Forest ecosystem restoration due to a national conservation plan in China

Ecological Engineering 37 (2011) 1387–1397

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Forest ecosystem restoration due to a national conservation plan in China D.Y. Yu ∗ , P.J. Shi ∗ , G.Y. Han, W.Q. Zhu, S.Q. Du, B. Xun State Key Laboratory of Earth Surface Processes and Resource Ecology, Key Laboratory of Environment Change and Natural Disaster, MOE/Academy of Disaster Reduction and Emergency Management, Beijing Normal University, Beijing 100875, China

a r t i c l e

i n f o

Article history: Received 6 December 2010 Received in revised form 4 March 2011 Accepted 27 March 2011 Available online 14 May 2011 Keywords: Land use/Land cover Landscape pattern Net primary productivity Deforestation Natural forest protection project

a b s t r a c t China has a long history of large-scale deforestation that has contributed to serious consequences such as frequent geological disaster, flood and soil erosion. It was only recently that forest management strategy shifted from the traditional harvesting orientation to a more balanced forest ecosystem management approach with a focus on conservation. To understand the effects of such a shift, on the forest dynamics especially since the implementation of Natural Forest protection Project (NFPP), this paper examined the case of Lushuihe region, a typical region of the northeast China forest zone. Land use and landscape pattern for the period of 1975–2007 were analyzed based on Landsat MSS and TM images. Net primary, productivity (NPP), estimated with the CASA productivity model, was used to assess the human impacts on the forest ecosystem function. The results showed a reversing trend of forest cover since 1988, from continuous decrease to rather rapid increase. From 1975 to 1988, due to reckless deforestation, the forest cover in the case region decreased about 10439.39 ha (8.54% of the study area). Forest cover of the region recovered from 77.68% in 1988 to 89.56% in 1999 and 92.33% in 2007. While the forest cover increased, landscape metrics indicate that human disturbance significantly altered the composition and structure of the forest landscape. NPP change indicated a continued decreasing trend until 2007, albeit at a slower pace since 1988. In 2007, while the decreasing trend of NPP was reversed, the forest structure was still inferior to that of 1975. Looking forward, diversifying and securing the livelihoods of the still growing local population that have been heavily dependent on the traditional forestry industry remain one of the key challenges as well as solutions for enhancing and managing the regional forest ecosystem structure and function in the region. © 2011 Elsevier B.V. All rights reserved.

1. Introduction Forest ecosystems have important functions and provide services that are essential to maintaining the life-support systems across all scales from local to global (Costanza et al., 1997; Johann and Carlos, 1999). Greenhouse gas regulation, water supplies and regulation, nutrient cycling, genetic and species diversity as well as recreation are only some examples of the services that forest ecosystems provide. The future of the world’s forests is of considerable interest from a variety of perspectives, including global change and biodiversity conservation (Zhang et al., 2002; Su et al., 2007; Bathurst et al., 2010; Schwarz et al., 2010). Worldwide, however, forest ecosystem services are stressed, threatened and even destroyed in the process of rapid urbanization, industrialization and economic development, of which deforesta-

∗ Corresponding authors at: State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China. Tel.: +86 1058800181. E-mail addresses: [email protected] (D.Y. Yu), [email protected] (P.J. Shi). 0925-8574/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.ecoleng.2011.03.011

tion is a major component (Sala et al., 2000). The pattern of forest exploitation is based on the utilization of resources with very little or no attention paid to the value of protected forests in providing ecological functions such as biodiversity maintenance, carbon storage, nutrients cycling and erosion control (Fearnside, 1997). Global deforestation is widely recognized as one of the world’s leading environmental problems (Brook et al., 2003; Sodhi et al., 2004). Deforestation obviously changes atmospheric components (Onil et al., 2008; Elisa et al., 2010) and accounts for 17–25% of all anthropogenic carbon emissions, contributing directly to ongoing concerns about global warming (Houghton, 1991; Bernardo et al., 2009). The impacts of deforestation are reflected at a regional level in ecosystem degradation (Derek and Arthur, 1997; Laurent et al., 2003; Justine et al., 2007; Menaka et al., 2008) and vastly elevated rates of soil erosion, landslide and an increased frequency and severity of floods (Sánchez-Azofeifa et al., 2002; Bruijnzeel, 2004; Sweeney et al., 2004). Deforestation not only produces a reduction in forest area, but also changes the landscape configuration (Skole and Tucker, 1993), which further contributes to habitat degradation, affecting the ecological conditions of the remaining forests with consequences over

