De(Re)Forestation and Climate Warming in Subarctic China

Applied Geography 32 (2012) 281e290

Contents lists available at ScienceDirect

Applied Geography

journal homepage: www.elsevier.com/locate/apgeog

De(re)forestation and climate warming in subarctic China

Jay Gao a,b, Yansui Liu a,* a Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China b School of Environment, University of Auckland, Auckland, New Zealand

abstract

Keywords: Although tropical deforestation bears a close relationship with climate change, its exact contribution to Deforestation climate warming and its threshold of exerting a noticeable influence remain unknown. This study Climate warming attempts to bridge this knowledge gap by analyzing deforestation data of Heilongjiang Province, China in Scale effect relation to climate data. It is found that forest cover was reduced from 238,335 km2 in 1958 to Remote sensing 2 2 Heilongjiang 216,009 km in 1980, and further to 207,629 km in 2000. During this period the provincial annual temperature rose by 1.68 C, against the nation-wide warming of 0.99 C during the same period. At the provincial level the observed deforestation caused a warming in the vicinity of 0.69 C. This warming does not bear any definite relationship with latitude and elevation. At the local scale, deforestation is related inversely to the rise in decadal temperature in the form of DT ¼0.013DF þ 0.4114 (R2 ¼ 0.30). There is a positive relationship between the accuracy (R2 value) of predicting climate warming from deforestation and its severity. The critical threshold for deforestation to exert a noticeable impact on climate warming (e.g., R2 ¼ 50%) appears to be 5 km2. The amount of forest cover at the beginning of a period can inhibit temperature rise, but its exact effect on climate warming is difficult to quantify. Ó 2011 Elsevier Ltd. All rights reserved.

Introduction as one of the most significant components in climate change scenarios. Deforestation exerts a number of regional and local It is extremely important to study climate change as it exerts climate effects. They include increases in temperature, a decrease in a significant impact on the environment, society, and people’s lives. water vapor mixing ratio (Sen, Wang, & Wang, 2004), reduced The climate of the Earth has been on a warming trend over the last precipitation (Werth & Avissar, 2005), and a change in the water century. Globally, temperature has risen by an average of 0.6 C cycle (Houghton, 1990). Large-scale deforestation has been linked since the industrial revolution. It has been repeatedly demon- to an increase in near-surface air temperature (Sampaio et al., strated that climate warming over the last 50 years is attributed 2007), projecting a warmer climate. Complete conversion of the mostly to anthropogenic activities (IPCC, 2007). The main source of broadleaf evergreen forests of South America, central Africa, and global climate change is human-induced changes in atmospheric the islands of Oceania to grasslands would result in highly signifi- composition as a result of fossil fuel burning. At the local and cant local responses (Findell, Knutson, & Milly, 2006). Deforestation regional scales, urbanization and land use changes are important in the Indochina Peninsula could be one of the major causes of contributors (Karl & Trenberth, 2003). climate change in the region. Complete Amazonian deforestation Land cover change, especially vegetation cover, affects climate can result in changes in the climate distant from the region of via alteration of the physical characteristics of the land surface such deforestation (Gedney & Valdes, 2000). At the local scale defores- as albedo and roughness (Brovkin, 2002). Increased surface albedo tation may increase temperatures (Molion, 1989). Current land use reflects more solar radiation back into space (Zeng & Neelin, 1999), (modified by anthropogenic activities) influences local climate and affects the redistribution of solar energy on and near the through the reinforcement of the monsoon circulation in both Earth’s surface. This change in energy budget alters near-surface winter and summer (Gao, Zhang, Chen, Pal, & Giorgi, 2007). In temperature, precipitation, and evaporation at a local scale. Land comparison with other forms of land cover change, deforestation use change, such as clearance of forest for agriculture, is recognized affects climate change in more than one way because of the large amount of CO2 trees can store. Past research on the interactive relationship between defores- “ ” * Corresponding author. Tel.: þ86 10 64889037; fax: þ86 10 64857065. tation and climate change has concentrated mostly on hot spots E-mail addresses: [email protected] (J. Gao), [email protected] (Y. Liu). where the interaction is the most significant: North Africa and

