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1.
In their recent paper, Hanan et al. (Global Ecology and Biogeography, 2014, 23 , 259–263) argue that the use of classification and regression trees (CARTs) to calibrate global remote sensing datasets, including the MODIS VCF tree‐cover dataset, makes these data inappropriate for analysing the frequency distribution of tree cover. While we agree with their most general point – that the use of remote sensing products should be informed and deliberate – their analysis overlooks a few key aspects of the use of CARTs in generating global tree‐cover data. Firstly, while their presentation of flaws in the use of CARTs is compelling, their use of hypothetical data obscures the reasons why CARTs are a useful tool. Secondly, they do not actually examine the error distributions of the MODIS VCF tree‐cover data. Such an analysis, which we perform, revealed the following: (1) the MODIS VCF product may not be useful for differentiating over small ranges of tree cover (less than c. 10%); (2) that the bimodality of low and high tree cover, with a frequency minimum at intermediate tree cover, is not attributable to bias in MODIS VCF tree‐cover calibrations; and (3) that the MODIS VCF is not well‐resolved below c. 20–30% tree cover, such that MODIS cannot be used with any confidence to evaluate multimodality in tree cover in that range. Further validation and calibration are likely to be helpful and, at low tree cover, necessary for improving MODIS VCF tree‐cover estimates. However, the MODIS VCF – which has facilitated major steps in our ability to examine ecological phenomena at global scales – remains a useful tool for well‐informed ecological analysis.  相似文献   

2.
    
Staver & Hansen (2015, Global Ecology and Biogeography, doi: 10.1111/geb.12285) comment on our recent paper (Hanan et al., Global Ecology and Biogeography, 2014, 23 , 259–263) in which we argue that classification and regression tree methods used with remote sensing data to predict tree cover may bias inference of bifurcations in savanna vegetation communities. While we agree with several of their comments, we remain unconvinced that a remote sensing product based on an inherently discontinuous statistical approach can, or should, be used to test for discontinuities.  相似文献   

3.
Multiple stable states, bifurcations and thresholds are fashionable concepts in the ecological literature, a recognition that complex ecosystems may at times exhibit the interesting dynamic behaviours predicted by relatively simple biomathematical models. Recently, several papers in Global Ecology and Biogeography, Proceedings of the National Academy of Sciences USA, Science and elsewhere have attempted to quantify the prevalence of alternate stable states in the savannas of Africa, Australia and South America, and the tundra–taiga–grassland transitions of the circum‐boreal region using satellite‐derived woody canopy cover. While we agree with the logic that basins of attraction can be inferred from the relative frequencies of ecosystem states observed in space and time, we caution that the statistical methodologies underlying the satellite product used in these studies may confound our ability to infer the presence of multiple stable states. We demonstrate this point using a uniformly distributed ‘pseudo‐tree cover’ database for Africa that we use to retrace the steps involved in creation of the satellite tree‐cover product and subsequent analysis. We show how classification and regression tree (CART)‐based products may impose discontinuities in satellite tree‐cover estimates even when such discontinuities are not present in reality. As regional and global remote sensing and geospatial data become more easily accessible for ecological studies, we recommend careful consideration of how error distributions in remote sensing products may interact with the data needs and theoretical expectations of the ecological process under study.  相似文献   

4.
    
In their recent paper, Staver and Hansen (Global Ecology and Biogeography, 2015, 24, 985–987) refute the case made by Hanan et al. (Global Ecology and Biogeography, 2014, 23, 259–263) that the use of classification and regression trees (CARTs) to predict tree cover from remotely sensed imagery (MODIS VCF) inherently introduces biases, thus making the resulting tree cover unsuitable for showing alternative stable states through tree cover frequency distribution analyses. Here we provide a new and equally fundamental argument for why the published frequency distributions should not be used for such purposes. We show that the practice of pre‐average binning of tree cover values used to derive cover values to train the CART model will also introduce errors in the frequency distributions of the final product. We demonstrate that the frequency minima found at tree covers of 8–18%, 33–45% and 55–75% can be attributed to numerical biases introduced when training samples are derived from landscapes containing asymmetric tree cover distributions and/or a tree cover gradient. So it is highly likely that the CART, used to produce MODIS VCF, delivers tree cover frequency distributions that do not reflect the real world situation.  相似文献   

5.
6.
    
