Natural and anthropogenic influences on a red-crowned crane habitat in the Yellow River Delta Natural Reserve, 1992–2008

This study aims to assess the relative importance of natural and anthropogenic variables on the change of the red-crowned crane habitat in the Yellow River Nature Reserve, East China using multitempopral remote sensing and geographic information system. Satellite images were used to detect the change in potential crane habitat, from which suitable crane habitat was determined by excluding fragmented habitat. In this study, a principal component analysis (PCA) with seven variables (channel flow, rainfall, temperature, sediment discharge, number of oil wells, total length of roads, and area of settlements) and linear regression analyses of potential and suitable habitat against the retained principal components were applied to explore the influences of natural and anthropogenic factors on the change of the red-crowned crane habitat. The experimental results indicate that suitable habitat decreased by 5,935 ha despite an increase of 1,409 ha in potential habitat from 1992 to 2008. The area of crane habitat changed caused by natural drivers such as progressive succession, retrogressive succession, and physical fragmentation is almost the same as that caused by anthropogenic forces such as land use change and behavioral fragmentation. The PCA and regression analyses revealed that natural factors (e.g., channel flow, rainfall, temperature, and sediment discharge) play an important role in the crane potential habitat change and human disturbances (e.g., oil wells, roads, and settlements) jointly explain 51.8 % of the variations in suitable habitat area, higher than 48.2 % contributed by natural factors. Thus, it is vital to reduce anthropogenic influences within the reserve in order to reverse the decline in the suitable crane habitat.     

Development of tiger habitat suitability model using geospatial tools—a case study in Achankmar Wildlife Sanctuary (AMWLS), Chhattisgarh India

Geospatial tools supported by ancillary geo-database and extensive fieldwork regarding the distribution of tiger and its prey in Anchankmar Wildlife Sanctuary (AMWLS) were used to build a tiger habitat suitability model. This consists of a quantitative geographical information system (GIS) based approach using field parameters and spatial thematic information. The estimates of tiger sightings, its prey sighting and predicted distribution with the assistance of contextual environmental data including terrain, road network, settlement and drainage surfaces were used to develop the model. Eight variables in the dataset viz., forest cover type, forest cover density, slope, aspect, altitude, and distance from road, settlement and drainage were seen as suitable proxies and were used as independent variables in the analysis. Principal component analysis and binomial multiple logistic regression were used for statistical treatments of collected habitat parameters from field and independent variables respectively. The assessment showed a strong expert agreement between the predicted and observed suitable areas. A combination of the generated information and published literature was also used while building a habitat suitability map for the tiger. The modeling approach has taken the habitat preference parameters of the tiger and potential distribution of prey species into account. For assessing the potential distribution of prey species, independent suitability models were developed and validated with the ground truth. It is envisaged that inclusion of the prey distribution probability strengthens the model when a key species is under question. The results of the analysis indicate that tiger occur throughout the sanctuary. The results have been found to be an important input as baseline information for population modeling and natural resource management in the wildlife sanctuary. The development and application of similar models can help in better management of the protected areas of national interest.     

Relationship Between Bird Abundances and Landscape Characteristics: The Influence of Scale

Scale is important to consider when investigating effects of the environment on a species. Breeding Bird Survey (BBS) data and landscape metrics derived from aerial photographs were evaluated to determine how relationships of bird abundances with landscape variables changed over a continuous range of 16 spatial scales. We analyzed the average number of birds per stop (1985–1994) for five songbird species (family Cardinalidae) for each of 50 stops on 198 BBS transects throughout six states in the Central Plains, USA. Land along each transect was categorized into six cover types, and landscape metrics of fractal dimension (a measure of shape complexity of habitat patches), edge density, patch density, and percent area were calculated, with principal components used to construct composite environmental variables. Associations of bird abundances and landscape variables changed in accordance with small scale changes. Abundances of three species were correlated with edge density and one with component I, which subsumes initial variables of patch density for urban, closed forest, open forest, and open country. Fractal dimension and component II (summarizing amount of closed forest versus open country) were associated with the most species. Correlation patterns of fractal dimension with northern cardinal (Cardinalis cardinalis) and painted bunting (Passerina ciris) abundances were similar, with highest correlations at intermediate to small scales, suggesting indirectly that these species thrive in areas where local habitat conditions are most important. Multiscale analysis can provide insight into the spatial scale(s) at which species respond, a topic of intrinsic scientific interest with applied implications for researchers establishing protocols to assess and monitor avian populations.     