Amazon forest (Shukla, Nobre, & Sellers, 1990). Dissimilar to these between climate warming and deforestation at the regional and regions, the subarctic is highly responsive and vulnerable to climate local scales through remote sensing and geographic information change. A small rise in temperature can exert a profound influence system (GIS); (2) to analyze the sensitivity of climate warming to on the ecosystem and agricultural production due to the growing the severity of deforestation at the local scale; and (3) to explore the conditions being at the climate ceiling. Thus, it is more important to influence of environmental variables on the observed warming. The study the impact of deforestation on climate in this special hypothesis of this study is that recent changes in deforestation are geographic zone. A common method of studying the relationship large enough to cause noticeable and detectable climate change. between deforestation and climate change is via simulation based on the general circulation model (Taylor, Lambin, Stephenne, Study area Harding, & Essery, 2002). All simulations indicate a significant increase in surface temperature in scenarios of large-scale defor- The study area is Heilongjiang Province located in Northeast estation in Amazonia (Correia, Alvalá, & Manzi, 2008). In general, China (121130e135050 N and 43220e5324’E). With a continental the climate models agree that tropical deforestation induces monsoon climate, this subarctic province has an annual tempera- a regional warming (Brovkin, 2002). ture ranging from 4 Cto4C. Its winters are long and frigid while This simulation approach faces two critical limitations. First, summers are short and cool. Apart from monsoons, this subarctic simulation outcomes are based on hypotheses that may or may not climate is also affected by latitude at the regional scale, and by eventuate. Because of the influence from variables that are not topographic relief at the local scale. The Province receives an annual considered in the model, the modeled outcome may not replicate rainfall of 500e600 mm on average. Its topography is dominated by what has taken place. Secondly and more importantly, simulation is a few mountain ranges. Two of the most important ones are the able to reveal climate change at the global or regional scales, but Greater Khingan (about 1600 m asl) and Lesser Khingan where may not yield accurate predictions at the local scale. These limita- natural forests are distributed (Fig.1). The Greater Khingan contains tions can be effectively overcome by detecting the actual defores- the largest remnant virgin forest in China. Heilongjiang is also an tation in the past and correlating the detected change with important timber base in China. Forestry covers 19.19 million ha of observed climate change to assess its impact realistically. the Province’s total land area (41.9%) with the common species One reliable method of studying deforestation is remote sensing. being Korean pine. With a history of decades or even longer, remotely sensed data Large-scale deforestation in Heilongjiang started in the early enable the detection of such change. Consequently, satellite data 1920’s after the construction of the “mid-eastern” railway. After have found wide applications in monitoring deforestation at the nearly 80 years of logging to meet the country’s insatiable demand regional scale (Reddy, Rao, Pattanaik, & Joshi, 2009; Zhang, Devers, for timber, the total timber reserve in this Province decreased to Desch, Justice, & Townshend, 2005). The Landsat Pathfinder data are about 1.4 billion m3 by 2008 (Zhang, 2008). Most of the newly ideal for mapping deforestation in the tropics. Remotely sensed data deforested areas were converted to farmland in response to the of Earth resources such as Landsat Multispectral Scanner, Thematic ever increasing rural population that grew from 7.46 million in Mapper and other data of a similarly high spatial resolution have 1949 to 18.3 million in 2000. Deforestation has caused grave been applied widely to assess tropical deforestation and determine environmental degradation, such as soil erosion and flooding. The the rate of deforestation (Tucker & Townshend, 2000). Regional worst flooding in 1998 was attributed to the worsened eco- deforestation is easily detected from coarse resolution satellite data environment in the watershed. Consequently, logging of virgin such as Moderate Resolution Imaging Spectroradiometer (MODIS) forest was strictly forbidden while limited logging of plantation (Di Maio Mantovani & Setzer, 1997; Ichii, Maruyama, & Yamaguchi, forest was permitted. At the same time more efforts were directed 2003). Near real-time mapping of deforested areas, assessment of at reforestation. their causes and impacts are possible via automatic analysis of MODIS data and the products derived from them (Ferreira, Ferreira, Data used Huete, & Ferreira, 2007). Analysis of aerial photographs together with satellite images allows detection of small-size deforested areas Meteorological data (Harper, Steininger, Tucker, Juhn, & Hawkins, 2007). The drawback of using optical images in detecting deforestation in the tropics (e.g., Temperature data were collected from the Data Center of the frequent cloud cover) can be circumvented with radar imagery that National Meteorological Bureau of China. These observations were has a strong penetration capability. The European radar satellites made at 28 stations widely distributed throughout the Province at ERS-1/2 have the potential for continuous monitoring of defores- a broad range of elevations (Fig. 1). However, there is a lower tation. They are suitable for monitoring and analyzing forest concentration in the far north due to a low population there. Also conversion at a scale of 1:100,000 (Kuntz & Siegert, 1999). Synthetic contained in the dataset are latitude, longitude, and elevation of aperture radar (SAR) data are a beneficial supplement to optical each station. These data have been aggregated to the monthly satellite data (Almeida-Filho, Rosenqvist, Shimabukuro, & Silva- interval, covering the period from January 1961 to December 2008. Gomez, 2007; Rignot, Salas, & Skole, 1997). SAR images have proved feasible in detecting recently deforested areas in the Bra- Forest cover maps zilian Amazon (Santos, Mura, Paradella, Dutra, & Goncalves, 2008). In spite of such advantages of remotely sensed data in studying A land use map of Northeast China was collected from the deforestation, no attempts have been made to link the detected Institute of Geographical Science and Natural Resources, Chinese change to climate change in exploring their causal relationship Academy of Science. It was produced at a scale of 1:3 million from except at the conceptual level. visual interpretation of aerial photographs taken in 1958. Contained This study attempts to establish a quantitative association in this map were ten categories of land cover, including primary/ between deforestation and climate warming to enhance our dense forest and secondary forest in addition to built-up, rice paddy, understanding of the impact of human activities on the environ- dry fields, grassland, swamp, water, sandy land, and desert. Primary ment at the regional and local scales. This paper aims to assess the forest refers to virgin or natural forest of a complete canopy. It has impact of deforestation on the climate of subarctic China with three been in existence for millennia. Secondary forest or plantation specific objectives: (1) to establish a quantitative relationship forest has a varying density never reaching 100%. It was established J. Gao, Y. Liu / Applied Geography 32 (2012) 281e290 283

