Global Forest Types: Comparison
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Please note this is a comparison between Version 2 by Nora Tang and Version 1 by Xianliang Zhang.

Forest types are generally identified using vegetation or land-use types. However, vegetation classifications less frequently consider the actual forest attributes within each type. To address this in an objective way across different regions and to link forest attributes with their climate, we aimed to improve the distribution of forest types to be more realistic and useful for biodiversity preservation, forest management, and ecological and forestry research. The forest types were classified using an unsupervised cluster analysis method by combining climate variables with normalized difference vegetation index (NDVI) data.

  • forest types
  • NDVI
  • AVHRR GIMMS
  • temperature range
  • precipitation range

1. Introduction

Forests vary in structure and function across the world. The broad-scale vegetation units with common formation characteristics, due to similar climates, are known as vegetation types [1]. Generally, forest types are derived from vegetation or land use types. In fact, the first way to define forest types was based on a vegetation classification. Vegetation types were originally developed based on the idea that similar climates select for similar plant forms [2], and therefore the resulting types were mostly climate-based. The first formal climate classification system was defined by Köppen and was also used to predict the global vegetation distribution [3]. Other systems for delineating types based on climate variables include Holdridge life zones [4[4][5],5], Box models [6,7][6][7] and Whittaker’s biome types, though in the case of Whittaker’s biomes, predefined vegetation unites were mapped onto a climate space [8,9][8][9]. The predicted vegetation produced by biogeographical models (e.g., BIOME3 [10], dynamic global vegetation models, and bioclimatic maps [11]), is also mainly derived from an assumed relationship between functional types and climate variables. The ecoregions defined by Olson et al. [12] relied on climate data, expert judgment, and species assemblages to differentiate certain forest types. By mainly considering climate data, these methods define forest types that better describe the potential vegetation of an area. They may, however, fail to correspond with the actual vegetation, since this is defined by the interaction between the potential vegetation and multiple factors, such as human influence, species interactions, and biogeographical history.
Forest types were also defined in land-cover classifications. Land-cover classifications delineate vegetation types based on satellite imagery [13,14,15,16,17,18][13][14][15][16][17][18]. Functional biomes have also been defined using vegetation information [19]. Climatic vegetation types, on the other hand, try to reflect the regional vegetation characteristics in terms of climate by merging climate and NDVI data, and reveal the actual vegetation distribution, taking advantage of the positive aspects of both approaches [20]. Although this method showed great promise in Zhang et al. [20], the coarse resolution of the data, and the limited number of vegetation types used, resulted in a forest type distribution with relatively low accuracy. The forest attributes and their linkage to climate were also not well investigated in that study. A reanalysis and improved definition of the method in Zhang et al. [20] is timely, and it can provide a useful global forest type cartography that can more accurately represent actual vegetation distributions.
Different vegetation classifications are useful for different purposes. For example, climate-based vegetation classifications emphasize the distribution of vegetation types, while land use classifications highlight the role of land cover and human activity. It is important, consequently, to clarify the intent of new classifications. Our classification focuses on forest types, as they have been shown to be reliably characterized using satellite data [14,16][14][16]. An accurate definition of forest types is fundamental for preserving biodiversity [11] and forest ecological research (e.g., for studies that compare and explore the drivers of large-scale forest productivity [21]). To this end, forest types should reflect the actual main forest types present in different regions, but this is not guaranteed when using forest type classification based only on climate. For instance, the main forest type in the Northeast China Plain is temperate sub-humid broadleaf forest, and it is generally classified as cropland in land use and vegetation classifications [22], which does not properly capture the actual characteristics of the forested ecosystem. Classifications based only on normalized difference vegetation index (NDVI) values, on the other hand, would not separate different forest types with similar NDVI values but very different functional compositions [20]. We argue that forest types delineated to reflect the actual forest distribution using both vegetation and climate data will be more useful for multiple uses, from management to research [23].