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the fluxes of species, energy and matter (Gasparri and Grau, 2009). From a conservation perspective, a number of studies have shown that habitat loss is more important than fragmentation (Fahrig, 2003) and deforestation is listed as the biggest threat to global biodiversity (Sala et al., 2000; Foley et al., 2005). The world is faced with an unprecedented reduction of biodiversity that is occurring in virtually every ecosystem in the world (Walt et al., 2009). The loss of species is over two orders of magnitude higher than previously observed in the geologic record (Dirzo and Raven, 2003). How to protect existing forest resources as well as how to restore and regenerate damaged forest ecosystem thus have far-reaching implications for both climate change mitigation and biodiversity protection. The processes that drive deforestation are complex (Rudel et al., 2005). Proximate causes of deforestation include agricultural expansion, wood extraction and infrastructure expansion, and the driving forces combining economic, institutional, technological, cultural and demographic factors. A diversity of social, economical, and geographical conditions may influence the intensity of deforestation (Silvio et al., 2009; Daniel et al., 2011). Several studies at the global level concluded that a complex set of underlying social economic processes lead to the proximate causes of deforestation, implying that multiple rather than single factors are critical (Koop and Tole, 2001; Ehrhardt-Martinez et al., 2002; Rudel, 2002; Meyer et al., 2003; Barbier, 2004; Rudel et al., 2005; Robert, 2006). China has a long history of large-scale deforestation that has contributed to serious consequences such as frequent geological disaster, flood and soil erosion (Wang et al., 1999; Zhang et al., 2007; Liu and Min, 2010; Yan et al., 2010). Deforestation and ecosystem degradation were mainly driven by unreasonable economic development and rapid population increase (Stokes et al., 2010; Wang et al., 2010; Zhang and Dong, 2010; Lu et al., 2011). In China, a country where two-thirds of the land is made up of hills and mountains, erosion and landslides are largely the result of deforestation, bad farming practice and over-exploitation of resources in the last 50 years (Liu and Diamond, 2005; Stokes et al., 2008, 2010; Cao et al., 2009). In 1998, massive floods along the Yangtze River and waterways in the northeast claimed the lives of over 3000 people and led to more than US$ 12 billion in property damages and production losses and in response, the Chinese government has initiated two major projects, the Natural Forest Protection Project (NFPP) and the Sloping Land Conversion Project (SLCP) to protect the country’s fragile and fragmented environment (Xu et al., 2006). The NFPP covers 17 provinces (or municipalities) including Yunnan, Sichuan, Chongqing, Guizhou, Hubei, Jiangxi, Shanxi, Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang, Inner Mongolia, Jilin, Heilongjiang, Hainan and Henan in China (Fig. 1(a)). The aims of the NFPP are to ban logging in the southwest and to substantially reduce harvests in the northeast and other areas, and to strengthen management and protection in all natural forest regions (Xu et al., 2006). One of the important tasks of the project is that up to 2050, natural forest resources will be fundamentally restored; timber production will basically take advantage of plantation-based forest and the state forest zones will build a relatively complete forestry institution and rational forestry industry system. Specific measures of the project are to reclassify and zone the natural forest resources and adjust their management direction from the traditional timber harvesting to promoting natural resources conservation, and meanwhile, meeting the demand of the public for forest products. The large operating scales, tremendous public investments, and profound environmental implications of NFPP have drawn broad attention at home in China and abroad (Xu et al., 2006). It is believed that these projects, if properly implemented, can greatly benefit China and the world in combating some pressing environmental problems-water runoffs, soil erosion, landslides, flooding, and

desertification, as well as climate change and loss of biodiversity (Fang et al., 2001; Loucks et al., 2001; Xu et al., 2006). It is needed to study deforestation and restoration effects from broader perspectives of the deforestation process and consideration of historical patterns so as to make and rationalize restoration planning. However, past studies paid more attentions to introduction of the related policy issues. The current study selected Lushuihe region, a typical forest zone in northeast China and one of covered areas of NFPP as a case to detect the forest ecosystem dynamics under cumulative effects of human factors and evaluate the local forest ecosystem restoration under NFPP since 1998. 2. Study area 2.1. Biophysical conditions Changbai Mountain forest zone is one of the major forest areas in China, located in Jilin Province and the zonal vegetation is broad-leaved Korean pine forest. Due to historical human-induced disturbances, today the forest ecosystem mainly consists of secondary forests, with parts of residual borad-leaved Korean pine forest scattered in limited areas. Changbai Mountain was formed through volcanic eruptions and ash accumulation. With an altitude of 2691 meters of the main peak, the Changbai Mountain has a distinct vertical vegetation band spectrum covering vegetation types from the Temperate Zone to the Frigid Zone. Lushuihe Forest Bureau, one of the representative forest zones in the Changbai Mountain area, is located in the northwest of the main peak of Changbai Mountain (Fig. 1). It covers an area of approximately 1222.63 km2 between 127.29–128.02◦ N 42.20–42.4◦ E, with elevation ranging from 600 to 800 m. The climate of the region is temperate continental with significant seasonal variation of both temperature and precipitation. Mean daily temperature is about 0.9–1.5 ◦ C (1999–2003) and mean annual precipitation is 800–1040 mm. Thus the overall climatic condition in the region is marked by low temperature, relatively abundant precipitation and high humidity in the air, which limits the crop growth period to about 110 days, but provides favorable and conducive conditions for the formation of dense deciduous broad-leaved forest as well as coniferous forest. 2.2. The history of forest management strategy Since 1949, the forest management strategies in Lushuihe region can be broadly divided into three stages: before 1975, between 1975 and 1998, and since 1998. The first stage was characterized by small-scale timber harvesting and selective logging, with supplementary agricultural production. The second stage firstly experienced large-scale timber harvesting and wood production by clear-logging method and consequently massive deforestation during 1975–1988. Available forest resources for harvesting were nearly exhausted so that only small-scale and scattered logging occurred during 1988–1998, and forest ecosystems actually entered a passive recovery stage. Since the 1998, the forest management in the region has entered the third stage, whereas the focus has shifted from timber production to ecological conservation and natural forest protection through implementation of NFPP. 3. Materials and methods 3.1. Database development Based on availability and necessity, A Landsat Multi Spectral Scanner (MSS) satellite image with resolution of 57 m × 57 m for

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Fig. 1. Spatial distribution of NFPP and the location of study area in China (a) and its geographical outline (b).

vegetation growing season from 1975 and three Landsat Thematic Mapper (TM) satellite images with resolution of 30 m × 30 m from 1988, 1999 and 2007, respectively were used to produce four land cover maps during 1975–2007. Land cover maps were used to both track land use changes and guide the estimation of NPP of different land covers. We selected MSS image from 1975 and TM image from 1988 to measure the forest resource change before and after the large-scale deforestation in Lushuihe region throughout the late 1970s and most of the 1980s. The earliest time of available intact Moderate Resolution Imaging Spectroradiometer (MODIS) product (see below) during a year was in 2000, so we selected TM image of 1999 both to analysis the effect of passive recovery during 1988–1998 and estimate NPP in 1999. The TM image of 2007 was used to analysis the effect of active recovery since the NFPP implementation. All these data were reproduced at 30 m spatial resolution and projected to Universal Transverse Mercator (UTM) zone 52, by using World Geodical System-84 (WGS84) datum. 361 GPS groundtruth points describing vegetation type, land use and elevation were collected in the field investigation in July 2007. The GPS points were used for training site development to evaluate and classify land cover from the images. A supervised classification was performed on the 2007 Landsat TM image. A land cover map was derived with seven cover types including coniferous forest, broad-leaved forest, water, human infrastructure, agricultural land, deforestation area, and afforestation area (see Table 1 for description of the land cover types). More generally, the seven land covers can be combined into four types as coniferous forest, broad-leaved forest (including afforestation area), agricultural land and nonforest (including water, human infrastructure, deforestation area) so as to make the convenience of the following MODIS-based Normalized Difference Vegetation Index (NDVI) calculation for different land covers. Land cover maps based on images of 1975, 1988 and 1999 were also derived by supervised classification using the same categories of the land covers. We downloaded the available MODIS NDVI products for the year 2000 and 2007 from the EROS Data Center Distributed Active Archive Center (EDC DAAC) to calculate the MODIS-based NDVI

