Mapping tsetse habitat suitability in the common fly belt of Southern Africa using multivariate analysis of climate and remotely sensed vegetation data

Abstract The distribution of Glossina morsitans centralis, Glossina morsitans morsitans and Glossina pallidipes are described in part of southern Africa, using a range of multivariate techniques applied to climate and remotely sensed vegetation data. Linear discriminant analysis is limited in its predictive power by the assumption of common co-variances in the classes within multivariate environment space. Maximum likelihood classification is one of a variety of alternative methods that do not have this constraint, and produce a better prediction, particularly when a priori probabilities of presence and absence are taken into account. The best predictions are obtained when the habitat is subdivided, prior to classification, on the basis of a bimodality detected on the third component axis of a principal component analysis. The results of the predictions were good, particularly for G.m.centralis and G.m.morsitans , which gave overall correct predictions of 92.8% and 85.1 %, with a Kappa index of agreement between the predion and the training data of 0.7305 and 0.641 respectively. For G.pallidipes , 91.7% of predictions were correct but the value of Kappa was only 0.549. Very clear differences are demonstrated between the habitats of the two subspecies Gmxentralis and G.m.morsitans.     

Developing indices of temporal dispersion and continuity to map natural vegetation

An accurate and updated natural vegetation map is imperative for sustainable environmental management. This paper proposed a novel natural vegetation mapping algorithm based on time series images. Several indices of temporal dispersion and continuity were established for this purpose: low density (LD), medium density (MD), high density (HD) and medium continuity (MC). These indices were developed based on the particular percentiles-determined section of the EVI2 temporal profiles obtained through continuous wavelet transform. The natural vegetation was generally characterized as with lower temporal dispersion and greater temporal continuity compared with agricultural crops. The proposed methodology incorporated the indices of temporal dispersion and continuity and was applied to 13 provinces in central East China based on 500 m 8-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index with two bands (EVI2) in 2013. An overall accuracy of 92.97% was obtained when compared with 2715 ground truth sites. There was also a good agreement (kappa index = 0.8049) on the distribution and areas of different vegetation types between the MODIS-estimated image and the Landsat 8 OLI interpreted data on two test regions. This study demonstrated the efficiency of the transform and metric integrated time series classification approaches in the fields of land and vegetation cover mapping.     

Remotely sensed vegetation phenology for describing and predicting the biomes of South Africa

Questions: What are the patterns of remotely sensed vegetation phenology, including their inter‐annual variability, across South Africa? What are the phenological attributes that contribute most to distinguishing the different biomes? How well can the distribution of the recently redefined biomes be predicted based on remotely sensed, phenology and productivity metrics? Location: South Africa. Method: Ten‐day, 1 km, NDVI AVHRR were analysed for the period 1985 to 2000. Phenological metrics such as start, end and length of the growing season and estimates of productivity, based on small and large integral (SI, LI) of NDVI curve, were extracted and long‐term means calculated. A random forest regression tree was run using the metrics as the input variables and the biomes as the dependent variable. A map of the predicted biomes was reproduced and the differentiating importance of each metric assessed. Results: The phenology metrics (e.g. start of growing season) showed a clear relationship with the seasonality of rainfall, i.e. winter and summer growing seasons. The distribution of the productivity metrics, LI and SI were significantly correlated with mean annual precipitation. The regression tree initially split the biomes based on vegetation production and then by the seasonality of growth. A regression tree was used to produce a predicted biome map with a high level of accuracy (73%). Main conclusion: Regression tree analysis based on remotely sensed metrics performed as good as, or better than, previous climate‐based predictors of biome distribution. The results confirm that the remotely sensed metrics capture sufficient functional diversity to classify and map biome level vegetation patterns and function.     