Fig. 1. Location of study area in relation to China and the distribution of the 28 weather stations. 1, 2, 3 ., 28: name of station (see Table 1). in former primary forest areas after trees had been felled. In addi- stations was averaged to derive the mean annual temperature for tion, two digital land cover maps of the Province in the ArcGIS the Province. Such annual temperature was regressed linearly format were also collected from the Chinese Academy of Science. against time to identify the trend of climate change. This regional They were derived from visual interpretation of Landsat TM/ trend of change was then compared with its nationwide counter- Enhanced TM Plus (ETMþ) images recorded in 1980 and 2000. part during the same period. In order to determine the relationship These images were recorded during the summer (JuneeSeptember) between the changes identified above and the environment, the when forest had a maximum spectral disparity from other covers. observed climate change was further explored in relation to Both digital maps contain an identical set of land covers that include elevation and latitude. In addition, decadal temperature was dense forest, sparse forest, shrubs, and orchards. Other covers are calculated in the 1960s, the 1970s, the 1980s, and the 1990s in order identical to those of the 1958 map, namely, dry fields, rice paddy, to remove yearly fluctuation. Climate warming was derived from grassland, built-up, water, sandy land, and wetland. These covers the rise in decadal temperature from the 1960s to the 1970s, and were delineated from the false color composites of bands 4, 3 and 2 from the 1980s to the 1990s. through on-screen digitization in the PCI image analysis system Deforestation was detected by overlaying consecutive land (version 7.0). The data supplier claimed an accuracy of around 90% cover maps in ArcGIS after they had been projected to the same for the forest-related covers, derived from a combination of field coordinate system (i.e., zone 51 north of UTM), and the relevant visits and comparison with existing land cover statistics. forest-related covers properly aggregated (e.g., dense forest considered as equivalent of primary forest; sparse forest and shrubs Data analysis combined as secondary forest). Namely, the 1958 map produced from aerial photographs was “unioned” with the 1980 map, and the Annual temperature at each weather station was calculated 1980 map with the 2000 map. This spatial comparison revealed from the monthly temperature. Temperature at all the weather deforestation for the entire Province. The resultant deforestation 284 J. Gao, Y. Liu / Applied Geography 32 (2012) 281e290 maps were then queried to identify the quantity of conversion in the 1980 data is defined to have a density of 30% or higher. between forest and other land covers. Therefore, a large majority of dense forest actually represents In order to reveal the local effect of deforestation, a 25 km buffer secondary forest. This differential density explains the twofold loss was used to generate polygons around each of the 28 weather of secondary forest during this period. This inconsistency in defi- stations. Such a distance was considered appropriate as the buff- nition, nevertheless, disappears if both classes of forest are amal- ered polygons did not overlap with each other while still preserving gamated into one. After merging forested area decreased by a net the climatic influence of deforestation. This buffer layer was value of 22,326 km2. The actual extent of deforestation is much subsequently intersected with the three land cover maps individ- more severe than this figure suggests because of reforestation in ually. The amount of all forest-related covers within each buffered the interim and the relaxed criteria for secondary forest that also circle in 1958, 1980 and 2000 was quantified from their attribute encompassed sparse woodland with a density between 10 and 30%, table, respectively, and expressed in square kilometers. The change and even newly planted saplings that can be barely called forest. in forest cover from 1958 to 1980 and from 1980 to 2000 was The trend of deforestation continued during 1980e2000 (Fig. 2). calculated via subtraction. Climate warming as measured by the Given that the two datasets were produced in accordance with the rise in decadal temperature from the 1960s to the 1970s, and from same classification scheme, the forested classes were directly the 1980s to the 1990s was regressed against deforestation during comparable to each other. Dense forest decreased by 9211 km2 1958e1980 and during 1980e2000. Those stations with minimal while sparse forest gained 831 km2, resulting in a net loss of deforestation during 1980e2000 were increasingly eliminated on 8380 km2 (Fig. 2). These changes reveal forest dynamics in that the ascending order (e.g., the stations with the smallest defores- existing trees are logged while newly cleared areas are reforested. tation were excluded first from the analysis) in order to study the The subdued pace of deforestation during this period is attributed sensitivity of climate warming to the magnitude or severity of to the scarcity of forest remaining in an environmentally sound and deforestation, and to identify the deforestation threshold at which physically accessible location, as well as to the tightened control a noticeable climate change is induced. Temperature at those over logging that has been instituted to minimize the negative stations without drastic deforestation was compared with that at impact of deforestation on the environment during this period. places where extensive deforestation has occurred over the study period to examine the effect of forest cover on climate warming. Regional effect of deforestation