Agriculture and elevation are the main factors for Pampasic stream habitat and water quality

Streams of the Pampasic plain in Southeastern South America are ecosystems affected by both water pollution and habitat alteration mainly due to agricultural activity. Water quality is influenced by the quality of habitats and both depend on land use and watershed morphology. The objective of this study was to determine the relationship between the variables of four factors: (1) the morphology of the watershed, (2) land use in the watershed, (3) river habitat, and (4) water quality of wadeable streams in Uruguay, as well as to determine the most representative variables to quantify such factors. We studied 28 watersheds grouped into three ecoregions and four principal activities, which generated seven zones with three to five streams each. Correlations between the variables of each factor allowed reducing the total number of variables from 57 to 32 to perform principal component analyses (PCA) by factor, reducing the number of variables to 18 for a general PCA. The first component was associated with water quality and elevation. The second was associated with the stream and watershed size, the third with habitat quality, and the fourth to the use of neighboring soils and objects in the channel. Our results indicate that agricultural intensity and elevation are the main factors associated with the habitat and water quality of these lowland streams. These factors must be especially considered in the development of water quality monitoring programs.     

Landscape Correlates of Breeding Bird Richness Across the United States Mid-Atlantic Region

Using a new set of landscape indicator data generated by the U.S.EPA, and a comprehensive breeding bird database from the National Breeding Bird Survey, we evaluated associations between breeding bird richness and landscape characteristics across the entire mid-Atlantic region of the United States. We evaluated how these relationships varied among different groupings (guilds) of birds based on functional, structural, and compositional aspects of individual species demographics. Forest edge was by far the most important landscape attribute affecting the richness of the lumped specialist and generalist guilds; specialist species richness was negatively associated with forest edge and generalist richness was positively associated with forest edge. Landscape variables (indicators) explained a greater proportion of specialist species richness than the generalist guild (46% and 31%, respectively). The lower value in generalists may reflect finer-scale distributions of open habitat that go undetected by the Landsat satellite, open habitats created by roads (the areas from which breeding bird data are obtained), and the lumping of a wide variety of species into the generalist category. A further breakdown of species into 16 guilds showed considerable variation in the response of breeding birds to landscape conditions; forest obligate species had the strongest association with landscape indicators measured in this study (55% of the total variation explained) and forest generalists and open ground nesters the lowest (17% of the total variation explained). The variable response of guild species richness to landscape pattern suggests that one must consider species' demographics when assessing the consequences of landscape change on breeding birds.     

Towards the harmonization between National Forest Inventory and Forest Condition Monitoring. Consistency of plot allocation and effect of tree selection methods on sample statistics in Italy

