Mapping vegetation suitability in coarse sandy hill ... - Springer Link

Front. For. China 2008, 3(2): 148–157 DOI 10.1007/s11461-008-0033-8

RESEARCH ARTICLE

Zhongming WEN, Xiaohui HE, Feng JIAO, Tim R. McVICAR, Lingtao LI, Tom G. VAN NIEL

Mapping vegetation suitability in coarse sandy hill catchment areas of the Loess Plateau

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Higher Education Press and Springer-Verlag 2008

Abstract Vegetation suitability mapping is important for the selection of species for implementing the re-vegetation program in the coarse sandy hill catchment areas of the Loess Plateau, China. We introduce a Boolean model, which uses a thin plate smoothing spline interpolation method to model the distribution of precipitation and temperature and analyze the suitability of 38 species using GIS techniques considering the requirement of these species for environmental conditions. Then we overlay a single suitability map of these 38 species to obtain a species frequency map for a specific site. In our study, we mean with ‘high frequency’ a high suitability of a site for revegetation. The results show that we can model the spatial changes of vegetation suitability with a combination of topographic analyses based on DEM and GIS functions. We also demonstrate these spatial changes on the screen using Visual Basic language and GIS functions, which will help decision makers to have a clear view of species and site suitability changes over large areas. Although more powerful models, such as GLM and GAM, were not used due to data limitation, our methods can still provide some relevant insights for similar studies. Keywords vegetation suitability, topographic analysis, thin plate smoothing spline interpolation

Translated from Science of Soil and Water Conservation, 2007, 5(1): 19–26 [译自: 水土保持科学] Zhongming WEN (*), Xiaohui HE, Feng JIAO Institute of Soil and Water Conservation, Northwest A & F University, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100, China E-mail: [email protected] Tim R. McVICAR, Lingtao LI School of Environment and Water Conservancy, Zhengzhou University, Zhengzhou 450001, China Tom G. VAN NIEL CSIRO Land and Water, GPO Box 1666, 2601, Canberra, ACT, Australia

1 Introduction The Loess Plateau is one of those areas in the world that have suffered considerably from soil erosion. Approximately 90% of the sediments in the Yellow River come from its major south-flowing branch, draining the region of the Loess Plateau, locally known as the ‘‘sandy coarse-sandy area’’. To control soil erosion in the region, the state government of China has implemented several programs aimed at reducing erosion-associated environmental problems since the 1950s. Because of improper choice of species or lack of understanding of the spatial distribution of species suitability (Wang, 2004), forest plantations have resulted in severe ecohydrological problems for some sites within the areas (Li, 2001; Huang et al., 2003; Wang et al., 2004a, 2004b), causing the formation of dry soil layers in the soil profile and threatening the stability of vegetation and delay their benefits to the environment. Some studies have been carried out to find species suitability in the region and to provide scientific evidence for the choice of species. For example, He et al. (2003) analyzed the relations between photosynthesis and variation in soil water and defined a suitable soil water range for Robinia pseudoacacia, Caragana microphylla and Malus domestica Borkh. Xu et al. (2004) explored the feasibility of planting Pinus tabulaeformis Carr. in the Loess Plateau on the basis of soil suitability. However, these studies mainly focused on local sites and cannot help the re-vegetation program on a regional scale. On the other hand, there has been an increasing use of predictive vegetation mapping both globally and specifically in China over the last 30 years for a range of issues including: 1) ecological restoration planning; 2) biodiversity conservation planning; 3) site selection for afforestation programs and 4) assessing disturbance impacts on the distribution and function of vegetation. In our investigation we have combined the methods developed in these previous studies with GIS techniques to predict the species suitability on a regional scale and used environmental variables, such


as precipitation, temperature and topography, as predictive variables. The objective of this study is to map the species suitability in the coarse sandy hill catchment (CSHC) area of the Loess Plateau and provide the bases for the re-vegetation program in this area or a new clue for further studies.