Results Annual temperature in Heilongjiang fluctuated widely around the mean of 1.5 C without any apparent trend of change from the Trend of deforestation 1960s to the mid-1980’s(Fig. 3). Since then it started to rise, and reached the first maxima of 3.56 C in 1990. It stayed at an There were 169,534 km2 and 68,801 km2 of primary and unprecedentedly high level until 2000. Apparently, the warming secondary forest, respectively, in 1958, with a combined area of trend was accelerated in the last two decades. The 1970s and the 238,335 km2 (Fig. 2). Forest in Heilongjiang experienced a drastic early 1980s had a cooler temperature below this general trend reduction during 1958e1980. Not only has the spatial extent of while the late 1980s and the 1990s had a higher temperature above forest cover shrunk, but also the composition of forest has been this warming trend. According to the regression model, Hei- degraded in that former dense forest was converted to shrubs and longjiang experienced a warming of 1.68 C over the period of sparse forest. Although primary forest gained 16,659 km2, 1961e2000, or 0.42 C per decade. secondary forest decreased by 38,985 km2. This increase in primary This pattern of warming is also reminiscent of that of the nation forest is considered plausible and irrational as it most likely results where annual temperature anomaly hovered around 0.5 C from the changed definition of dense forest whose cover density (Fig. 3). The warming trend emerged primarily from the mid-1980s was relaxed to as low as 30% instead of the former 100%. Woodland onwards. After a drop in 1984, the anomaly started to rise sharply

Fig. 2. Relationship of forest cover and decadal temperature during 1958e2000 in Heilongjiang Province. J. Gao, Y. Liu / Applied Geography 32 (2012) 281e290 285

Fig. 3. Trend of annual temperature in Heilongjiang, averaged from the annual temperature observed at 28 stations (top line), and annual temperature anomalies of whole China during 1961e2000 (bottom line, source: Ren et al., 2005). until the first peak of 0.47 C in 1990. Then it fluctuated around 0 C Deforestation bears an inverse relationship with the observed and stayed above this value ever since. Thus, the increase in climate warming as revealed by the increase in decadal tempera- temperature was confined mostly to the last two decades. ture (Fig. 2). A lower forest cover on the ground corresponds to According to the regression model, annual temperature anomaly a higher decadal temperature in the following decade. Over the rose by approximately 0.99 C during 1961e2000, or an average period of 1958e2000, roughly, deforestation of every 10,000 km2 decadal rise of 0.25 C. Therefore, the nationwide warming is corresponds to a rise of 0.42 C in decadal temperature. Thus, 0.69 C lower than that of Heilongjiang during the same period. deforestation over the four decades in Heilongjiang is responsible This differential warming trend as measured by annual tempera- for causing its climate to warm by a magnitude in the vicinity of ture at the provincial level can also be appreciated from the decadal 0.69 C. Although the bulk of warming in the province took place temperature that kept rising gradually but steadily from 1.58 Cin post-1980 whereas the bulk of deforestation occurred prior to 1980, the 1960s to 1.79 C in the 1970s, to 2.37 C in the 1980s, and this assertion is still convincing as deforestation has a delayed eventually to 2.86 C in the 1990s (Fig. 2). Decadal temperature rose warming effect of about 15 years (Gao & Liu, 2011). only 0.51 C over the 1960se1980s period, but the pace of warming This pace of warming at the provincial scale resembles quickened in the 1990s. This increase in decadal temperature in remarkably the 0.6 C warming associated with the conversion Heilongjiang outpaces that of its national counterpart in every from tropical broadleaf evergreen forest to cropland in eastern decade. Moreover, the difference between two consecutive decadal Santa Cruz, Bolivia reported by Bounoua, DeFries, Imhoff, and temperatures is consistently larger in Heilongjiang than the entire Steininger (2004). A similar warming trend in mean surface nation. temperature by about 2.5 C has been reported by Nobre, Sellers, The observed faster pace of warming in Heilongjiang may be and Shukla (1991) if the Amazonian tropical forests were affected by a number of environmental variables, such as elevation replaced by degraded pasture as revealed via simulation. The pace and latitude. At the national scale this warming bears an obvious of regional warming caused by deforestation in Heilongjiang is relationship with latitude, with higher latitude having a larger pace much lower because it took place at a limited scale (e.g., there was of warming (Yin, 2006). At the provincial level, however, no definite still forest left). correlation exists between the rise in decadal temperature and latitude (Fig. 4A). The correlation between elevation and decadal Local effect of deforestation temperature rise is also weak for the large majority of the stations (Fig. 4B). If the two high-elevation outliers are excluded, a higher The local effect of deforestation on climate change is studied by altitude appears to correspond to a higher decadal temperature. examining the amount of de(re)forestation within a 25 km radius Thus, a higher altitude is marginally more sensitive to temperature from a weather station during 1958e1980 and 1980e2000 irre- rise. Overall, the relationship between elevation and temperature is spective of the type of forest cover. Namely, dense forest, sparse not so definite probably because the study area has only a very forest and shrubs were treated indiscriminately. This change in narrow range of elevation. Only a few stations located at a higher forest cover is then correlated with the change in decadal elevation experienced a lower rise in decadal temperature. The temperature from the first decade (e.g., the 1980s) to the second absence of a close correlation between climate warming with decade (e.g., the 1990s), that was treated as the surrogate for elevation and latitude leaves only land cover change (e.g., defor- climate warming, corresponding to the deforestation period. The estation) to account for the higher rise in decadal temperature at rationale underlying this correlation analysis is that deforestation the regional scale. over a period must be followed by a gradual change in temperature 286 J. Gao, Y. Liu / Applied Geography 32 (2012) 281e290