In the frame of a process aiming at harmonizing National Forest Inventory (NFI) and ICP Forests Level I Forest Condition Monitoring (FCM) in Italy, we investigated (a) the long-term consistency between FCM sample points (a subsample of the first NFI, 1985, NFI_1) and recent forest area estimates (after the second NFI, 2005, NFI_2) and (b) the effect of tree selection method (tree-based or plot-based) on sample composition and defoliation statistics. The two investigations were carried out on 261 and 252 FCM sites, respectively. Results show that some individual forest categories (larch and stone pine, Norway spruce, other coniferous, beech, temperate oaks and cork oak forests) are over-represented and others (hornbeam and hophornbeam, other deciduous broadleaved and holm oak forests) are under-represented in the FCM sample. This is probably due to a change in forest cover, which has increased by 1,559,200 ha from 1985 to 2005. In case of shift from a tree-based to a plot-based selection method, 3,130 (46.7 %) of the original 6,703 sample trees will be abandoned, and 1,473 new trees will be selected. The balance between exclusion of former sample trees and inclusion of new ones will be particularly unfavourable for conifers (with only 16.4 % of excluded trees replaced by new ones) and less for deciduous broadleaves (with 63.5 % of excluded trees replaced). The total number of tree species surveyed will not be impacted, while the number of trees per species will, and the resulting (plot-based) sample composition will have a much larger frequency of deciduous broadleaved trees. The newly selected trees have—in general—smaller diameter at breast height (DBH) and defoliation scores. Given the larger rate of turnover, the deciduous broadleaved part of the sample will be more impacted. Our results suggest that both a revision of FCM network to account for forest area change and a plot-based approach to permit statistical inference and avoid bias in the tree sample composition in terms of DBH (and likely age and structure) are desirable in Italy. As the adoption of a plot-based approach will keep a large share of the trees formerly selected, direct tree-by-tree comparison will remain possible, thus limiting the impact on the time series comparability. In addition, the plot-based design will favour the integration with NFI_2.     

Comparison of remote sensing change detection techniques for assessing hurricane damage to forests

This study compared performance of four change detection algorithms with six vegetation indices derived from pre- and post-Katrina Landsat Thematic Mapper (TM) imagery and a composite of the TM bands 4, 5, and 3 in order to select an optimal remote sensing technique for identifying forestlands disturbed by Hurricane Katrina. The algorithms included univariate image differencing (UID), selective principal component analysis (PCA), change vector analysis (CVA), and postclassification comparison (PCC). The indices consisted of near-infrared to red ratios, normalized difference vegetation index, Tasseled Cap index of greenness, brightness, and wetness (TCW), and soil-adjusted vegetation index. In addition to the satellite imagery, the “ground truth” data of forest damage were also collected through field investigation and interpretation of post-Katrina aerial photos. Disturbed forests were identified by classifying the composite and the continuous change imagery with the supervised classification method. Results showed that the change detection techniques exerted apparent influence on detection results with an overall accuracy varying between 51% and 86% and a kappa statistics ranging from 0.02 to 0.72. Detected areas of disturbed forestlands were noticeable in two groups: 180,832–264,617 and 85,861–124,205 ha. The landscape of disturbed forests also displayed two unique patterns, depending upon the area group. The PCC algorithm along with the composite image contributed the highest accuracy and lowest error (0.5%) in estimating areas of disturbed forestlands. Both UID and CVA performed similarly, but caution should be taken when using selective PCA in detecting hurricane disturbance to forests. Among the six indices, TCW outperformed the other indices owing to its maximum sensitivity to forest modification. This study suggested that compared with the detection algorithms, proper selection of vegetation indices was more critical for obtaining satisfactory results.     

A half-century analysis of landscape dynamics in southern Québec,Canada

We studied landscape dynamics for three time periods (<1950, 1965, and 1997) along a gradient of agricultural intensity from highly intensive agriculture to forested areas in southern Québec. Air photos were analyzed to obtain long-term information on land cover (crop and habitat types) and linear habitats (hedgerows and riparian habitats) and landscape metrics were calculated to quantify changes in habitat configuration. Anthropogenic areas increased in all types of landscapes but mostly occurred in the highly disturbed cash crop dominated landscape. Perennial crops (pasture and hayfields) were largely converted into annual crops (corn and soybean) between 1965 and 1997. The coalescence of annual crop fields resulted in a more homogeneous agricultural landscape. Old fields and forest cover was consistently low and forest fragmentation remained stable through time in the intensive agriculture landscapes. However, forest cover increased and forest fragmentation receded in the forest-dominated landscapes following farm abandonment and the transition of old fields into forests. Tree-dominated hedgerows and riparian habitats increased in areas with intensive agriculture. Observed changes in land cover classes are related to proximate factors, such as surficial deposits and topography. Agriculture intensification occurred in areas highly suitable for agriculture whereas farm abandonment was observed in poor-quality agriculture terrains. Large-scale conversion of perennial crops into annual crops along with continued urbanization exerts strong pressures on residual natural habitats and their inhabiting wildlife. The afforestation process occurring in the more forested landscapes along with the addition of tree-dominated hedgerows and riparian habitats in the agriculture-dominated landscapes should improve landscape ecological value.     