which is one of key inputs for calculating NPP in 1999 and 2007, respectively. These NDVI data are 16-day composites of atmospherically corrected maximal value at 250 m spatial resolution. We produced a time series of 32-day composite product of the maximal value. Given that each period covers 32 days, one year thus includes about 11 time series of composite product of maximal NDVI. Compared to the resolution of Landsat TM image, a MODIS image pixel is corresponding to a mosaic of about 69 Landsat TM pixels so that it is improper to allocate the same NDVI value in a MODIS image pixel to the potential different land covers in the 69 TM image pixels. We designed a linear-based model to obtain the NDVI for the three land covers (coniferous forest, broad-leaved forest, agricultural land) in a TM image scale as:

⎧ ⎪ ⎨ M1 = M11 P11 % + M12 P12 % + M13 P13 % ⎪ ⎩

M2 = M21 P21 % + M22 P22 % + M23 P23 %

(1)

M3 = M31 P31 % + M32 P32 % + M33 P33 %

Ma (a = 1, 2, 3) is the NDVI of the mth pixel of the MODIS image and Mab is the NDVI of the b (b = 1, 2, 3) types of land covers of TM image included in the mth pixel of MODIS data. Pab % means the area percentage of the bth land cover to the ath pixel of the MODIS image. We calculated the new MODIS-based NDVI for different land covers by moving a 3 × 3 slide window throughout the whole MODIS image which was implemented by designing program under the 6.2 version of IDL software. NDVI for non-forest types was set to zero value. Some researchers have proven that compared to climate factors, land use change has a larger impact on NPP in a short period (Elmore et al., 2008; Yu et al., 2009). But in order to filter the possible impact of climate factor fluctuation on NPP, meteorological data including the monthly (32 days) means of air temperatures, total precipitation and total solar radiation during 1999–2007 were required as inputs for NPP model. The meteorological data are based on records from six weather stations around the Lushuihe regions. The point data was then bi-linearly interpolated into spatial images with 30 m × 30 m resolution and sample projection to match Landsat MSS/TM image data and MODIS-based NDVI data.

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Table 1 Land covers considered in image classification and change detection. Land covers

Description

Coniferous forest

Forest areas with estimated 75% or more of the existing crown covered by planted or naturally growing conifer trees. The predominant species are: Pinus koraiensis, Abies nephrolepis, Picea koraiensis, Korean larch, etc. Forest areas with estimated 75% or more of the existing crown covered by broadleaf trees. The predominant species are: Tilia amurensis, Fraxinus mandshurica, Juglans mandshurica, Quercus liaotungensis, Phellodendron amurense, Betula costata, Betula platyphylla, Ulmus pumila, Populus davidiana, etc. River, reservoir and creek. Residential area, traffic system, and other built-up infrastructure for human use. Land for planting corn, soybeans, vegetables, etc. Residual and bared land after cutting timber from the original forest land. Natural and human regeneration in the deforestation area. Its spectral signature, shape, and texture are obviously different from other forests.

Broad-leaved forest

Water Human infrastructure Agricultural land Deforestation area Afforestation area

The observed NPP was provided by the former Ministry of Forestry of China which is now named as State Forestry Administration (SFA), P.R. China.

by the vegetation. ε(x, t) is LUE (g C MJ−1 ) of the vegetation. The algorithm of LUE is given as:

3.2. Evaluation on deforestation rationality

where Tε1 (x, t) and Tε2 (x, t) are temperature stress coefficients (see Potter et al., 1993; Field et al., 1995); Wε (x, t) is a moisture stress coefficient and εmax is a biome-specific light use efficiency factor that is estimated by daily meteorological conditions. εmax is the maximal light use efficiency of the specific biome under ideal conditions. In the original CASA model, the moisture stress coefficient (Wε (x, t)) is calculated by inputting many soil parameters, such as field moisture capacity, wilting coefficient, the percentage of soil sand, clay particles, and depth of the soil, etc.; however, it is difficult for us to acquire fundamental soil data in the study area. We used the regional evapotranspiration model (Zhou and Zhang, 1995) with monthly meteorological data (monthly total solar radiation, monthly average temperature and monthly total precipitation) as input parameters to estimate the regional moisture stress coefficient (Wε (x, t)). It is given by:

Slope is a major factor for inducing soil erosion processes in Lushuihe region. A digital elevation model (DEM) and maps with degree of slope were derived. The slope was selected as one of the principal limiting parameters for deforestation capacity and it was classified into the following ranges: (a) 0 to <15◦ ; (b) 15 to <25◦ ; (c) 25 to <35◦ ; and (d) 35◦ and more: unsuitable for deforestation. In China, mature timber stands can be deforested but the clear-logging approach should be strictly controlled. Generally, the logging area should be less than 5 ha, and in the flat areas the logging area can reach 20 ha (Shao et al., 2001). Deforestation and afforestation areas from 1988, 1999 and 2007 classification images were overlaid respectively with the slope map to calculate the corresponding deforested and afforested area in each of the slope ranges.