2

Study area

Our research site is a sandy to coarse-sandy area, covering 113000 km2 and involves 72 counties and 42 catchments. It is the most severely eroded area in the Loess Plateau. About 55.7% of the sediment in the Yellow River comes from this area of which coarse sand (. 0.05 mm) accounts for 73% of the total sediment from this watershed (Ye,

Fig. 1

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1994). Therefore, re-vegetation in this region plays an important role in controlling soil and water loss and reducing the amount of sediment going into the Yellow River. The region straddles semi-humid, semiarid and arid climatic zones. The average annual rainfall ranges from 200 mm in the northwest to 650 mm in the southeast. More than 60% of the rainfall occurs during the summer monsoon (July to September) season. Since rainfall is low and variable, water is the most important factor limiting re-vegetation in the region. Meanwhile the fragmented and undulating topography, which has a visible impact on redistribution of precipitation and temperature, makes it difficult to re-vegetate the region. The natural vegetation of this region can be divided into three zones, i.e., a forest zone, a forest-steppe zone and a steppe based on Zou (2000).

Location and scope of study area

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3 3.1

Zhongming WEN, et al.

vegetation site suitability assessment (or predictive vegetation mapping), these variables can either be continuous or categorical and this, in part, determines the type of modeling approach that can be used.

Methods and materials Model selection

There are different models available for the evaluation of vegetation suitability (Guisan et al., 2000; Anderson et al., 2003). Figure 2 shows the classification of these models and criteria for the choice of suitable models for specific purposes. Generally, the choice of models depends not only on the purpose of a study but also on access to data and scale of the project. Our aim was to map vegetation suitability for re-vegetation planning in the CSHC area. Following Guisan and Zimmerman (2000), this broad aim has the characteristics of 1) being general, 2) not incorporating disturbances and dynamics, 3) being conducted over a large geographic area and 4) will not incorporate effects of climate change. Given our data availability, we have chosen a Boolean approach to assess the vegetation suitability, using grid-format data. With the model defined, the next step is to define the environmental variables. Following Austin (1987), three types of environmental variables may be used to determine vegetation distribution, abundance and quantities. They are 1) resources consumed by the plant, e.g., CO2, water, light and nutrients; 2) direct variables not consumed by the plant, yet have direct physiological effect on growth, e.g., temperature (both air and soil) and pH and 3) indirect variables that have no direct physiological effect on growth, but are correlated with species distribution due to their correlation with variables such as temperature, soil moisture and precipitation (e.g., position of landscape, elevation, longitude and latitude, distance from the coast). In any exercise of

Fig. 2

3.2

Choice and treatment of environmental variables

Austin’s classification of environmental variables can help define variables for vegetation suitability assessment, but it should be noted that these variables might have different impacts on the distribution of vegetation at various scales. Walter (1984) demonstrated in his book Vegetation of the Earth that climate controls the vegetation pattern on a macro-scale while topography plays a more important role in confining the vegetation distribution on a local scale. Considering the scale in our study, we have chosen precipitation, temperature and topography as the main variables. There is one other important variable, soil, which supports the development of vegetation and also impacts the distribution of vegetation. So we took soil as an additional variable. However, these variables cannot be used directly for model building and need to be expressed in the form of indices. Given the data at our disposal, we have chosen mean annual precipitation (R), average temperature in July (D), topography (I), soil pH value (D) and soil nitrogen (R) as environmental indices, where I stands for indirect variable, D for direct variable and R for resource variable. No doubt, other variables also have an impact on the distribution of vegetation, but these impacts appear limited on a large scale and have not been taken into consideration by us. Data of mean annual precipitation and average temperatures in July have been obtained from weather stations

Criteria for model selection for vegetation suitability assessment


Fig. 3 Input data for vegetation suitability assessment (a) annual precipitation (R); (b) mean July temperature (D); (c) soil total nitrogen (R); (d) soil pH (D); (e) land position (I)