A habitat-based microscale forest classification system for zoning wood production areas to conserve a rare species threatened by logging operations in south-eastern Australia

The production of timber from native forests is presently one of the most controversial land management issues in Australia. Part of this controversy results from the potential impacts of forestry practices on forest-dependent fauna, particularly those that are rare and endangered, such as Leadbeater's Possum Gymnobelideus leadbeateri McCoy, in the forests of central Victoria, south-eastern Australia. A significant proportion of the highly limited distribution of this species overlaps with some of the most valuable wood production forests in Australia within which extensive clearfelling operations are employed to produce timber and pulpwood. These operations can destroy the habitat of G. leadbeateri. The Victoria government agency that is responsible for forest and wildlife management has devised a forest zoning system as part of the management strategies to conserve G. leadbeateri within timber production areas. This is designed to partition the forest into three types of areas: (1) where the conservation of G. leadbeateri is a priority, (2) where wood production is a priority, and, (3) where both land uses are a joint priority. The classification of areas of forest where the conservation of G. leadbeateri is the primary land use is based on an understanding of the habitat requirements of the species. The results of recent field studies, where statistical models of the habitat requirements of G. leadbeateri have been developed and their performance subsequently tested using a new dataset, highlights the need for a new basis to guide the classification of areas for the conservation of the species within wood production forests. We describe a method for devising a forest management zoning system that is based on a statistical model of the habitat requirements of G. leadbeateri and which will better integrate wood production and the conservation of the species. This procedure accounts for the uncertainty in the statistical model and, in turn, reduces the risk that areas where G. leadbeateri occurs are logged, whilst ensuring that other areas are not unnecessarily excluded from timber harvesting.     

Analyzing riparian forest cover changes along the Firniz River in the Mediterranean City of Kahramanmaras in Turkey

Riparian forests adjacent to surface water are important transitional zones which maintain and enrich biodiversity and ensure the sustainability in a forest ecosystem. Also, riparian forests maintain water quality, reduce sediment delivery, enhance habitat areas for aquatic life and wildlife, and provide ecological corridors between the upland and the downstream. However, the riparian ecosystems have been degraded mainly due to human development, forest operations, and agricultural activities. In order to evaluate the impacts of these factors on riparian forests, it is necessary to estimate trends in forest cover changes. This study aims to analyze riparian forest cover changes along the Firniz River located in Mediterranean city of Kahramanmaras in Turkey. Changes in riparian forest cover from 1989 to 2010 have been determined by implementing supervised classification method on a series of Landsat TM imagery of the study area. The results indicated that the classification process applied on 1989 and 2010 images provided overall accuracy of 80.08 and 75 %, respectively. It was found that the most common land use class within the riparian zone was productive forest, followed by degraded forest, agricultural areas, and other land use classes. The results also indicated that the areas of degraded forest and forest openings increased, while productive forest and agricultural areas decreased between the years of 1989 and 2010. The amount of agricultural areas decreased due to the reduction in the population of rural people. According to these results, it can be concluded that special forest management and operation techniques should be implemented to restore the forest ecosystem in riparian areas.     

Accounting for Active Management and Risk Attitude in Forest Sector Models

Given the importance of anthropogenic determinants in forest ecosystems within Europe, the objective of this paper is to link the evidence arising from biological models to socio-economic determinants, where the expected returns of forest investments represent the main driver. A micro-economic area allocation module is therefore coupled with an inventory-based forest dynamics module and a partial-equilibrium market module in a national-level forest sector model for France (FFSM++). Running long-term scenarios (until 2100), we show the implication of an active management policy on forest composition: when the most profitable option drives forest investments, coniferous forests are generally preferred over broadleaved ones. This result is, however, reappraised when the risk aversion of forest owners is explicitly considered in the model, given the higher risk associated with the former. We further show the strong stability of forest ecosystems that, due to the very long cycles, undergoes very small variations in volume stocks, even in scenarios where the initial forest regeneration is strongly influenced.     