ε(x, t) = Tε1 (x, t)Tε2 (x, t)Wε (x, t)εmax

Wε (x, t) = 0.5 + 0.5 × r,

r=

3.3. Landscape metrics Landscape metrics are a useful tool to evaluate landscape pattern change. We select three landscape metrics and six patch metrics (O’Neill et al., 1988; Gustafson and Parker, 1992; Li and Reynolds, 1993; Hamazaki, 1992; Chen and Fu, 1996) to evaluate forest landscape pattern change at patch and landscape level, respectively during the study periods. The selected metrics are described in Table 2. Classified images were converted to shape format under the ERDAS IMAGINE Version 8.5 environment (ERSI, USA). The polygon themes so generated, were exported to ArcGIS Version 8.3 (ESRI, USA) to get the attribute information of each land cover in order to calculate the patch and landscape metrics. 3.4. NPP model

E1 (x, t) E2 (x, t)

(4)

where E1 (x, t) is the estimated evapotranspiration and E2 (x, t) is potential evapotranspiration. E1 (x, t) =

P(x, t) × R(x, t) × [P(x, t)2 + (R(x, t))2 + P(x, t) × R(x, t)] P(x, t) + R(x, t)] × [P(x, t)2 + R(x, t)2 ]

In Eq. (5), P(x, t) is monthly precipitation (mm m−2

(2)

where NPP(x, t) represents NPP in the geographic coordinate of a given location x and time t. APAR(x, t) (MJ m−2 mon−1 ) is the APAR

(5)

mon−1 ) and R(x,

t) expresses net solar radiation (MJ m−2 mon−1 ). E2 (x, t) =

E1 (x, t) + E0 (x, t) 2

(6)

where E0 (x, t) = 16 × [10 × T(x, t)/I(x)]a(x) . ˛(x) and I(x) can be computed by the following equation (7): a(x) = (0.675I(x)3 − 77.1I(x)2 + 17920I(x) + 492390) × 10−6 ,

The spatially explicit global terrestrial carbon model CarnegieAmes-Stanford Approach (CASA) ecosystem model is robust in describing spatial and temporal NPPs patterns (Potter et al., 1993). Based on estimating light use efficiency (LUE), the CASA model is a process-based model and appropriate to estimate NPP on a global or regional scale. We used CASA ecosystem model to examine forest ecosystem NPP change. In the CASA model, NPP is the product of modulated Absorbed Photosynthetically Active Radiation (APAR) and a LUE factor (Potter et al., 1993), namely NPP(x, t) = APAR(x, t)ε(x, t)

(3)

12

(7)

1.514

[T (x, t)/5)] . with I(x) = i=1 I(x) is the total heat index in a year and T(x, t) is the monthly average temperature (◦ C m−2 mon−1 ).εmax for different vegetation types was calculated by formula (8): E(x) =

j 

(mi − ni εmax )2 or E(x) =

i=1

+

j 

j  i

mi 2

x ∈ [l, u]

j 

ni εmax − 2

mi ni εmax

i=1

(8)

i=1

where, E(x) is the error function between the observed NPP and the estimated NPP; i represents the sample number of the vegeta-

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Table 2 Description of landscape metrics calculated for forest landscape pattern. Metrics

Scale

Algorithm

Shannon-Weaner diversity (SWD)

landscape level

SWD = −

M 

Pi log Pi

i=1

Dominance (D)

landscape level

D = Hmax +

Fragmentation (F)

landscape level

F=

Patch density (PD)

Patch level

PDi =

Mean patch size (MPS)

Patch level

MPSi =



M 

(pi ) log(pi ); Hmax = log(M) i=1

ni



A ni Ai

; Ai =

k

j=1

aij

10000 aij

ni

× 10−4



k

Patch size coefficient of variation (PSCV)

Patch level

PSCVi =

PSSDi MPSi

; PSSDi =

k

Patch level

MSIi =

Mean patch fractal dimension (MPFD)

Patch level

MPFDi =

Patch isolation (PI)

Patch level

PIi =

Di Si

√

j=1

Mean patch shape (MSI)

j=1

(0.25Pi j /

k

ni

j=1

aij )/ni ]2

× 10−4

aij )

ni

j=1

; Di =

k

[aij −(

1 2

[2In(0.25Pij )/In(aij )]

nni

i

A

; Si =

Ai A

Note: In the above formulas, ni and k represent the patch number of the i forest type; Ai (hm2 ) is the total area of the i forest type; A (hm2 ) is the total forest area; aij (m2 ) means the area of jth patch of the i forest type; Pi j (m) means the perimeter of jth patch of the i forest type; pi is the area percentage of the i forest type to the total forest.

tion, mi is the observed NPP and can be considered an independent variable and εmax is a dependent variable. ni is a constant and can be calculated by the product of APAR, temperature and water stress factors illustrated above. l and u are the lower and the upper light use efficiency of the vegetation, respectively. When the error between the observed NPP and the estimated NPP is the minimal and then εmax will be obtained for different land covers. The CASA model we used in this paper requires three key inputs: (1) remote sensing inputs (land cover, MODIS-based NDVI); (2) monthly surface meteorological inputs (monthly solar radiation which was used to estimate APAR, monthly average temperature and monthly total precipitation which were used to estimate temperature stress coefficients (Tε1 , Tε2 ) and moisture stress coefficient (Wε ); (3) biome-specific coefficients (observed NPP, ε and εmax ). Based on the land cover, observed NPP, temperature stress coefficients and moisture stress coefficient, the maximal light use efficacy (εmax ) of the vegetation type was estimated to produce light use efficiency (ε) of the vegetation type, which was then used with the APAR to predict monthly NPP. Final estimation of annual NPP was obtained by adding the 11 time series of NPP in a year. 4. Results 4.1. Land use change during the study periods Based on the ground-truthing points collected in the field during 2007, the accuracy obtained for the final classification of the 2007 image was 89.2%. The final classification accuracies of image in 1975, 1988 and 1999 were 85.6%, 86.9% and 88.6%, respectively. The land use maps for 1975, 1988, 1999 and 2007 were presented in Fig. 2 and the areas under each for the seven land use types were shown in Table 3. From Table 3, the forest cover (including coniferous forest, broad-leaved forest and afforestation area) in 1975, 1988, 1999 and 2007 were 86.22%, 77.68%, 89.56% and 92.33%, respectively. During 1975-1988, the forest cover greatly decreased by 8.54% (about 10439.39 ha). This trend was reversed during the second (1988–1999) and the third (1999–2007) study period and forest cover increased by 18.85%, or 17902.67 ha during 1988–2007. Forest cover and forest area change during the thirtytwo years also reflected the forest management variation. During