Classification of vegetation in the Western Australian wheatbelt using Landsat MSS data

We examined the use of Landsat multispectral scanner (MSS) data to provide preliminary information on broad vegetation types present within nature reserves in the wheatbelt region of Western Australia. We analysed Landsat data for an area of natural vegetation for which ground survey and aerial photographic data are available. We used canonical variate analysis to examine the degree of spectral separation between training sites selected in the main structural vegetation types. The training classes were then grouped into spectral classes and an allocation procedure used to map the pixels in the reserve into these classes. The analysis provided a good correspondence between spectral classes and broad vegetation types recognised from aerial photography, but did not discriminate between differences in dominant species (e.g. between different types of Eucalypt woodland). The classification derived from the study reserve was then applied successfully to two nearby reserves, indicating that the data can be used to provide initial information on the broad vegetation types present in wheatbelt reserves, although it is not suitable for finer resolution studies.Abbreviation MSS = Multi-spectral scanner     

Probing time series vegetation data for evidence of succession

A subset of Stephens & Waggoner's (1970) data, spanning 40 years of recording at the same site, is analysed by AOC for trends in density fluctuations. The results suggest that the recording period is sufficiently long for trends to be detected. A dominant trend depicts density changes as simple monotone functions of time. Other lesser trends signify cyclic changes of different lengths. The fact that a dominant monotone trend exists is interpreted as evidence of succession.Nomenclature follows Little (1953)     

Univariate analysis of tsetse habitat in the common fly belt of Southern Africa using climate and remotely sensed vegetation data

Abstract Tsetse are vectors of trypanosomes that cause diseases both in humans and livestock. Traditional tsetse surveys, using sampling methods such as Epsilon traps and black screen fly rounds, are often logistically difficult, costly and time-consuming. The distribution of tsetse, as revealed by such survey methods, is strongly influenced by environmental conditions, such as climate and vegetation cover, which may be readily mapped using satellite data. These data may be used to make predictions of the probable distribution of tsetse in unsurveyed areas by determining the environmental characteristics of areas of tsetse presence and absence in surveyed areas. The same methods may also be used to characterize differences between tsetse species and subspecies. In this paper we analyse the distribution of Glossina morsitans centralis, Glossina morsitans morsitans and Glossina pallidipes in southern Africa with respect to single environmental variables. For G.m.centralis the best predictions were made using the average NDVI (75% correct predictions; range > 0.37) and the average of the maximum temperature (70% correct predictions; 27.0–29.2°C). For G.m.morsitans the best prediction was given by the maximum of the minimum temperature (84% correct predictions; range > 18.8°C), and for G.pallidipes , also by the maximum of the minimum temperature (86% correct predictions; range > 19.6 °C). The following paper compares a range of multivariate techniques for making predictions about the distribution of these species in the same region.     

Non‐destructive dry matter estimation of Alhagi sparsifolia vegetation in a desert oasis of Northwest China

Question: Can above‐ground biomass of naturally growing Alhagi sparsifolia shrubs be estimated non‐destructively? Location: Qira oasis (37° 01′N, 80° 48′E, 1365 ma.s.l.) at the southern fringe of the Taklamakan desert, Xinjiang, NW China. Methods: Two methods were compared to estimate above‐ground biomass (AGB) of Alhagi. At first shrub AGB was estimated by manual ground measurements (called ‘allometric approach’) of length, width and height of 50 individuals. Subsequently regression equations were established between calculated shrub canopy volume and shrub AGB (r²= 0.96). These equations were used to calculate AGB from manual ground measurements in 20 sample plots within the Alhagi field. Secondly, kite‐based colour aerial photography coupled with the use of a Geographic Information System (called ‘GIS approach’) was tested. First and second order polynomial regressions between AGB data of the 50 individual shrubs and their respective canopy area allowed to automatically calculate the AGB of all remaining shrubs covered by the photograph (r²= 0.92 to 0.96). The use of non‐linear AGB regression equations required an automatised separation of shrubs growing solitary or in clumps. Separation criteria were the size and shape of shrub canopies. Results: The allometric approach was more reliable but also more time‐consuming than the GIS‐based approach. The latter led to an overestimation of Alhagi dry matter in densely vegetated areas. However, this systematic error decreased with increasing size of the surveyed area. Future research in this field should focus on improvements of AGB estimates in areas of high shrub density.     