Aim This paper evaluates a method of combining data from GPS ground survey with classifications of medium spatial resolution LANDSAT imagery to distinguish variations within Neotropical savannas and to characterize the boundaries between savanna areas and the associated gallery forests, seasonally dry forests and wetland communities. Location Rio Bravo Conservation Area, Orange Walk District, Belize, Central America. Methods Dry season LANDSAT data for 10 April 1993 and 9 March 2001 covering a conservation area of 240,000 acres (97,459 ha), were rectified to sub‐pixel accuracy using ground control points positioned by GPS ground survey. The 1993 image was used to assess the accuracy with which the boundaries between the savanna matrix and gallery forests, high forests, wetlands and water bodies could be discriminated. The image was classified by a maximum likelihood (ML) classifier and the shapes and areas of forest and wetland classes were compared with an interpretation of these land cover types from 1 : 24,000 aerial photography, mapped at 1 : 50,000 scale in 1993. The 2001 image was used to assess whether different subtypes of savanna could be distinguished from LANDSAT data. This required the creation of a reference (‘ground truth’) data set for testing classifications of the image. One hundred and sixty sample patches (650 ha, distributed over an area of 7000 ha) of ten sub‐types of savanna vegetation and associates identified using a physiognomic classification scheme, were delineated on the ground by GPS and divided into two subsets for training and testing. Continuous classifications of LANDSAT data covering the savannas were developed that estimated potential contributions from up to five sub‐types of land cover (grassland, wetland, pine woodland, gallery forest and palmetto). The accuracy of each classification was assessed by comparison against ground data. An ML classification was also produced for the 2001 image using the same areas for training. This allowed a comparison of the relative accuracy of both continuous and Boolean ML methods for classifying savanna areas. Results The boundary between savannas and evergreen forests, gallery forests and open water in the study region could be delineated by the ML classifier to within 2 pixels (60 m) using LANDSAT imagery. However, the constituent sub‐types within the savanna were poorly discriminated. Whilst the shape and extent of closed canopy forest, gallery forest, wetlands and water bodies agreed closely with the distributions interpreted from aerial photography, classes such as ‘open pine savanna’ or ‘grassland’ were only 45–65% accurate when tested against ground data. A continuous classification, estimating the proportions of three savanna vegetation subtypes (grassland, marshland and woodland) present in each pixel, correctly classified more of the ground data for these cover types than the comparable ML result. Proportional mixtures of the land cover estimated by the continuous classifier also compared realistically with the vegetation formations observed along ground transects. Main conclusions By using GPS, a ground survey of vegetation cover was accurately matched to remotely sensed imagery and the accuracy of delineating boundaries and classifying areas of savanna was assessed directly. This showed that ML classification techniques can reliably delineate the boundaries of savannas, but continuous classifiers more accurately and realistically represent the distribution of the subtypes comprising savanna land cover. By combining these ground survey and image classification methods, medium spatial resolution satellite sensor data can provide an affordable means for land managers to assess the nature, extent and distribution of savanna formations. Over time, using the archives of LANDSAT (and SPOT) data together with marker sites surveyed in the field, quantitative changes in the extents and boundaries of savannas in response to both natural (e.g. fire, hurricane and drought) and anthropogenic (e.g. cutting and disturbance) factors can be assessed.  相似文献   

7.
    
It has recently been found that the frequency distribution of remotely sensed tree cover in the tropics has three distinct modes, which seem to correspond to forest, savanna, and treeless states. This pattern has been suggested to imply that these states represent alternative attractors, and that the response of these systems to climate change would be characterized by critical transitions and hysteresis. Here, we show how this inference is contingent upon mechanisms at play. We present a simple dynamical model that can generate three alternative tree cover states (forest, savanna, and a treeless state), based on known mechanisms, and use this model to simulate patterns of tree cover under different scenarios. We use these synthetic data to show that the hysteresis inferred from remotely sensed tree cover patterns will be inflated by spatial heterogeneity of environmental conditions. On the other hand, we show that the hysteresis inferred from satellite data may actually underestimate real hysteresis in response to climate change if there exists a positive feedback between regional tree cover and precipitation. Our results also indicate that such positive feedback between vegetation and climate should cause direct shifts between forest and a treeless state (rather than through an intermediate savanna state) to become more likely. Finally, we show how directionality of historical change in conditions may bias the observed relationship between tree cover and environmental conditions.  相似文献   

8.
    
