Influences of forest decline on various properties of soils at Mt. Hirugatake of Tanzawa Mountains, Kanto District, Japan

Thesis (Ph. D. in Agriculture)--University of Tsukuba, (A), no. 3086, 2003.3.25Includes bibliographical reference

Toward an Operational Bare Soil Moisture Mapping Using TerraSAR-X Data Acquired Over Agricultural Areas

International audienceTerraSAR-X data are processed for an "operational" mapping of bare soils moisture in agricultural areas. Empirical relationships between TerraSAR-X signal and soil moisture were established and validated over different North European agricultural study sites. The results show that the mean error on the soil moisture estimation is less than 4% regardless of the TerraSAR-X configuration (incidence angle, polarization) and the soil surface characteristics (soil surface roughness, soil composition). Furthermore, the potential of TerraSAR-X data (signal, texture features) to discriminate bare soils from other land cover classes in an agricultural watershed was evaluated. The mean signal backscattered from bare soils can be easily differentiated from signals from other land cover classes when the neighboring plots are covered by fully developed crops. This was observed regardless of the TerraSAR-X configuration and the soil moisture conditions. When neighboring plots are covered by early growth crops, a TerraSAR-X image acquired under wet conditions can be useful for discriminating bare soils. Bare soil masks were calculated by object-oriented classifications ofmono-configuration TerraSAR-Xdata. The overall accuracies of the bare soils mapping were higher than 84% for validation based on object and pixel. The bare soils mapping method and the soil moisture relationships were applied to TerraSAR-X images to generate soil moisture maps. The results show that TerraSAR-X sensors provide useful data for monitoring the spatial variations of soil moisture at the within-plot scale. The methods of bare soils moisture mapping developed in this paper can be used in operational applications in agriculture, and hydrology

Ecological niche partitioning between Anopheles gambiae molecular forms in Cameroon: the ecological side of speciation

<p>Abstract</p> <p>Background</p> <p>Speciation among members of the <it>Anopheles gambiae </it>complex is thought to be promoted by disruptive selection and ecological divergence acting on sets of adaptation genes protected from recombination by polymorphic paracentric chromosomal inversions. However, shared chromosomal polymorphisms between the M and S molecular forms of <it>An. gambiae </it>and insufficient information about their relationship with ecological divergence challenge this view. We used Geographic Information Systems, Ecological Niche Factor Analysis, and Bayesian multilocus genetic clustering to explore the nature and extent of ecological and chromosomal differentiation of M and S across all the biogeographic domains of Cameroon in Central Africa, in order to understand the role of chromosomal arrangements in ecological specialisation within and among molecular forms.</p> <p>Results</p> <p>Species distribution modelling with presence-only data revealed differences in the ecological niche of both molecular forms and the sibling species, <it>An. arabiensis</it>. The fundamental environmental envelope of the two molecular forms, however, overlapped to a large extent in the rainforest, where they occurred in sympatry. The S form had the greatest niche breadth of all three taxa, whereas <it>An. arabiensis </it>and the M form had the smallest niche overlap. Correspondence analysis of M and S karyotypes confirmed that molecular forms shared similar combinations of chromosomal inversion arrangements in response to the eco-climatic gradient defining the main biogeographic domains occurring across Cameroon. Savanna karyotypes of M and S, however, segregated along the smaller-scale environmental gradient defined by the second ordination axis. Population structure analysis identified three chromosomal clusters, each containing a mixture of M and S specimens. In both M and S, alternative karyotypes were segregating in contrasted environments, in agreement with a strong ecological adaptive value of chromosomal inversions.</p> <p>Conclusion</p> <p>Our data suggest that inversions on the second chromosome of <it>An. gambiae </it>are not causal to the evolution of reproductive isolation between the M and S forms. Rather, they are involved in ecological specialization to a similar extent in both genetic backgrounds, and most probably predated lineage splitting between molecular forms. However, because chromosome-2 inversions promote ecological divergence, resulting in spatial and/or temporal isolation between ecotypes, they might favour mutations in other ecologically significant genes to accumulate in unlinked chromosomal regions. When such mutations occur in portions of the genome where recombination is suppressed, such as the pericentromeric regions known as speciation islands in <it>An. gambiae</it>, they would contribute further to the development of reproductive isolation.</p