Fig. 4. Relationship between rise in decadal temperature from the 1960s to 2000s with latitude (A) and elevation (B).

in the interim. Climate warming exhibits a generally negative a processing did not bring noticeable improvement to the accuracy relationship with deforestation among the 58 observations (Fig. 5). of the regression model (e.g., R2 value did not improve much). The Initially, the relationship is rather weak at an R2 value of only 0.23. replacement of the initial forest cover by the mean forest cover The relationship becomes stronger (R2 ¼ 0.299) after one station during a period slightly improved the accuracy of the regression (Anda) that did not experience any deforestation during model, indicating that forest cover did not exert a uniform effect on 1958e1980 due to the absence of forest cover was excluded from climate warming. the analysis. This relationship can be expressed as: In order to ascertain the exact relationship between forest cover, deforestation and climate change, the 28 stations were carefully DT ¼0:013DF þ 0:4114 R2 ¼ 0:299 (1) scrutinized and ranked in terms of their forest cover and the amount of deforestation. They fall into four groups of sparse forest The above relationship can explain 30% of the variation in the (e.g., <116 km 2 or 6% of total area), dense forest (>480 km2 or 24%), rise of decadal temperature. The remaining variation is accountable deforestation and reforestation at a threshold >10 km2 (Table 1). by such variables as the amount of initial forest cover and the Thus, those meteorological stations with a few km2 of deforestation magnitude or severity of deforestation. The amount of deforesta- were excluded from further analysis because of the negligible tion in relation to forest cover at the beginning or proportion amount of forest cover in their vicinity (e.g., <116 km 2). (severity) of deforestation was taken into account by dividing the Table 2 shows the mean and standard deviation of each group. amount of deforestation by the initial forest acreage. However, such Results of a t-test of the mean rise in decadal temperature between J. Gao, Y. Liu / Applied Geography 32 (2012) 281e290 287

Fig. 5. Relationship between deforestation during 1958e1980 and rise in decadal temperature from the 1960s to the 1970s, and that during 1980e2000 and the rise in decadal temperature from the 1980s to the 1990s. sparse and dense forest covers demonstrate that the null hypoth- the average forest cover during a period and climate warming esis of no statistical difference between the means has a rejection within the same period (Fig. 6). Those stations surrounded by little region of jtj2.12 at the 95% significance level. The calculated forest tend to experience more warming than those surrounded by t-value of 2.476 exceeds this rejection threshold. Therefore, the rise extensive forest. in decadal temperature for dense forest cover is statistically However, this relationship (R2 ¼ 0.539) based on only 17 different from that of sparse forest cover. More forest cover is stations is heavily influenced by five stations that have a forest conducive to a slower rise in decadal temperature. This finding is cover over 400 km2. Thus, this inhibiting effect of forest cover on consistent with that of Wigley (1993) who concluded that forest the rise in decadal temperature is more pronounced if the forest cover bears a close relationship with climate warming. This cover itself is sufficiently extensive at the local scale. The influence conclusion is also confirmed by the inverse relationship between of the severity of deforestation on climate warming at the local

Table 1 Rise in decadal temperature from the 1980s to the 1990s at 28 meteorological stations, their deforestation during 1980e2000 and forest cover in 1980.