the 1970s, Lushuihe Forest Bureau was newly established, and the local residents were focusing on building the infrastructure. Smallscale timber harvesting was the core of forest management policy, and selective logging was the main way of logging. No obvious logging areas were found in the 1975 MSS image. Agricultural production was secondary during that period. Crops such as corn, soybeans and vegetables were only grown to meet farmer’s own needs, and there was little interest to develop agricultural production because of the low crop yield due to the short frost-free growing period. During the 2007 interview with local residents and local government officials, we learned that there was no major agricultural land planed since the change of forest management strategy from the 1980s and onward. This was confirmed by the three TM images from 1988, 1999 and 2007 used in this study. The farmland in 1975 image mainly distributed around human residential areas (Fig. 2) and the registered agricultural population was less than 5000 people. Up until the mid 1970s, the human disturbance to the forest ecosystem in the region was relatively small, so the forest ecosystem structure and function were relatively intact. More specifically, coniferous forest, the zonal vegetation, made up 60.87% of the total study area and 70.5% of the total forest area. Since the implement of “Chinese Open and Reform” which stated in 1979, with rapid economic development in China, the rapidly increasing demand for domestic timber supply as well as export has driven many stateowned forest zones like Lushuihe region to extensively log forest resources. From the early 1980s to early 1990s, large-scale timber production and wood processing were the dominant forest management strategy in the Lushuihe region. Timber harvesting mode changed from the first large-scale clear-logging to the selective logging by large timber diameter, and then back to large-scale clear-logging. As the clear-logging proportion was so large that forest cover greatly decreased from 86.22% in 1975 to 77.68% in 1988 and this was especially serious for coniferous forest which decreased by 44% from 1975 to 1999, with annual mean decline of 1365.67 ha (Table 3). Up to 1988, residual deforestation area was equivalent to 13.53% of the total area and Lushuihe had cumulatively produced 6.27 million m3 of wood (according to the local produce document). Due to the depletion of available large timber diameter of forest resources for logging, deforestation area

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Fig. 2. Land covers of 1975, 1988, 1999 and 2007 in Lushuihe Region.

significantly decreased during 1988–1999 and forest ecosystem entered into a passive recovery stage with deforestation area in 1999 decreased by 74.2% than that in 1988. As the covered area of the NFPP, the Lushuihe region has taken measures to reduce timber harvesting and restore forest

resources since 1998 and these measures have played a significant role. Compared with 1988, forest cover in 2007 increased by 14.65% (Table 3). In particular, compared with 1999, the area of native zonal coniferous forest increased by about 26.54% in 2007.

Table 3 Area and the percentage of different land covers during 1975–2007. Land covers

Coniferous forest Broad-leaved forest Water Human infrastructure Agricultural land Deforestation area Afforestation area

1975

1988

1999

2007

Percent change in land use

Area (ha)

%

Area (ha)

%

Area (ha)

%

Area (ha)

%

1975–1988

1988–1999

1999–2007

1975–2007

74409.66 30982.60 562.09 9115.20 7171.59

60.87 25.35 0.46 7.46 5.87

50648.31 37904.58 758.25 9986.52

41.43 31.01 0.62 8.17

41633.68 64857.76 632.46 7870.90

34.06 53.06 0.52 6.44

52685.30 59459.38 461.89 7053.57

43.10 48.64 0.38 5.77

−31.93 22.34 34.90 9.56

−17.80 71.11 −16.59 −21.18

26.54 −8.32 −26.97 −10.38

−29.20 91.91 −17.83 −22.62

16535.04 6408.45

13.53 5.24

4266.12 2980.24

3.49 2.44

1861.68 719.33

1.52 0.59

−74.20 −53.50

−56.36 −75.86

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Table 4 Unchanged and gained percentage of main forest types between 1975 and 2007. Land covers

Percent of land use in 1975 (%) Unchanged in 2007

Coniferous forest Broad-leaved forest

56.01 77.2

Net gain/loss (%) Changed to other classes

Gained from other classes

43.99 22.8

14.73 114.75

Table 3 also indicates that the area of built-up areas reached the peak in 1988 and had been in decline until 2007, but the population has increased from 31,090 in 1988 to 41,692 in 2005 (Statistical Yearbook of Lushuihe region, 1988, 2005). The logical interpretation may be that although population increase may encroach on forest lands, with the cessation of large-scale deforestation, some more human infrastructures originally used for timber logging operation, transport and storage were abandoned and were covered by vegetation until now. Table 4 shows that compared with 2007, 43.99% of coniferous forest in 1975 was lost to other land use types and gained 14.73% from others. The net loss of coniferous forest was 29.26%. On the contrary, with 77.2% of 1975 unchanged in 2007 and 114.75% of 2007 gained from other types, broad-leaved forest had a net gain rate of 91.95% after the negative loss counteracting with the positive gain. It should be pointed out that the change percentage of coniferous forest may be more objective than that of broad-leaved forest. The regeneration succession of most broad-leaved forests is relatively faster that the former. It usually takes fifty years or more for coniferous forest like Pinus koraiensis, Abies nephrolepis and Picea koraiensis to dominate the forest canopy in Lushuihe region, but only twenty years or less for broad-leaved tree species to finish this process (Wang, 1957; Liu, 1957), so that their change may not be detected in the momentary TM images and thus the change percentage of broad-leaved forest may be underestimated. We extracted deforestation and afforestation areas (not including the parts may be encroached by infrastructure sprawl) in images from 1988, 1999 and 2007, respectively, to unit them with overlaid parts only spatially calculated for one time, and found that the total logging area was 36672.03 ha, about 34.8% of the forest area in 1975. 4.2. Deforestation evaluation The Lushuihe region is basically flat and about 82.1% of the area has slope of less than 15◦ .The areas within slope scope of 15–25◦ , 25-35◦ and more than 35◦ made up 12.58%, 3.98% and 1.34% of the whole area, respectively. 80.60%, 13.36%, 4.22% and 1.83% of the logging areas during 1975–2007 fell into the slope ranges of <15◦ , 15–25◦ , 25–35◦ and more than 35◦ , respectively. So only from the perspective of slope factor was the logging location basically reasonable,with about 1.34% of the logging areas with slope over 35◦ where soil erosion easily occurred. We extracted a total of 13828 logging patches from 1988, 1999 and 2007 images, which are reflected in Fig. 2. Each area of 91.5% of the logging patches was less than 5 ha, and their total area comprised 25.52% of the total harvested areas during 1975–2007. The total area of logging patches with each area within 5–25 ha was 10188.26 ha, about 31.1% of the whole logging area. However, only 1.27% of logging patches with each area more than 20 ha took about 43.4% of the total harvested area, and especially, the area of the largest logging patch totaled to 866.27 ha which occurred in 1988. So these super large logging patches seriously broke the technical specification for harvesting. In fact, past forest harvesting often occurred for excellent tree species and large diameter timber, so it was easily deduced that the heavy and frequent human deforestation must weaken

