine the caveats of using high spatial resolution imagery to map rare classes. Classification accuracy is assessed in several different ways in order to examine the impact on perceived map accuracy. In addition, the effects on habitat extent and configuration resulting from post-classification implementation of a minimum mapping unit are examined. Results indicate that classification accuracies may vary considerably depending on the assessment technique used. Specifically, ignoring the presence of fine-scale heterogeneity in a classification during accuracy assessment falsely lowered the accuracy estimates. Further, post-classification smoothing had a large effect on the spatial pattern of rare classes. These findings suggest that routinely used image classification and assessment techniques can greatly impact mapping of rare classes.

Satellite imagery such as Landsat has been in use for decades for many landscape and regional scale mapping applications, but has been too coarse for use in detailed forest inventories where stand level structural and compositional information is desired. Recently available high spatial resolution satellite imagery may be well suited to mapping fine-scale components of ecosystems, however, this remains an area of ongoing research. The first goal of this thesis was to assess the capacity of high spatial resolution satellite imagery to detect the variability in late seral coastal temperate rainforests in British Columbia, Canada. Using an object-based classifier, two hierarchical classification schemes are evaluated: a broad classification based on structural (successional) stage and a finer classification of late seral vegetation associations. The finer-scale classification also incorporates ancillary landscape positional variables (elevation and potential soil moisture) derived from Light Detection and Ranging (LiDAR) data, and the relative contribution of spectral, textural and landscape positional data for this classification is determined. Results indicate that late seral forests can be well distinguished from younger forests using QuickBird spectral and textural data. However, discrimination among late seral forest associations is challenging, especially in the absence of landscape positional variables. Classification accuracies were particularly low for rare forest associations. Given this finding, the objective of the third chapter was to explicitly examine the caveats of using high spatial resolution imagery to map rare classes. Classification accuracy is assessed in several different ways in order to examine the impact on perceived map accuracy. In addition, the effects on habitat extent and configuration resulting from post-classification implementation of a minimum mapping unit are examined. Results indicate that classification accuracies may vary considerably depending on the assessment technique used. Specifically, ignoring the presence of fine-scale heterogeneity in a classification during accuracy assessment falsely lowered the accuracy estimates. Further, post-classification smoothing had a large effect on the spatial pattern of rare classes. These findings suggest that routinely used image classification and assessment techniques can greatly impact mapping of rare classes. / Forestry, Faculty of / Graduate

京都大学 / 0048 / 新制・課程博士 / 博士(地域研究) / 甲第20734号 / 地博第225号 / 新制||地||83(附属図書館) / 京都大学大学院アジア・アフリカ地域研究研究科アフリカ地域研究専攻 / (主査)教授 木村 大治, 准教授 安岡 宏和, 助教 佐藤 宏樹, 教授 市川 光雄 / 学位規則第4条第1項該当 / Doctor of Area Studies / Kyoto University / DGAM

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The distribution of species in space is determined by the species dispersal capacity, adaptation to environmental conditions, and response to predators and competitors. To determine the importance of dispersal limitation, environmental filtering, and species interactions on the distribution of species in the Brazilian Amazonian forest, I sampled termites in a large area of Brazil. I investigated patterns in species occurrence that could indicate competition and predation structuring termite communities, and analyzed the association of termite abundance and species richness with the density of ant predators. The spatial distribution of termites, and their association with climatic and edaphic conditions were also used to infer about the effects of dispersal limitation and environmental filtering. A total of 271 termite species and 4,389 colonies was found in the 148 transects sampled. Predator density was the strongest predictor of termite abundance and species richness at small spatial scales, but the turnover in termite species composition was mostly associated with measures of soil texture. At broad spatial scales, soil chemistry, climate, and isolation by distance were associated with termite abundance, species richness, and species composition. These results suggest that both species interactions, their association with the environment, and their dispersal capacity determine their distribution. Nevertheless, dispersal limitation seem to be stronger over large areas, whereas environmental filtering can act both at small and large geographic scales.