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within as well as from outside the study area. A total of 100 weather stations were selected for interpolation of these two variables using a thin plate smoothing spline interpolation method (Hutchinson, 1991, 1995). Our source for soil pH and nitrogen data is the Soil Resources on Loess Plateau (Loess Plateau Science Exploration Team of CAS, 1991). This data was incorporated into the soil map of the Loess Plateau at a scale of 1:500000. We then excised the study area from the soil map and transferred it into a grid format (cell size is 100 m 6 100 m) using ARCGIS. The landscape was classified into six classes: LM (Liangmao, means mountain ridge), SFS (south facing slope), NFS (north facing slope), FB (flat bottom), SSG (steep slope gully) and RM (rocky mountain). All variables were transferred into the grid format data as shown in Fig. 3. 3.3

Choice of species

Over time, re-vegetation programs may use different growth forms — trees, shrubs and grasses — or a mixture. On the Loess Plateau, the choice of species for afforestation has been extensively studied (Yuan and Zhang, 1991; Zhao et al., 1994; Wu and Yang, 1998). In these studies, the choice of species mainly focused on trees and shrubs. For example, Yuan and Zhang (1991) listed 66 species and provided the fundamental Boolean rules used to map suitability assessment for these species. Zhao et al. (1994) carried out an extensive investigation of shrubs species on the Loess Plateau. For our study, we selected a subset of these 66 species that are primarily native to the Loess Plateau and show optimal growth performance in the CSHC area. We chose 38 species, of which 24 were tree species and 14 shrub species (Table 1). 3.4 Requirement of ecological conditions of different species The assessment of suitability, either plant-based or community-based, depends on the availability of data required. For our purposes, the suitability assessment was based on plants, not on communities, because of the excellent research on the Loess Plateau during past decades (Yuan and Zhang, 1991). Based on that research, we revised and redefined the ranges of ecological variables of 47 individual species. We carried out a cross-check of these values to ensure that there were no contradictions in the data for a single species (Table 2). 3.5

Assessment of vegetation suitability and site analysis

Assessment of vegetation suitability was carried out according to the requirement of different species for ecological conditions. The first step was to select the cells that meet the requirements of different species for ecological conditions by programming in ARCGIS. The areas covered by these cells are areas suitable for a specific species. A separate map was produced for each of the 38 species.

Table 1

Species selected for suitability assessment

code number

native

growth form

Latin name

1* 2* 3*

yes yes yes

T T S

4 5* 6* 7* 8* 9

yes yes yes yes yes yes

S T T T T S

10 11* 12* 13 14 15* 16* 17 18* 19* 20 21 22* 23* 24 25 26* 27* 28 29* 30* 31* 32 33 34 35* 36 37* 38

yes no yes yes yes yes no yes yes yes yes yes yes yes yes no yes yes yes yes yes yes yes yes yes yes yes yes no

S T T S T S S S S S S S T S T T T T T T T T T T S T T T T

Pinus tabulaeformis Carr. Betula platyphylla Suk. Amygdalus davidiana (Carr.) C.de Voe ex Henry Rosa xanthina Lindl. Quercus liaotungensis Koidz. Ulmus pumila Populus simonii Carr. Platycladus orientalis (L.) Vitex negundo Linn.var. heterophylla (Franch.) Rehd Sophora davidii Robinia pseudoacacia Salix matsudana Ziziphus jujuba var. spinosa Populus davidiana Ostryopsis davidiana Decne Amorpha fruticosa Elaeagnus angustifolia Caragana microphylla Salix psammophila Tamarix spp. Salix cheilophila Schneider Ailanthus altissima Hippophae rhamnoides P. cathayana Rehd Populus alba Prunus davidiana Prunus armeniana var. ansu Populus tomentosa Carr. Populus hopeiensis Malus domestica Borkh. Pvres bretschneideri Mofus alba L. Juglans regia Xanthoceras sorbifolia Bge. Pyrus betulaefoli Catalpa bungei C.A.Mey Zizyphus jujuba Mill Populus nigra var. thevestina (Dode) Beain

Note: * marks common species; T means tree and S means shrub

The next step was to overlay these 38 maps according to the type of plants, i.e., trees and shrubs, to find how many species were suitable for a specific cell. No doubt, the larger the number of suitable species, the larger the number of suitable cells for the type of plants. Following this logic, site suitability was assessed (Fig. 4).