Latitudinal pattern of mountain vegetation zonation in southern and eastern Asia

A new scheme of altitudinal and latitudinal vegetation zonation is proposed for eastern Asia. The latitudinal patterns of mountain vegetation zonation show a clear boundary at ca. 20°–30° N. For the tropical mountains south of 20° N, the altitudinal series includes tropical lowland, tropical lower montane, and tropical upper montane zones. For the temperate mountains north of 30° N, the series includes temperate lowland, temperate lower montane, and temperate upper montane zones. The mountains located between 20° and 30° N show a transitional zonation pattern; the lower two zones are comparable to the lower two of the tropical zonation (tropical lowland and tropical lower montane), and the upper two zones are comparable to those of the temperate zonation (temperate lower montane and temperate upper montane). The tropical upper montane zone is not found north of 20°–30° N, while the tropical lower montane zone reaches down to sea level and constitutes the temperate lowland zone. Thus the zonation between 20° and 30° N includes tropical lowland, tropical lower montane/temperate lowland, temperate lower montane, and temperate upper montane zones. The latitudinal series of lowland rain forests follows the scheme of climatic division into tropical, subtropical/warm-temperate, cool-temperate and cold-temperate, with a shift of the respective life forms, evergreen, evergreen notophyllous, deciduous, and evergreen needle-leaved. The tropical lower montane forest can be correlated to the horizontal subtropical/ warm-temperate zone. The temperate altitudinal and latitudinal zonations above 30° N are correlated and show an inclined parallel pattern from high altitudes in the south to low altitudes down to sea level in the north.     

Trend analysis of vegetation dynamics in Qinghai-Tibet Plateau using Hurst Exponent

As one of the most sensitive areas responding to global environmental change, especially global climate change, Qinghai-Tibet Plateau has been recognized as a hotspot for coupled studies on global terrestrial ecosystem change and global climate change. As an important component of terrestrial ecosystems, vegetation dynamic has become one of the key issues in global environmental change, and numerous case studies have been conducted on vegetation dynamic trend in different study periods. However, few are focused on the quantitative analysis of the consistency of vegetation dynamic trends after the study periods. In the study, taking Qinghai-Tibet Plateau as a case, vegetation dynamic trend during 1982-2003 were analyzed, with the application of the method of linear regression analysis. The results showed that, vegetation dynamics in Qinghai-Tibet Plateau experienced a significant increasing as a whole, with nearly 50% forest degradation in the study period. And among the 7 kinds of vegetation types, the change of forest was the most fluctuant with desert the least one. Furthermore, the consistency of vegetation dynamic trends after the study period, was quantified using Hurst Exponent and the method of R/S analysis. The results showed high consistency of future vegetation dynamic trends for the whole plateau, and inconsistent areas were mainly meadow and steppe distributed in the middle or east of the plateau. It was also convinced that, vegetation dynamic trends in the study area were significantly influenced by topography, especially the elevation.     

Computerized classification of Mediterranean vegetation using panchromatic aerial photographs

Abstract. Historical aerial photographs are an important source for data on medium- to long-term (10 - 50 yr) vegetation changes. Older photographs are panchromatic, and manual interpretation has traditionally been used to derive vegetation data from such photographs. We present a method for computerized analysis of panchromatic aerial photographs, which enables one to create high resolution, accurate vegetation maps. Our approach is exemplified using two aerial photographs (from 1964 and 1992) of a test area on Mt. Meron, Israel. Spatial resolution (pixel size) of the geo-rectified photos was 0.30 m and spatial accuracy (RMS error) ca. 1 m. An illumination adjustment prior to classification was found to be essential in reducing misclassification error rates. Two classification approaches were employed: a standard maximum-likelihood supervised classifier, and a modification of a supervised classification, which takes into account spectral properties of individual pixels as well as their neighbourhood characteristics. Accuracy of the maximum likelihood classification was 81 % in the 1992 image and 54 % in the 1964 image. The neighbour classifier increased accuracy to 89 % and 82 % respectively. The overall results suggest that computerized analysis of sequences of panchromatic aerial photographs may serve as a valuable tool for the quantification of medium-term vegetation changes.     

Long‐term vegetation stability and the concept of potential natural vegetation in the Neotropics

When vegetation trends over time are analysed from an appropriate long‐term perspective using palaeoecological records, the concept of potential natural vegetation (PNV) is unsupported because of continual vegetation changes driven by natural or anthropic forcings. However, some palaeoecological records show long‐lasting (i.e. millennial) vegetation stability at multidecadal to centennial time scales in the absence of natural and human drivers of change, which fits within the definition of PNV. A more detailed palaeoecological analysis of these records shows that they are an exception rather than a rule, and that they cannot be differentiated from other transient ecological states. Therefore, long records of vegetation stability cannot be considered to be valid evidence for PNV. From a palaeoecological perspective, the PNV concept is concluded to be unnecessary, even in cases of multidecadal to centennial vegetation stability in the absence of environmental disturbance.     