−29.26 91.95

the function and stabilization of the forest ecosystem during 1975–1988. 4.3. Changes of forest landscape pattern Forest landscape pattern metrics are listed in Table 5. Compared with other three study years, the Shannon-Weaner diversity metric in 1975 was the lowest, but contrary to this trend, dominance in 1975 was the highest. The results of the two metrics indicated that forest landscape in 1975 was relatively simple but it occupied a more dominant position in the regional landscape. In 1975, the main forest type was coniferous forest, taking about 60.87% of the total area (Table 3). With the deforestation increasing, the percentage of coniferous forest greatly decreased to 34.06% in 1999, so human disturbances had significantly changed the forest landscape composition and structure during the study periods. The fragmentation of the forest landscape in 1975 was the lowest and with large-scale of timber clear-logging implementation during the 1980s, fragmentation metric increased. Although timber harvesting in 1999 greatly decreased, the regenerative forests in the original logging areas were still in an intersected status with the surrounding stand, so the fragmentation extent was the highest. We found that forest fragmentation was mitigated until 2007. The landscape metrics at patch level are illustrated in Table 6. Patch density, mean patch size and patch isolation all indicated that in 1999, the fragmentation of coniferous forest was the highest and the percentage of coniferous forest reached the lowest value (Table 3). The least area percentage of broad-leaved forest occurred in 1988 (Table 3). The patch size coefficient of variation indicated that spatial patch area difference for both coniferous and broadleaved forest was the biggest in 2007 and the least in 1988. Mean patch shape and mean patch fractal dimension illustrated that due to few human disturbances in 1975, the mean edge shape of the forest patches was the most complex and it was helpful to form diverse habitats. There may be three reasons for forest fragmentation: (1) with human disturbance (mainly deforestation) strengthening, the original larger forest patches were cut into many smaller ones or the original natural homogeneous landscapes were modified into many heterogeneous parts; (2) climate change may affect the structure and succession process of the forest community and thus causes fragmentation, but compared with human-induced disturbance, it usually take longer time (Yu et al., 2005); (3) during the study periods, forest community succession marked by strong tree species competition must take place, and this was especially true for broad-leaved forest. So we could conclude that human-induced disturbances played a more important Table 5 Forest landscape pattern metrics during 1975– 2007. Year

Shannon-Weaner diversity

Dominance

Fragmentation

1975 1988 1999 2007

0.874 0.985 0.965 0.997

0.126 0.015 0.033 0.003

0.034 0.134 0.214 0.098

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Table 6 Forest patch metrics during 1975–2007. Forest types

Year

Patch density Mean patch size Patch isolation Patch size coefficient of variation Mean patch shape Mean patch fractal dimension

Coniferous forest

1975 1988 1999 2007

0.02 0.08 0.29 0.10

52.82 12.83 3.50 9.77

0.08 0.18 0.43 0.23

19.07 14.09 19.78 31.36

1.74 1.45 1.03 0.70

2.00 1.06 1.26 1.53

Broad-leaved forest

1975 1988 1999 2007

0.07 0.21 0.17 0.09

14.17 4.81 5.93 10.53

0.24 0.35 0.26 0.21

9.00 7.74 27.68 38.20

1.85 1.35 1.24 1.29

1.63 1.04 1.03 1.04

role in causing forest fragmentation. The own succession of forest community also can not be ignored and meanwhile climate change promoted this process. The study area is located in the Changbai Flora area, and the zonal vegetation is broad-leaved Korean pine forest. When carried out field survey in 2007, we found that broad-leaved Korean pine forest accounted for a very little percentage. The forest ecosystem had strayed from the original stable status, and a new succession process has already begun. Therefore, in the subsequent forest management practices, forest logging on the residual natural forest and at the sites with larger slope should be strictly prohibited. Selective logging for inferior trees can be chosen to promote the health of the forest ecosystem, but a large area of clear-logging must be avoided in order to make the fragile ecosystem to restore and strengthen self-regulation ability. 4.4. NPP change Table 7 shows the estimated value of mean and total NPP for Lushuihe region which were calculated by using the CASA model. The calculated NPP were in accordance with that of Zhu (2005) who estimated the NPP based on NOAA/AVHRR NDVI data (with spatial resolution of 8 km × 8 km) for China and gained that mean NPP of broad-leaved forest in this region ranged between 700 and 850 g C/m2 , and mean NPP of coniferous forest was about 550 g C/m2 . The mean NPP for coniferous forest, broad-leaved forest and afforestation area increased between 1999 and 2007. Mean broadleaved NPP increased most, about 82.44 g C/m2 , or being 11.68% of that in 1999, which indicated that forest quality per area improved during 1999–2007. The earliest time of available MODIS products in Lushuihe region was in 2000, so we cannot directly calculate NPP of different land covers for 1975 and 1988 based on MODIS-based NDVI. The total NPP values in 1975 and 1988 were estimated according to mean NPP in 2007 and 1999, respectively. This may underestimate and overestimate the total NPP in 1975 and 1988, respectively, so it may be a conservative assumption to estimate forest productivity loss during the large-scale deforestation period of 1975–1988. Total NPP of coniferous forest in 1975 was the largest, about 425.82 Gg Carbon (1 Gg = 109 g) and made up 63.55% of the total NPP. The forest area and NPP illustrated that forest structure and function in 1975 was more rational and zonal coniferous species occupied larger proportion than that in other periods. During 1975–1988 and 1988–1999, coniferous forest NPP decreased by 33.06% and 17.8%, respectively, which was mainly caused by timber harvesting, and this trend was reversed in 2007, but the forest ecosystem structure was still inferior to that in 1975, with NPP of broad-leaved forest (468.78 Gg C) occupying about 60.56% of the total area. The spatial difference of NPP for Lushuihe region derived from CASA model between 1999 and 2007 is shown in Fig. 3. The dark