Development of a Grassland Integrity Index Based on Breeding Bird Assemblages

We utilized landscape and breeding bird assemblage data from three Breeding Bird Survey (BBS) routes sampled from 1965–1995 to develop and test a grassland integrity index (GII) in a mixed-grass prairie area of Oklahoma. The overall study region is extensively fragmented from long-term agricultural activity, and native habitat remnants have been degraded by recent encroachment of woody vegetation, namely eastern redcedar (Juniperus virginiana L.). The 50 individual bird survey points along the BBS routes, known as stops, were used as sample sites. Our process first focused on developing a grassland disturbance index (GDI) as a measure of cumulative landscape disturbances for these sites. The GDI was based on five key landscape variables identified in an earlier species-level study of long-term avian community dynamics: total tree, shrub, and herbaceous vegetation cover indices, overall mean landscape patch size, and grassland patch core size. The GII was then developed based on breeding bird assemblage data. Assemblages were based on commonly used response guilds reflective of five avian life history parameters: foraging mode/location, nesting location, habitat specificity, migratory pattern, and dietary guild. We tested the response of 78 candidate assemblage metrics to the GDI, and eliminated those with no or poor response or with high correlations (redundant), resulting in 13 metrics for use in the final index. Individual metric scores were scaled to fall between 0 and 10, and the cumulative index to range from 0 to 100. Although broader application and refinement are possible, the avian-based GII has an advantage over labor-intensive, habitat-based monitoring in that the GII is derived from readily available long-term BBS data. Therefore, the GII shows promise as an inexpensive tool that could easily be applied over other areas to monitor changes in regional grassland conditions.     

Microbiological and physical–chemical water quality of the rice fields in Sinos River’s basin, Southern Brazil

In this study, we evaluate spatial and temporal variations of the water’s quality used in rice fields. Every 15 days during the different phases of cultivation of the rice—vegetative, reproductive, and maturity—samples were collected from the main irrigation channel, from the surface water in the field, and from the excess-water drainage channel. The differences in the values of 13 variables were analyzed by the analysis of variance system and by the Principal Component Analysis (PCA) technique. The results demonstrate that the values observed for the presence of total coliforms, heat-tolerant coliforms, and pH were higher in the irrigation water and that the calcium and magnesium components were greater in the drainage water. The PCA results indicate that three groups of variance exist and that these three account for 77 % of the observed variances. The first principal component, (39 % of the variances), includes the variables pH, phosphorus, potassium, carbon, and turbidity; the second (28.1 %), calcium, magnesium, and conductivity; while the third accounts for only 9.9 % of the variation and incorporates the variable biological thermotolerant coliforms. The spatial pattern resulting from the distribution of the sampling locations as regards the first two principal components indicates a difference between the irrigation and drainage waters. The variables of the first and third items (except for the turbidity in the second component) reach higher values in the irrigation water, while the variables associated with the second component have higher values in the drainage water. The information provided is important for the analysis of the influence exercised by plantation management decisions on the microbiological, physical, and chemical quality of the water. The results confirm the ability of paddy rice field to filter out some of the chemicals and coliforms from the irrigation water as it passes through the agro ecosystem.     