Living at the edge: biogeographic patterns of habitat segregation conform to speciation by niche expansion in Anopheles gambiae

<p>Abstract</p> <p>Background</p> <p>Ongoing lineage splitting within the African malaria mosquito <it>Anopheles gambiae </it>is compatible with ecological speciation, the evolution of reproductive isolation by divergent natural selection acting on two populations exploiting alternative resources. Divergence between two molecular forms (M and S) identified by fixed differences in rDNA, and characterized by marked, although incomplete, reproductive isolation is occurring in West and Central Africa. To elucidate the role that ecology and geography play in speciation, we carried out a countrywide analysis of <it>An. gambiae </it>M and S habitat requirements, and that of their chromosomal variants, across Burkina Faso.</p> <p>Results</p> <p>Maps of relative abundance by geostatistical interpolators produced a distinct pattern of distribution: the M-form dominated in the northernmost arid zones, the S-form in the more humid southern regions. Maps of habitat suitability, quantified by Ecological Niche Factor Analysis based on 15 eco-geographical variables revealed less contrast among forms. M was peculiar as it occurred proportionally more in habitat of marginal quality. Measures of ecological niche breadth and overlap confirmed the mismatch between the fundamental and realized patterns of habitat occupation: forms segregated more than expected from the extent of divergence of their environmental envelope – a signature of niche expansion. Classification of chromosomal arm 2R karyotypes by multilocus genetic clustering identified two clusters loosely corresponding to molecular forms, with 'mismatches' representing admixed individuals due to shared ancestral polymorphism and/or residual hybridization. In multivariate ordination space, these karyotypes plotted in habitat of more marginal quality compared to non-admixed, 'typical', karyotypes. The distribution of 'typical' karyotypes along the main eco-climatic gradient followed a consistent pattern within and between forms, indicating an adaptive role of inversions at this geographical scale.</p> <p>Conclusion</p> <p>Ecological segregation between M and S is consistent with niche expansion into marginal habitats by chromosomal inversion variants during early lineage divergence; presumably, this process is promoted by inter-karyotype competition in the higher-quality core habitat. We propose that the appearance of favourable allelic combinations in other regions of suppressed recombination (e.g. pericentromeric portions defining speciation islands in <it>An. gambiae</it>) fosters development of reproductive isolation to protect linkage between separate chromosomal regions.</p

Multispectral Satellite Image Processing

International audienc

A Two-Branch CNN Architecture for Land Cover Classification of PAN and MS Imagery

International audienceThe use of Very High Spatial Resolution (VHSR) imagery in remote sensing applications is nowadays a current practice whenever fine-scale monitoring of the earth’s surface is concerned. VHSR Land Cover classification, in particular, is currently a well-established tool to support decisions in several domains, including urban monitoring, agriculture, biodiversity, and environmental assessment. Additionally, land cover classification can be employed to annotate VHSR imagery with the aim of retrieving spatial statistics or areas with similar land cover. Modern VHSR sensors provide data at multiple spatial and spectral resolutions, most commonly as a couple of a higher-resolution single-band panchromatic (PAN) and a coarser multispectral (MS) imagery. In the typical land cover classification workflow, the multi-resolution input is preprocessed to generate a single multispectral image at the highest resolution available by means of a pan-sharpening process. Recently, deep learning approaches have shown the advantages of avoiding data preprocessing by letting machine learning algorithms automatically transform input data to best fit the classification task. Following this rationale, we here propose a new deep learning architecture to jointly use PAN and MS imagery for a direct classification without any prior image sharpening or resampling process. Our method, namely MultiResoLCC , consists of a two-branch end-to-end network which extracts features from each source at their native resolution and lately combine them to perform land cover classification at the PAN resolution. Experiments are carried out on two real-world scenarios over large areas with contrasted land cover characteristics. The experimental results underline the quality of our method while the characteristics of the proposed scenarios underline the applicability and the generality of our strategy in operational settings