blue polygons of obvious increased NPP mainly scattered among the deforestation areas in 1999. The areas with deceased NPP mainly distributed in the concentrated areas with heavy human activities. According to the reaction equations of photosynthesis and respiration, vegetation absorbs 1.62 g CO2 to produce 1 g carbon of dry matter and releases 1.2 g O2 in the process. Between 1975 and 1988, the forest ecosystems in Lushuihe lost 82.94 Gg of carbon that is equivalent to reducing carbon sink ability of 134.36 Gg and a reduction in release of 99.53 Gg O2 . The heat contained in 1 g carbon of dry matter equals that contained in 0.00067 g standard coal, so 82.94 Gg NPP is equivalent to the heat loss contained in 5.56 × 104 tons of standard coal. NPP is the source of the complex food chain of the forest ecosystem and frequent human disturbances damaged the survival environment for many plants and animals. Up to 2007, NPP increased by at least 187.04 Gg carbon, or 31.87% of that in 1988, corresponding to 303 Gg of net dioxide carbon sequestration and the regional forest ecosystem recovered to a certain extent.

5. Discussion Ecological restoration is the process of assisting the recovery of an ecosystem that has been degraded, damaged, or destroyed and the goal of this process is to emulate the structure, functioning, diversity and dynamics of the specified ecosystem using reference ecosystems as models (Lewis, 2005). Ecological engineering involves creating and restoring sustainable ecosystems that have value to both humans and nature (Mitsch and Jørgensen, 2004) and is characterized by three primary goals (Lewis, 2005): (1) the restoration of ecosystems that have been substantially disturbed by human activities, (2) the development of new sustainable ecosystems that have both human and ecological value, and (3) the realization of (1) and (2) in a cost-effective way. Ecosystem restoration assessment should correspond to restoration objectives, and suitable variables should be selected to identify the degree to which those restoration objectives are approached at any given time point (Hobbs and Norton, 1996). In this paper we shed light on forest ecosystem structure, functions and naturalness as the objectives to which NFPP is aiming and towards which restoration practices should strive. The results from Lushuihe case told us that forest ecosystem structure and function suffered from serious human disturbance, and the NFPP played an important role in forest ecosystem restoration during the study period. The NFPP has made substantial progress across the whole covered areas. Hu and Liu (2006) estimated that the increase of carbon storage through the NFPP was 21.32 Tg (1 Tg = 1012 g) over the period of 1998–2002. The carbon sequestration from reducing the timber production was about 22.75 Tg. The total storage of carbon sequestration of the covered areas of NFPP was 44.07 Tg, equaling to about 1.2% of total national industrial CO2 emission during 1998–2002. NFPP reduced timber harvests from natural (or mixed)

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Table 7 Estimated values of mean and total NPP in 1975, 1988, 1999 and 2007 for Lushuihe region.

a b

Mean NPP (g C/m2 )

Coniferous forest

Broad-leaved forest

Afforestation area

1999 2007 Percentage change in unit NPP (%)

562.79 572.26 1.68

705.96 788.40 11.68

538.51 542.92 0.82

Total NPP (Gg C) 1975a 1988b 1999 2007

425.82 285.04 234.31 301.50

244.27 267.59 457.87 468.78

34.51 16.05 3.91

670.08 587.14 708.23 774.18

Percentage change in total NPP (%) 1975–1988 1988–1999 1999–2007

−33.06 −17.80 28.67

9.55 71.11 2.38

−53.50 −75.67

−12.38 20.62 9.31

Total

Total NPP in 1975 was calculated by multiplying the area of forest area in 1975 with mean NPP in 2007. Total NPP in 1988 was calculated by multiplying the area of forest area in 1988 with mean NPP in 1999.

forests from 32 million m3 in 1997 (Stokes et al., 2010) to 1.48 million m3 in 2009 (CFY, 2010), or decreased by 95.4% of that in 1997. The cumulative reduction in timber harvest and increase in forest stock during 2000–2009 totaled to 220 million m3 and 725 million m3 , respectively; forest area and forest cover increased by 100 million ha and 3.7%, respectively. By the end of 2009, China had invested nearly 152 billon US dollars (1US$ = 6.57 RMB yuan) in the NFPP. 5.85 million hectares had been afforested, including artificial planting of 2.66 million ha and aerial seeding of 3.19 million ha; furthermore, 12.07 million ha of mountain closure had been executed for forest regeneration. In the natural forests, the targets for log-

ging bans, harvest reductions, and resource protection have been largely met (CFY, 2003), and totally, the covered areas of NFPP have gained significant improvement in ecological conditions, obvious mitigation of soil erosion and sediment into the Yangtze River and the Yellow River (CFY, 2010). Since 1998, logging bans and harvest reductions have displaced 0.62 million of loggers and other forest employees who were transferred to afforestation and other forest management activities, retired, or laid off. Ecotourism and other development activities—such as dairy, cattle and deer farming, cultivating annual crops, mushrooms, fruits, and ginseng, and collecting wild herbs,

Fig. 3. NPP difference map of Lushuihe region between 1999 and 2007.