Spatio‐temporal variation in tropical savanna tree cover remains poorly understood. We aimed to quantify the drivers of tree cover in tropical mesic savannas in Kakadu National Park by relating changes in tree cover over 40 years to: mean annual rainfall, fire activity, initial tree cover and prior changes in tree cover. Aerial photography, acquired in 1964, 1984 and 2004, was obtained for fifty sites in Kakadu that spanned a rainfall gradient from approximately 1200 to 1600 mm. The remotely sensed estimates of tree cover were validated via field survey. Linear mixed effects modelling and multi‐model inference were used to assess the strength and form of the relationships between tree cover and predictor variables. Over the 40 years, tree cover across these savannas increased on average by 4.94 ± 0.88%, but was spatio‐temporally variable. Tree cover showed a positive albeit weak trend across the rainfall gradient. The strength of this positive relationship varied over the three measurement times, and this suggests that other factors are important in controlling tree cover. Tree cover was positively related to prior tree cover, and negatively correlated with fire activity. Over 20 years tree cover was more likely to increase if (i) tree cover was initially low or (ii) had decreased in the previous 20‐year interval or (iii) there had been fewer fires. Across the examined rainfall gradient, the greater variability in fire activity and inherently higher average tree cover at the wetter latitudes resulted in greater dynamism of tree cover compared with the drier latitudes. This is consistent with savanna tree cover being determined by interactions between mean annual rainfall, tree competition and frequent fire in these tropical mesic savannas.  相似文献   

9.
    
Forest–savanna mosaics exist across all major tropical regions. Yet, the influence of environmental factors on the distribution of these mosaics is not well explored, limiting our understanding of the environmental constraints on savannas especially in Southeast Asia, where most savannas exist in mosaics. Despite clear structural and functional characteristics indicative of savannas, most SE Asian savannas continue to be classified as forest. This designation is problematic because SE Asian savannas are threatened by both fragmentation and forest-centric management practices. By studying forest–savanna mosaics across SE Asia, we aimed to parse out how landscape mosaics of forest and savanna may be constrained by fire, climate and soil characteristics. We used remotely sensed data to characterize the distribution of tree cover and forest–savanna mosaics. Using regression models, we quantified the relative effects of precipitation, fire frequency, seasonality and soil characteristics on average tree cover and landscape patchiness. We found that low tree cover, indicative of savannas, occurs in drier, seasonal subregions that experience frequent fire. Further, our results demonstrate that fire and precipitation strongly shape landscape patchiness. Landscapes were patchiest in subregions with low precipitation and intermediate fire frequency. These results demonstrate that the environmental factors important in delineating the distribution of savannas globally shape the distribution of tree cover and landscape patchiness across SE Asia. Fire especially drives patterns of tree cover across scales. In a region where fire suppression is a common management strategy, our results suggest that further research studying vegetation response to fire and fire suppression is needed to improve management and conservation of these mosaic landscapes. More broadly, this work demonstrates a useful approach for studying the environmental drivers that influence the distribution of forest–savanna mosaics.  相似文献   

10.
    
Abstract

The analysis of landscape changes in space and time plays an important role in landscape ecology. Analyzing landscape dynamics through time may be crucial for identifying historical and current processes that shape the actual landscapes and for developing predictive landscape models for ecosystem management and conservation. In this view, the propensity of land cover patches to change is at least partially related to the nature of their contact types. The interactions of a given patch with adjacent land cover types affect both land use exploitation by humans and vegetation dynamics. The aim of this paper is to use patch boundary dynamics for describing the landscape changes that occurred in the Lepini Mountains (central Italy) during 1954 – 2000. Results show an increase in landscape complexity in the Mediterranean land units and a corresponding decrease in landscape complexity in the Temperate land units. This differential trend is due to a complex, human-driven temporal dynamics of Mediterranean ecosystems that generates heterogeneity as opposed to a diffuse landscape abandonment in the Temperate region that leads to a more homogeneous boundary structure.  相似文献   

11.
Forest and savanna biomes dominate the tropics, yet factors controlling their distribution remain poorly understood. Climate is clearly important, but extensive savannas in some high rainfall areas suggest a decoupling of climate and vegetation. In some situations edaphic factors are important, with forest often associated with high nutrient availability. Fire also plays a key role in limiting forest, with fire exclusion often causing a switch from savanna to forest. These observations can be captured by a broad conceptual model with two components: (1) forest and savanna are alternative stable states, maintained by tree cover-fire feedbacks, (2) the interaction between tree growth rates and fire frequency limits forest development; any factor that increases growth (e.g. elevated availability of water, nutrients, CO(2)), or decreases fire frequency, will favour canopy closure. This model is consistent with the range of environmental variables correlated with forest distribution, and with the current trend of forest expansion, likely driven by increasing CO(2) concentrations. Resolving the drivers of forest and savanna distribution has moved beyond simple correlative studies that are unlikely to establish ultimate causation. Experiments using Dynamic Global Vegetation Models, parameterised with measurements from each continent, provide an important tool for understanding the controls of these systems.  相似文献   