Station Location Lat. Elevation Warming De(re)forestation Forest in 1980 Long. (m) (C) (km2) (km2) 1.Tieli 4959,128.01 210.5 0.47 150.38 372.40 2. Shangzhi 45.13,127.58 189.7 0.67 121.98 621.07 3. Sunwu 49.26,127.31 234.5 0.66 113.78 673.74 4. Beian 48.17,126.31 269.7 0.45 64.53 144.87 5. Baoqing 46.19,132.11 83.0 0.46 49.88 258.89 6. Yilan 46.18,129.35 100.1 0.40 46.78 303.87 7. Nenjiang 49.10,125.14 242.2 0.51 35.91 90.94 8. Hailun 47.26,126.58 239.2 0.62 24.61 33.08 9. Mingshui 47.10,125.54 247.2 0.66 23.61 54.62 10. Hihe 50.15,127.27 166.4 0.40 15.03 370.05 11. Tonghe 45.58,128.44 108.6 0.45 13.30 251.07 12. Mudanjiang 44.34,129.36 241.4 0.47 11.74 499.26 13. Mohe 52.58,122.31 433.0 0.05 217.01 901.75 14. Jixi 45.18,130.56 280.8 0.33 15.10 497.58 15. Tailai 46.24,123.25 149.5 0.59 13.24 13.59 16. Yichun 47.44,128.55 240.9 0.19 2.52 920.72 17. Suifenhe 44.23,131.10 567.8 0.60 9.71 570.22 18. Huma 51.43,126.39 177.4 0.40 1.40 482.13 19. Hegang 47.20,130.16 227.9 0.39 1.13 482.29 20. Jiamushi 46.49,130.17 81.2 0.45 1.51 113.88 21. Hulin 45.46,132.58 100.2 0.42 5.42 99.44 22. Keshan 48.03,125.53 234.6 0.61 4.51 76.20 23. Fuyu 47.48,124.29 162.7 0.69 4.19 44.32 24. Fujin 47.14,131.59 66.4 0.35 0.77 40.06 25. Qiqihar 47.23,123.55 147.1 0.73 2.82 35.00 26. Harbin 45.45,126.46 142.3 0.74 3.50 18.13 27. Suihua 46.37,126.58 179.6 0.55 0.00 3.87 28. Anda 46.23,125.19 149.3 0.72 0 0 288 J. Gao, Y. Liu / Applied Geography 32 (2012) 281e290

Table 2 Results of t-test for the rise in decadal temperature (C) for forest cover and change during 1980e2000.

Forest cover Forest change

Sparse (<120 km2) Dense (>480 km2) Deforestation (>10 km2) Reforestation (>10 km2) n 12 6 12 3 mean 0.59 0.38 0.51 0.32 Standard dev 0.131 0.233 0.104 0.270 Rejection region (p ¼ 95%) jtj2.120 jtj2.145 Calculated t-value 2.476 2.062