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nuts, and vegetables—have gained broad recognition (Xu et al., 2006). Despite these achievements, the NFPP still faces some hard challenges. These challenges include heavy reliance on state finance, the lack of inter-agency cooperation, the insufficient consideration of local interests, the neglect of appropriate practices, and the rigidity and inconsistency of certain policy measures (see Xu et al., 2006). Some researchers have worried that the NFPP seems successful, but care must be taken when interpreting the above-described progress in the sites whereby natural forest has been destroyed and replanted with exotic timber species (Trac et al., 2007). NFPP promotes afforestation at all costs, thus encouraging the shift from natural vegetation to man-made forests as a fast restoration of the landscape (Cao et al., 2009). Little thought has been given to how best control soil erosion and slippage (Stokes et al., 2010). The poor selection of appropriate species is common at the country level (Bennett, 2008). Some exotic timber species, such as Eucalyptus sp., Cryptomeria japonica and Jatropha curcas, may not be the best solution for soil conservation on steep terrain (Stokes et al., 2010). Understorey vegetation is often damaged or cannot grow in shade conditions, resulting in increased erosion rates (Genet et al., 2008). Some decrease in Chinese wood production is now compensated through the importation of logs from other countries and if this will cause environmental problems is unclear now (Sun et al., 2004). Native species should be preferred, and although the central government has ordered that trees be planted in the NFPP, it would also have been wise to consider the role of grasses and herbaceous species for soil conservation (Stokes et al., 2010). We must acknowledge that the NFPP is involved in too many factors, including not only technical, but also social, economical and even political issues. It is difficult for all these factors to be at the optimal or perfect status; however, most situations can be improved through technical rectification, compromise and seeking a win–win scheme among the stake holders. On October 29, 2010, on behalf of the central government, Wen Jiabao, the premier of P.R. China, declared that China will implement the second-phase project of the NFPP during 2011–2020, with the main aims being to increase 7800 hectares of forest area, 11 billion m3 forest stock and 416 million tons of forest carbon sink ability, and meanwhile to further improve the people’s livelihood in forest areas. Certainly, the experiences and lessons from the first-phase project should be considered in order to avoid making the same mistakes. Aside from the original covered areas, the second-phase project will cover an additional 11 counties (or cities, districts) around the Danjiangkou reservoir, which is located at the junction of Henan Province and Hubei Province and is the important source of water supply to Beijing City. The international community must be more actively involved in assisting and facilitating the execution of the NFPP. China’s NFPP has provided a paradigm for the world to deal with the intractable human-land relationship. 6. Conclusion Forest dynamics in Lushuihe have been shaped by a range of factors, such as climate change, succession of forest community and human-induced disturbances. Human-induced disturbances were the more important force causing forest dynamics during 1975–2007. Human impact in Lushuihe region until the late twentieth century was mainly related to the use of timber, guided by internal and external interests that molded the fragmental and degraded status of the forest ecosystem. Since the implementation of the NFPP in 1998, the orientation of forest management strategy has changed from the traditional timber harvesting to pro-

moting forest conservation, and the regional forest ecosystem has recovered to a certain extent. Drivers of land-use change in the area were strongly influenced by changes in forest management strategy during the study periods, which themselves were influenced by institutional and economic factors. The forest dynamics in the Lushuihe region were in rhythm with Chinese forest management strategy transition. The study periods can be divided into three stages: (1) large-scale damage of forest ecosystem (1975–1988); (2) passive recovery (1988-1998) and (3) positive forest ecosystem restoration (1998–2007). Forest covers in 1975, 1988, 1999 and 2007 were 86.22%, 77.68%, 89.56% and 92.33%, respectively, and decreased by 8.54% during 1975-1988, about 10439.39 ha, which was caused by much unreasonable deforestation. This trend was reversed and deforestation greatly decreased during 1988–2007. The forest landscape pattern indicated that frequent human disturbances had significantly changed the composition and structure of the forest ecosystem. During the periods of 1975–1988 and 1988-1999, NPP decreased by 33.06% and 17.8%, respectively, which were mainly caused by timber harvesting, and this trend was reversed in 2007, but the current forest ecosystem structure and function was still inferior to that in 1975. The problem that must be confronted is of how to provide survival or opportunity for the local growing population who heavily depend on the traditional forestry industry in the past and until now. There may be two ways to deal with these issues: (1) reduction of population size by controlling birth rate, or ecological migration from the traditional forestry zone to other sites; (2) full use and development of forest ecosystem services to meet the needs of survival and development. From a macro-perspective, we studied the forest ecosystem dynamics which were impacted by the intertwining humaninduced and natural factors; however, further details of forest ecosystem parameters such as the responses of microorganism and animal, etc. to the forest ecosystem degeneration and restoration have not been dealt with, and are crucial and should be discussed in future work. Acknowledgments This research was funded by the Fundamental Research Fund for the Central Universities, the Program of National Natural Science Foundation of China (40801211) and Project of State Key Laboratory of Earth Surface Processes and Resources Ecology, Beijing Normal University. Special thanks are given to the referees and the editors for their instructive comments, suggestions and editing for the manuscript. References Barbier, E.B., 2004. Agricultural expansion, resource booms and growth in Latin America: implications for long-run economic development. World Development 32, 137–157. Bathurst, J.C., Bovoloa, C.I., Cisnerosb, F., 2010. Modelling the effect of forest cover on shallow landslides at the river basin scale. Ecol. Eng. 36, 317–327. Bennett, M.T., 2008. China’s sloping land conversion program: institutional innovation or business as usual? Ecol. Econ. 65, 699–711. Bernardo, S.R., Kerry, T., Brendan, F., Roberto, S., Andrew, L., 2009. Reducing emissions from deforestation—The “combined incentives” mechanism and empirical simulations. Global Environ. Chang. 19, 265–278. Brook, B.W., Sodhi, N.S., Ng, P.K.L., 2003. Catastrophic extinctions follow deforestation in Singapore. Nature 424, 420–423. Bruijnzeel, L.A., 2004. Hydrological functions of tropical forests: not seeing the soil for the trees? Agr. Ecosyst. Environ. 104, 185–228. Cao, S., Chen, L., Yu, X., 2009. Impact of China’s Grain for Green project on the landscape of vulnerable arid and semi-arid agricultural regions: a case study in northern Shaanxi Province. J. Appl. Ecol. 46, 536–543. Chen, L.D., Fu, B.J., 1996. Analysis of impact of human activity on landscape structure in yellow river delta–A case study of Dongying Region. Acta Ecol. Sin. 16 (4), 337–344.

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