Geospatial Analysis of Climatic and Geomorphic Interactions Influencing Stream Discharge, Appalachian Mountains, USA

Streamflow values are commonly synthesized for locations where flow measurement stations are lacking or where only intermittent measurements are available. In an Appalachian Mountains dataset comprised of 29 watersheds, the most appropriate among geomorphic, geologic, and hydrogeologic datasets were selected for use in prediction of streamflow at watershed scale. A statistical model was developed using principal components analysis (PCA) and cluster analysis (CA) for. Using CA on variables derived from the PCA, an optimum set of variables was derived for predicting streamflow. Results indicate there are two categories of watersheds in the study area. The first is strongly correlated with climatic variables (precipitation, temperature, elevation, and groundwater recharge). The second is strongly correlated with two geomorphic variables (watershed slope and percentage of forested area). The spatial distribution of cluster classifications shows that watersheds dominated by the climatic component are located along the Allegheny Front while watersheds dominated by the geomorphic component are located in the Allegheny Plateau and Valley and Ridge physiographic provinces. These variations between the Allegheny Plateau and Valley and Ridge physiographic provinces suggest that, to accurately model streamflow, modeling needs be done based on natural physiographic boundaries rather than political boundaries. In this physiographic setting, elevation seems to be a major control.     

Influence of Meteorological Factors NO2, SO2, CO and PM10 on the Concentration of O3 in the Urban Atmosphere of Eastern Croatia

Ozone, NO₂, SO₂, CO, PM₁₀ and meteorological parameters were measured simultaneously during the summer?Cautumn season 2007 in Osijek??the eastern, flat, agricultural part of Croatia. Fourier analysis confirms the existence of variation in ozone volume fractions with periods ranging from the usual semi-daily and daily to 7 and 28 daily cycles. The relationships between O₃ and other variables were modelled in three ways: principal component analysis, multiple linear regression and principal component regression. The results of the principal component analysis detected underlying relationships among ozone concentrations and meteorological variables. An extremely simple meteorological model is suitable for the prediction of ozone levels. The meteorological factors, temperature and cloudiness played a main role in the MLR model (R ²?=?0.83). The application of the principal component regression approach confirmed that the original variables associated with the valid principal components were meteorological variables (R ²?=?0.82).     

Spatial pattern analysis in Persian oak (Quercus brantii var. persica) forests on B&W aerial photographs

The purpose of this investigation was to develop a method to determine the spatial pattern of trees as a robust indicator to monitor changes from B&W aerial photographs in Persian oak forests of Zagros, Iran. A 500 x 600 m study area was selected in Servak forests next to Yasuj city in Kohgiluyeh-Va-BuyerAhmad Province. All the trees were tagged in the study area and the point map of stems were prepared. The spatial distribution of trees was determined as "dispersed" using nearest neighbour technique. Then the index of "C" calculated by T-square sampling method was applied to the point map of the study area in 30 systematic sample points in a 100 x 100 m network. Comparing the results of this method with the true spatial pattern of the study area showed that "C" can detect the spatial arrangement of trees. Thereafter the index was used on the air photo of the study area that was made of B&W aerial photographs. The method suggested in this study provides a suitable approach for detecting the spatial pattern of trees in Zagros forests on B&W air photos.     

Evaluation of Carbon Uptake and Emissions by Forests in Korea During the Last Thirty Years (1973–2002)

The contribution of Korean forests to carbon sequestration for anthropogenic carbon emissions was evaluated. In addition, monitoring of carbon species released from forest fires was conducted. Despite a high carbon uptake by Korean forests, a tremendous increase in fossil fuel burning resulted in a small contribution by forests to carbon removal. The removal efficiency had a 5–31% range with an average of 12% during the period 1973–2002. In 2000, the amount of carbon released from burned trees corresponded to 1.6% of carbon uptake by forests. The distribution of surface CO concentration (ppb) derived from MOPITT (Measurement of Pollution in the Troposphere) showed high CO levels over the East/Japan Sea on April 10, 2000 when the largest forest fires occurred along the east coast of Korea. Trajectory analysis and ground CO measurements also indicated that CO levels over the East/Japan Sea were influenced by forest fires. This study suggests that continuous monitoring of carbon emissions from forest fires is needed for a more reliable estimate of carbon flux in the environment.     