4 Result and analysis 4.1

Mapping species suitability

Based on the rules of Table 2 and using data shown in Fig. 3, we have produced a suitability map for each species listed in Table 1. Since it is impossible to show all 38 maps, only some examples are shown (Fig. 5). In these maps, grey means suitable areas for species while

Mapping vegetation suitability in coarse sandy hill catchment areas of the Loess Plateau Table 2 Code number

153

Requirement of 38 species for ecological conditions total N/%

pH

annual mean precipitation (R)/mm

mean temperature (T) in July/uC

Land form

1* 2* 3* 4 5* 6* 7* 8* 9 10 11*

no limit no limit no limit no limit no limit no limit > 0.05 no limit no limit no limit no limit

5.6 , pH , 6.5 5.6 , pH , 6.5 no limit no limit no limit no limit no limit no limit no limit no limit no limit

200 ( R ( 400 R . 400 R . 200 R . 400 R . 200 R . 200 R . 200 R . 200 R . 200 R . 200 R . 200

T > 14 no limit no limit no limit no limit no limit no limit T > 14 T > 14 T > 14 T > 14

12* 13 14 15* 16* 17 18* 19* 20

no limit no limit no limit no limit no limit no limit no limit no limit no limit


R . 200 R . 300(400) R . 400 R . 400 R . 200 R . 200 R . 200 R . 200 R . 400(200)

T > 14 T > 14 T > 14 T > 14 no limit no limit no limit no limit no limit

21 22* 23*

no limit no limit no limit

no limit no limit no limit

R . 200 R . 200 R . 200(400)

no limit no limit T > 14

24 25 26* 27* 28 29*

> 0.05 > 0.05 > 0.05 > 0.05 > 0.05 > 0.05

no limit no limit no limit no limit no limit no limit

R . 200(400) R . 200(400) R . 400 R . 200(400) R . 500(400) R . 200(400)

no limit (T > 14) no limit (T > 14) T > 14 T > 14 T > 14 T > 14

30* 31* 32 33 34 35* 36 37* 38

> 0.05 > 0.05 > 0.05 > 0.05 > 0.05 > 0.05 > 0.05 > 0.05 . 0.08


R . 400 R . 400 R . 400 R . 400 R . 400 R . 300(400) R . 400 R . 400 R . 400

T > 14 T > 14 T > 14 T > 14 T > 14 T > 14 T > 18 T > 18 T > 14

LM, NFS, RM SFS, RM SFS, RM (SFS+), RM RM SFS, NFS, FB, RM SFS, NFS, FB (LM+), SFS, FB, RM SFS SFS (LM2), SFS, NFS, FB, RM (SFS+), FB SFS, RM (NFS+), RM (NFS+), RM (LM+), SFS, FB, RM SFS, FB, RM, LM, SFS, FB FB, (LM2), SFS, NFS, FB FB, LM, SFS, FB LM, SFS, NFS, FB, RM (LM2), NFS, FB (LM2), FB LM, FB, RM LM, SFS, FB, RM LM, FB NFS, (FB+), RM, (SFS2) LM, FB, RM LM, FB, RM LM, FB, (RM2) LM, SFS, FB, RM LM, SFS LM, SFS, FB, NFS, FB, (LM2) NFS, FB, (LM2) LM, FB

Note: no limit means that at the study site, soil fertility is not a limiting factor. The number shows in which range one species can grow. LM: Liangmao (mountain ridge) and high flat area; NFS: north facing slopes; SFS: south facing slopes; FB: flat bottom; RM: rocky mountain; * indicates the most common species; F: forest zone; FS: forest-steppe zone; S: steppe zone.

white areas are not suitable. These maps indicate the spatial pattern of species suitability, thus providing implications for the choice of species for different land positions. For example, Pinus tabulaeformis Carr., Robinia pseudoacacia and Platycladus orientalis can be planted in most of the sandy coarse-sandy areas while Salix psammophila and Salix cheilophila Schneider are confined to the parts of the northwest. 4.2

species maps and all shrub species maps (Fig. 6). These frequency maps show how many trees or shrubs or common trees or shrubs can be planted in each pixel. For a given pixel, the more trees or shrubs or common trees or shrubs can be planted, the more suitable the pixel will be for re-vegetation with these tree or shrub species. From these frequency maps, it can be seen that the habitats in the south are suitable for a large number of species.