Consistent shifts in spring vegetation green‐up date across temperate biomes in China, 1982–2006

Understanding spring phenology changes in response to the rapid climate change at biome‐level is crucial for projecting regional ecosystem carbon exchange and climate–biosphere interactions. In this study, we assessed the long‐term changes and responses to changing climate of the spring phenology in six temperate biomes of China by analyzing the global inventory monitoring and modeling studies (GIMMS) NOAA/AVHRR Normalized Difference Vegetation Index (NDVI) and concurrent mean temperature and precipitation data for 1982–2006. Results show that the spring phenology trends in the six temperate biomes are not continuous throughout the 25 year period. The spring phenology in most areas of the six biomes showed obvious advancing trends (ranging from ?0.09 to ?0.65 day/yr) during the 1980s and early 1990s, but has subsequently suffered consistently delaying trends (ranging from 0.22 to 1.22 day/yr). Changes in spring (February–April) temperature are the dominating factor governing the pattern of spring vegetation phenology in the temperate biomes of China. The recently delayed spring phenology in these temperate biomes has been mainly triggered by the stalling or reversal of the warming trend in spring temperatures. Results in this study also reveal that precipitation during November–January can explain 16.1% (P < 0.05), 20.9% (P < 0.05) and 14.2% (P < 0.05) of the variations in temperate deciduous forest (TDF), temperate steppe (TS), temperate desert (TD) respectively, highlighting the important role of winter precipitation in regulating changes in the spring vegetation phenology of water–limited biomes.     

Short‐term propagation of rainfall perturbations on terrestrial ecosystems in central California

Question: Does vegetation buffer or amplify rainfall perturbations, and is it possible to forecast rainfall using mesoscale climatic signals? Location: Central California (USA). Methods: The risk of dry or wet rainfall events was evaluated using conditional probabilities of rainfall depending on El Niño Southern Oscillation (ENSO) events. The propagation of rainfall perturbations on vegetation was calculated using cross‐correlations between monthly seasonally adjusted (SA) normalized difference vegetation index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR), and SA antecedent rainfall at different time‐scales. Results: In this region, El Niño events are associated with higher than normal winter precipitation (probability of 73%). Opposite but more predictable effects are found for La Niña events (89% probability of dry events). Chaparral and evergreen forests showed the longest persistence of rainfall effects (0‐8 months). Grasslands and wetlands showed low persistence (0‐2 months), with wetlands dominated by non‐stationary patterns. Within the region, the NDVI spatial patterns associated with higher (lower) rainfall are homogeneous (heterogeneous), with the exception of evergreen forests. Conclusions: Knowledge of the time‐scale of lagged effects of the non‐seasonal component of rainfall on vegetation greenness, and the risk of winter rainfall anomalies lays the foundation for developing a forecasting model for vegetation greenness. Our results also suggest greater competitive advantage for perennial vegetation in response to potential rainfall increases in the region associated with climate change predictions, provided that the soil allows storing extra rainfall.     

近50年北京植被对全球变暖的响应及其时效--基于遥感数据和物候资料的分析

植被对全球变暖的响应方式及其程度问题是全球变化研究的焦点之一。利用1951～2000年的气温、降水等气候资料、1982～2000年的NOAA／AVHRR遥感数据和1951～2002年山桃始花的物候数据,分析了北京各气候参量与生态系统植被在年际和年内时间尺度上的变化规律及其关系。结果表明,北京近20年增温1．2E,增温态势显著;过去50年中,降水的年际变化小于生长季内的波动幅度。年NDVI最大值(VP)、平均值(VM)的年际变化曲线呈波动中缓慢上升的趋势,表明植被的生长状况总体上在变好或生长季在延长。VP出现日期(VPI))提前,20世纪90年代比80年代提前4．5d。山桃始花物候期的分析表明,北京1988年以后春季开始日期提前9．63d。时间尺度不同,各个气候指标对各植被指标的影响程度不同：①年际时间尺度,年均温影响VP、VM的时效为1年;月际水平上,除温度影响VM的时效为2个月外,各气候参量影响VP、VM的时效为1个月。年际尺度上,温度影响山桃始花时效为2年;月际尺度上,2、3、4月份温度影响山桃始花日期。