scale can be appreciated from the results in Table 3. If all observa- that experienced reforestation of 13.24 km2, highly comparable to tions except two that did not experience any deforestation were its initial forest cover of 13.59 km2 (Table 1). Its removal from the used in the regression analysis, the regression model has an R2 reforestation group resulted in the difference of the two mean rises value of only 0.267, highly comparable to 0.299 in Eq. (1). This R2 in decadal temperature being statistically significant at the 95% value rises to 0.315 if the deforestation threshold is raised to 3 km2 level. Thus, not only the amount of de(re)forestation but also the at which 20 stations remain. If the deforestation threshold is raised amount of initial forest cover exert an impact on climate warming, further to 5 km2, then the model accuracy improves much more to even though this impact cannot be quantified precisely as it is 0.482 with 17 stations meeting the selection criterion. If the subject to the interference of both variables. threshold doubles to 10 km2, the number of observation drops to 15, based on which the regression model has an R2 value of 0.539. In Discussion other words, more than half of the warming in decadal temperature during 1980e2000 can be explained by deforestation alone. Deforestation is conducive to a warmer climate regime because Therefore, the relationship between deforestation and the rise in it is critical to the building-up of CO2 in the atmosphere. A huge decadal temperature becomes increasingly stronger as more quantity of carbon is locked up in the forest biomass, which, if observations with the smallest amount of deforestation are incre- released, would add considerably to climate warming (Molion, 2 mentally eliminated from the analysis. Furthermore, 5 km of 1989). Thus, atmospheric CO2 content is related to land-use deforestation appears to be the critical threshold at which climate change such as deforestation (Dale, 1997; Kalnay & Cai,, 2003; warming can be predicted from the amount of deforestation at Trenberth, 2004). Large-scale land cover change accounts for up a reasonable accuracy (e.g., about 50%) at the local scale. After this to half of the observed warming in southwest Western Australia threshold, the probability at which the null hypothesis about the R2 (Pitman, Narisma, Pielke, Sr., & Holbrook, 2004) owing probably to value is rejected stays at 0.10% (Table 3). significant differences in cloud formation between forested and It must be noted that in Table 2 the warming in decadal deforested regions (Nair, Welch, Lawton, Pielke, & Sr., 2000). temperature (0.51 C) averaged from the 12 deforestation stations Apart from deforestation, climate change can also be caused by is not statistically different from that averaged from the three solar activities (e.g., aerosols), volcanism, and other human activi- reforestation stations (0.32 C) at the 95% significance level, even if ties (Zhao et al., 2005). The larger pace of warming in the 21st the 10 km2 threshold is used to screen out minor de(re)forestation century is likely to be attributed to more aerosols associated with stations. However, the two means are statistically different from the vehicles. However, their influence on climate warming can be each other only at the 90% significance level (jtj1.761). A very considered constant at the regional scale, and is fragmented by critical station affecting the t-test outcome at the 95% level is Tailai human settlements. In areas of forest cover, this influence on

Fig. 6. Relationship between average forest cover during 1980e2000 and the rise in decadal temperature from the 1980s to the 1990s (Data as shown in Table 1). J. Gao, Y. Liu / Applied Geography 32 (2012) 281e290 289

Table 3 Relationship between deforestation magnitude during 1980e2000, regression model accuracy and its signiﬁcance level.

Deforestation threshold No. of observation Model accuracy (R2) T-test results for R2

Calculated t-value Reject region of null hypothesisa jDFj>0km2 26 0.247 (r ¼ 0.50) 2.828 0.46% jDFj>3km2 20 0.315 (r ¼ 0.56) 2.868 0.51% jDFj>5km2 17 0.482 (r ¼ 0.69) 3.692 0.10% jDFj>10 km2 15 0.539 (r ¼ 0.73) 3.851 0.10%