2. Development

We produced a high-resolution global forest type cartography, delineated using the K-means clustering method based on monthly NDVI, temperature, and precipitation data. In previous studies, forest types have been identified as potential vegetation based mainly on climate data (e.g., [3,6,10][3][6][10]), or as actual vegetation based on satellite imagery (e.g., [14,16][14][16]). By contrast, here we used a forest type definition that we believe is closer to the modern concept of type (discussed in [23,46][23][24]) by considering not only climate variation, but also the patterns of monthly changes in the actual vegetation. There were clear differences in the monthly variations of NDVI values, temperature, and precipitation between forest types. Compared to a climate-based classification, also considering NDVI values has the advantage of reflecting the realized rather than the potential types. The example of Africa in Figure 5 stresses this, showing how two tropical forest types could be well separated when we also consider NDVI as vegetation data. However, overall, our classification is still highly consistent with climate-based forest types and should be seen as a refinement of them, rather than a challenge to previous work. There are clear differences in interpretation between our forest type classification and other vegetation classifications. Vegetation or land-cover-based classifications highlight the vegetation or land physical attributes, while our forest types emphasize the forest attributes. In addition, land cover classifications include human-transformed vegetation, such as pasture and urban buildings [47][25], while we masked out human-created types when identifying the forest regions. There is no detailed information on the forest types defined using land-cover and vegetation classification. Our classification improves the detail in forest types and contains more information on forest types than other vegetation classifications. Our classification seems to accurately identify known forest ecosystems, whose climatic definitions were well separated. This is particularly important for distinct forest types that share similar NDVI values. For instance, we could clearly identify known sub-divisions of the boreal forest that could not be differentiated in an objective way using only climate, i.e., sub-frigid sub-humid deciduous coniferous forests, frigid semiarid coniferous forests, and sub-frigid sub-humid broadleaf and needleleaf mixed forests, clearly distinguishing sub-frigid from frigid coniferous ecosystems. The main difference between sub-frigid semiarid coniferous forests and frigid semiarid coniferous forests is that the main forest ecosystem in the former type is the larch-dominated bright coniferous forest while the sparse larch trees with shrub dominate in the latter forest type. An important feature of our model is that it includes a dynamic definition of forest types. Since the NDVI is a characteristic of the vegetation and changes every year, the forest types defined by the NDVI can change with time, as the main vegetation evolves, or as a response to changing climate (e.g., [48][26]). That way, it is possible to regularly update the forest type classification, to keep the forest types accurate and to study the effect that a changing climate has on forested landscapes. However, in the short- and medium-timescale, we expect these forest types to be quite stable due to the rather stable signal of the long-term climate data (compared with a NDVI-only model). It should be noted that there are some forest types that are not only determined by climate but also by edaphic and/or hydrological conditions, such as dry forests (vs. wooded grassland), riverine forests and swamp forests, which our approach is not able to differentiate. Another main limitation of our classification is that forest types occupying a small region (i.e., with an extension lower than that provided by the macroclimate data products we used), and those not having a distinct NDVI and climate to nearby forest types would be merged into nearby forest types. Tropical and temperate montane forests were not picked up by our clustering analysis, likely due to their limited spatial extension and lack of climate data resolution. Increasing data resolution across the world will alleviate this problem. Alternatively, including a proxy for ‘montane conditions’, such as, perhaps, relative elevation or a combination of solar radiation and exposition, would aid in this goal. It is not easy to make a one-on-one comparison between forest type classifications because they differ in the number of types they consider, they are based on different datasets, and are designed for different purposes [19]. While there are important differences between our classification and previous classifications on fine scales, on a large scale our forest types tended to largely agree with corresponding land-cover types or vegetation types defined in previous studies [14,16][14][16]. GlobCover forest types had a high large scale agreement with ours. However, using better datasets, updating our methods, and including a larger number of types improved our ability to correctly identify the boundaries between different forest types at a finer scale. We also included self-descriptive forest labels, rather than types based on climate notation. These labels are easier and more intuitive to interpret for non-scientists. The result is a more accurate distribution of forest types, which will hopefully be more suitable for forestry and biogeographic studies.

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