Spatial and temporal dynamics of land use pattern in Eastern Turkey: a case study in Gümüşhane

Recognition and understanding of landscape dynamics as a historical legacy of disturbances are necessary for sustainable management of forest ecosystems. This study analyzed spatial and temporal changes in land use and land cover patterns in a typical mountain watershed in the Gumushane district along the Northeastern part of Turkey. The area is investigated by comparing LANDSAT images from 1987 to 2000 and evaluated the temporal changes of spatial structure of forest conditions through spatial analysis of forest cover type maps from 1971 and 1987 using GIS and FRAGSTATS™. The results show a general decreasing trend in area of natural land cover types including broadleaf and conifer forests as well as coppice between 1971 and 1987 (0.54%, respectively). In contrast, between 1987 and 2000 this natural land cover types show increasing trend (1.6%). In parallel with forest dynamics, the area of managed land including lowland and upland agricultural areas, rangelands and grasslands increased during the first period and decreased during second period. In terms of spatial configuration, Gümüşhane forests aren’t generally fragmented by intensive forest utilization in the latter periods. This is partially due to out-migration of rural population in Gümüşhane. Nevertheless, land use pattern significantly changed over time depending on a few factors such as unregulated management actions, social pressure and demographic movements. The study revealed that demographic movements have a major effect on landscape dynamics.     

Can tree species diversity be assessed with Landsat data in a temperate forest?

The diversity of forest trees as an indicator of ecosystem health can be assessed using the spectral characteristics of plant communities through remote sensing data. The objectives of this study were to investigate alpha and beta tree diversity using Landsat data for six dates in the Gönen dam watershed of Turkey. We used richness and the Shannon and Simpson diversity indices to calculate tree alpha diversity. We also represented the relationship between beta diversity and remotely sensed data using species composition similarity and spectral distance similarity of sampling plots via quantile regression. A total of 99 sampling units, each 20 m × 20 m, were selected using geographically stratified random sampling method. Within each plot, the tree species were identified, and all of the trees with a diameter at breast height (dbh) larger than 7 cm were measured. Presence/absence and abundance data (tree species number and tree species basal area) of tree species were used to determine the relationship between richness and the Shannon and Simpson diversity indices, which were computed with ground field data, and spectral variables derived (2 × 2 pixels and 3 × 3 pixels) from Landsat 8 OLI data. The Shannon-Weiner index had the highest correlation. For all six dates, NDVI (normalized difference vegetation index) was the spectral variable most strongly correlated with the Shannon index and the tree diversity variables. The Ratio of green to red (VI) was the spectral variable least correlated with the tree diversity variables and the Shannon basal area. In both beta diversity curves, the slope of the OLS regression was low, while in the upper quantile, it was approximately twice the lower quantiles. The Jaccard index is closed to one with little difference in both two beta diversity approaches. This result is due to increasing the similarity between the sampling plots when they are located close to each other. The intercept differences between two investigated beta diversity were strongly related to the development stage of a number of sampling plots in the tree species basal area method. To obtain beta diversity, the tree basal area method indicates better result than the tree species number method at representing similarity of regions which are located close together. In conclusion, NDVI is helpful for estimating the alpha diversity of trees over large areas when the vegetation is at the maximum growing season. Beta diversity could be obtained with the spectral heterogeneity of Landsat data. Future tree diversity studies using remote sensing data should select data sets when vegetation is at the maximum growing season. Also, forest tree diversity investigations can be identified by using higher-resolution remote sensing data such as ESA Sentinel 2 data which is freely available since June 2015.     

基于遥感的区域人居环境指数构建及其应用

基于生态宜居和以人为本双重理念,选取区域绿度、区域热度、区域蓝度、区域亮度、区域透明度和区域起伏度等6个指标,利用主成分分析法构建基于遥感的区域人居环境指数（RHEI）。以福建省为例,分季度测算其RHEI,揭示区域人居环境的时空差异。结果表明：福建省RHEI随着季节变化逐渐降低,呈现东南低西北高、沿海岸线向内陆上升的空间分布格局。通过比较各指标的回归模型系数,发现区域蓝度对区域人居环境的影响最大,区域透明度的影响最小。由年度均值回归模型预测,未来每增加0.166单位的区域蓝度或减少0.278单位的区域热度,能提升0.1单位的人居环境质量。