Analysis and mapping of habitat suitability 4.3

Based on the species suitability maps, frequency maps of species were produced by summing all species maps, all tree species maps, all shrub species maps, all common

Binary vegetation suitability maps

The suitability maps produced above are valuable for the selection of species for a site or the choice of a site

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Fig. 4

Flowchart of suitability assessment of species

for a particular species. However, these maps cannot be used directly owing to the difficulty in reading maps. Thus, we produced another map that we called a binary vegetation map. It identifies not only the number of species (any combination) suitable for a place (pixel) but also the names of these species. Figure 7 shows the suitability of common species for Suide County as an example. In the ArcInfo environment (Fig. 7a), by clicking a cell (Fig. 7b), the Info tool will list all species, suitable or not for that specific cell (Fig. 7c, 1 is suitable, 0 is not suitable). With this binary map, the decision makers can easily find suitable species for a specific site and make correct decisions for the re-vegetation program.

5

Discussion

The Loess Plateau is a typical climatic and ecological transitional zone in China. Re-vegetation in this area faces diversified environmental conditions. The fragmented topography, mainly affecting the re-distribution of water and heat, further aggravates the difficulties in re-vegetation. Therefore, assessment of vegetation suitability or habitat is of great importance to re-vegetation practices. We have selected precipitation, temperature and soil as

environmental variables and in our use of GIS techniques and analyzed and mapped vegetation and habitat suitability. The result show that the method developed in our study is valuable in studies of vegetation and ecology on a large scale. Prediction of vegetation suitability depends on data availability. In our investigation, we have adopted a thin plate smoothing spline technique proposed by Hutchinson, which is more powerful than other interpolation methods for variables affected by topography and for the interpolation of precipitation and temperature (Hutchinson, 1991, 1995; de Boer et al., 2001). Given the limits on vegetation and soil data at our disposal, only a Boolean model was used to predict the vegetation suitability. However, much progress has been made in the prediction of vegetation. Newer models, such as GLM (generalized linear model), GAM (generalized additive model) or a combination of GIS, topography analysis based on DEM and non-linear static models have been applied to predict the spatial distribution of vegetation or vegetation suitability or habitat (Guisan et al., 1999; Guisan and Zimmermann, 2000; Rajan and Chaudhuri, 2001; Barry and Welsh, 2002; Guisan et al., 2002; Lane, 2002; Miller and Franklin, 2002; Anderson et al., 2003; Kauermann and Opsomer, 2003; Rigby and Stasinopoulos, 2005). Some investigators have developed

Fig. 5

Suitability maps of main species in the coarse sandy hill catchments of the Loess Plateau

Mapping vegetation suitability in coarse sandy hill catchment areas of the Loess Plateau 155

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Fig. 6

Frequency maps of species distribution

Fig. 7 Binary map for species and habitat suitability (case study in Suide County) (a) binary vegetation map for common species; (b) enlarged to show Info cursor; (c) Info displayed including number and names of species suitable to the grid cell

special modules to integrate the statistics and GIS to predict spatially the vegetation pattern (Lehmann et al., 2002). This paper just reported our initial work on the Loess Plateau. More efforts should be expanded in the future. Acknowledgements This work was supported by the Talent Cultivation Program ‘‘Western Light’’ of the CAS (2006HX01), the ‘‘National Basic Research Program of China (2007CB407203)’’, the State Science and Technology Support Project, the Vegetation Configuration Optimized and Sustained Management Construction Technology in the Loess Plateau (2006BAD09B03) and the National Natural Science Foundation of China (Grant No. 40301029).

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