12.
基于多光谱影像的森林树种识别及其空间尺度响应   总被引:1,自引:0,他引:1  
当前,不同空间分辨率卫星影像对森林类型识别结果中普遍存在的尺度效应,而且纹理参量对不同尺度下树种识别精度的影响仍缺乏广泛认知.本研究以中国东北旺业甸林场为研究区,采用观测时相同步、地理坐标匹配的GF-1 PMS、GF-2 PMS、GF-1 WFV,以及Landsat-8 OLI卫星传感器数据组成空间尺度观测序列(1、2、4、8、16、30 m),并结合支持向量机(SVM)模型,探讨了区域内5种优势树种遥感识别结果的尺度变化规律及其纹理特征参数的影响,同时检验了基于尺度上推转换影像的树种识别结果差异.结果表明: 影像空间分辨率对区域树种识别结果具有显著影响,其中,研究区森林树种识别的最佳影像分辨率为4 m,当分辨率降低至30 m时,树种识别结果最差.在1~8 m影像分辨率范围内,增加纹理信息能够显著提高不同优势树种的识别精度,使总分类精度提升了2.0%~3.6%,但纹理信息对16~30 m影像的识别结果没有显著影响.与真实尺度卫星影像相比,基于升尺度转换影像的树种识别结果及其尺度响应特征存在显著差异,表明在面向多个空间尺度的遥感观测和应用研究中,需要采用真实分辨率影像以确保结果的准确性.  相似文献   

13.
    

Aim

Although much tropical ecology generally focuses on trees, grasses are fundamental for characterizing the extensive tropical grassy biomes (TGBs) and, together with the tree functional types, for determining the contrasting functional patterns of TGBs and tropical forests (TFs). To study the factors that determine African biome distribution and the transitions between them, we performed the first continental analysis to include grass and tree functional types.

Location

Sub‐Saharan Africa.

Time period

2000–2010.

Major taxa studied

Savanna and forest trees and C4 grasses.

Methods

We combined remote‐sensing data with a land cover map, using tree functional types to identify TGBs and TFs. We analysed the relationships of grass and tree cover with fire interval, rainfall annual average and seasonality.

Results

In TGBs experiencing < 630 mm annual rainfall, grass growth was water limited. Grass cover and fire recurrence were strongly and directly related over the entire subcontinent. Some TGBs and TFs with annual rainfall > 1,200 mm had the same rainfall seasonality but displayed strongly different fire regimes.

Main conclusions

Water limitation to grass growth was fundamental in the driest TGBs, acting alongside the well‐known limitation to tree growth. Marked differences in fire regimes across all biomes indicated that fire was especially relevant for maintaining mesic and humid TGBs. At high rainfall, our results support the hypothesis of TGBs and TFs being alternative stable states maintained by a vegetation–fire feedback for similar climatic conditions.  相似文献   

14.
    
Prediction of ecosystem response to global environmental change is a pressing scientific challenge of major societal relevance. Many ecosystems display nonlinear responses to environmental change, and may even undergo practically irreversible ‘regime shifts’ that initiate ecosystem collapse. Recently, early warning signals based on spatiotemporal metrics have been proposed for the identification of impending regime shifts. The rapidly increasing availability of remotely sensed data provides excellent opportunities to apply such model‐based spatial early warning signals in the real world, to assess ecosystem resilience and identify impending regime shifts induced by global change. Such information would allow land‐managers and policy makers to interfere and avoid catastrophic shifts, but also to induce regime shifts that move ecosystems to a desired state. Here, we show that the application of spatial early warning signals in real‐world landscapes presents unique and unexpected challenges, and may result in misleading conclusions when employed without careful consideration of the spatial data and processes at hand. We identify key practical and theoretical issues and provide guidelines for applying spatial early warning signals in heterogeneous, real‐world landscapes based on literature review and examples from real‐world data. Major identified issues include (1) spatial heterogeneity in real‐world landscapes may enhance reversibility of regime shifts and boost landscape‐level resilience to environmental change (2) ecosystem states are often difficult to define, while these definitions have great impact on spatial early warning signals and (3) spatial environmental variability and socio‐economic factors may affect spatial patterns, spatial early warning signals and associated regime shift predictions. We propose a novel framework, shifting from an ecosystem perspective towards a landscape approach. The framework can be used to identify conditions under which resilience assessment with spatial remotely sensed data may be successful, to support well‐informed application of spatial early warning signals, and to improve predictions of ecosystem responses to global environmental change.  相似文献   

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