a observed value comes from a population in which rho ¼ 0. climate warming is considerably overwhelmed by deforestation- deforestation is positively correlated with its severity at the local induced warming. At the local scale the absence of a consistent scale. Nearly half of the variation in local climate warming can be effect of deforestation on climate change across all stations is explained by deforestation over 5 km2. Therefore, the minimum explained by two factors. First, they are located in urban areas that threshold for deforestation to have a noticeable impact on climate may have expanded over the last few decades. Inevitably, the warming appears to be 5 km2. In addition, the rise in decadal observed rise in temperature is subject to the influence of urban temperature is also affected by the amount of initial forest cover. heat island. In other words, urbanization inevitably exerts some Those stations with extensive forest cover experienced a slower variable effect on local climate warming. Nevertheless, urbaniza- pace of warming. However, the impact of forest cover averaged over tion cannot account for the higher pace of warming in Heilongjiang a period on climate warming is not uniform and cannot be quan- than the national warming as urbanization was not just confined to tified accurately. Although the land cover maps themselves may this province alone. Arguably, urbanization would have an almost involve a degree of inaccuracy, they are generally regarded as equal effect on climate warming throughout China. Second and adequately accurate in carrying out land cover studies at the more importantly, the forest cover in the vicinity of a weather regional scale. Their inaccuracy may affect the exact figures of station is indiscriminately treated. This handling does not take into deforestation, but should not invalidate the general relationship consideration the quality of forest cover (e.g., amount of biomass on between deforestation and climate warming at both the provincial the ground). In other words, the amount of change in biomass and local levels. caused by deforestation or reforestation has been ignored, even though the influence may be minor. This imprecision may disguise Acknowledgments the impact of deforestation on the local temperature. This defi- ciency can be overcome with the use of normalized difference This research could not have been done without funding from vegetation index that can overcome the imprecision in the defini- the Science Faculty Staff Development Fund at the University of tion of forest covers (e.g., forest cover density is implicitly consid- Auckland. Additional funding was received from the Knowledge ered). This quantitative measure should shed more light on the Innovation Program of the Chinese Academy of Sciences (grant exact influence of deforestation on local climate. number KZCX2-EW-304), and the National Natural Science Foun- dation of China (grant numbers 40635029 and 40871257). Two Conclusions anonymous reviewers supplied some valuable and constructive comments on an earlier version of this manuscript. Huan Li helped Heilongjiang Province experienced a drastic reduction in forest with some additional data analyses. cover during 1958e2000. Forest area shrank from 238,335 km2 in 1958 to 216,009 km2 in 1980, and further to 207,629 km2 in 2000, or by 30,706 km2 during 1958e2000. Temporally, the rate of References deforestation is rather high during the first period but subdued Almeida-Filho, R., Rosenqvist, A., Shimabukuro, Y. E., & Silva-Gomez, R. (2007). during the second period. While the rate of deforestation slowed Detecting deforestation with multitemporal L-band SAR imagery: a case study down during 1980e2000, the quality of the remaining forest in western Brazilian Amazônia. International Journal of Remote Sensing, 28, deteriorated. Dense forest area decreased by 9211 km2, against 1383e1390. 2 2 Bounoua, L., DeFries, R. S., Imhoff, M. L., & Steininger, M. K. (2004). Land use and a gain of 831 km sparse forest, resulting in a net loss of 8380 km . local climate: a case study near Santa Cruz, Bolivia. Meteorology and Atmo- The observed deforestation bears an inverse relationship with spheric Physics, 86,1e2, 73-85. decadal temperature on the regional scale. The Province’s annual Brovkin, V. (2002). Climate-vegetation interaction. Journal De Physique IV, 12,57e72. e Correia, F. W. S., Alvalá, R. C. S., & Manzi, A. O. (2008). Modeling the impacts of land temperature rose by 1.68 C during 1961 2000, 0.69 C higher than cover change in Amazonia: a regional climate model (RCM) simulation study. the national average of 0.99 C during the same period. The pace of Theoretical and Applied Climatology, 93, 225e244. rise in decadal temperature is consistently higher for Heilongjiang Dale, V. H. (1997). The relationship between land-use change and climate change. Ecological Applications, 7, 753e769. than the whole country. Thus, deforestation was responsible for Di Maio Mantovani, A. C., & Setzer, A. W. (1997). Deforestation detection in the causing climate to warm by a magnitude in the vicinity of 0.69 Cat Amazon with an AVHRR-based system. International Journal of Remote Sensing, the provincial scale. Deforestation of 10,000 km2 corresponds to 18,273e286. a rise of 0.42 C in annual temperature. This warming does not bear Ferreira, N. C., Ferreira, L. G., Huete, A. R., & Ferreira, M. E. (2007). An operational deforestation mapping system using MODIS data and spatial context analysis. any definite relationship with latitude and elevation at the International Journal of Remote Sensing, 28,47e62. provincial level. At the local scale, deforestation is related inversely Findell, K. L., Knutson, T. R., & Milly, P. C. D. (2006). Weak simulated extratropical e to climate warming in the form of DT ¼0.013DF þ 0.4114. This responses to complete tropical deforestation. Journal of Climate, 19, 2835 2850. Gao, J., & Liu, Y. (2011). Climate warming and land use change in Heilongjiang regression model is able to explain 30% of the variation in the rise in Province, Northeast China. Applied Geography, 31, 476e482. decadal temperature. Meteorological stations in the midst of dense Gao, X. J., Zhang, D. F., Chen, Z. X., Pal, J. S., & Giorgi, F. (2007). Land use effects on forest cover have a statistically significant difference in mean climate in China as simulated by a regional climate model. Science in China, Series D: Earth Sciences, 50, 620e628. decadal temperature rise from those of sparse forest cover at the Gedney, N., & Valdes, P. J. (2000). Effect of Amazonian deforestation on the northern 95% level. The accuracy of modeling climate warming from hemisphere circulation and climate. Geophysical Research Letters, 27, 3053e3056. 290 J. Gao, Y. Liu / Applied Geography 32 (2012) 281e290

Harper, G. J., Steininger, M. K., Tucker, C. J., Juhn, D., & Hawkins, F. (2007). Fifty years Rignot, E., Salas, W. A., & Skole, D. L. (1997). Mapping deforestation and secondary of deforestation and forest fragmentation in Madagascar. Environmental growth in Rondonia, Brazil, using imaging radar and thematic mapper data. Conservation, 34, 325e333. Remote Sensing of Environment, 59,167e179. Houghton, R. A. (1990). Global effects of tropical deforestation. Environmental Sampaio, G., Nobre, C., Costa, M. H., Satyamurty, P., Soares-Filho, B. S., & Cardoso, M. Science and Technology, 24,414e422. (2007). Regional climate change over eastern Amazonia caused by pasture and Ichii, K., Maruyama, M., & Yamaguchi, Y. (2003). Multi-temporal analysis of soybean cropland expansion. Geophysical Research Letters, 34, L17709. deforestation in Rondônia state in Brazil using Landsat MSS, TM, ETMþ doi:10.1029/2007GL030612. and NOAA AVHRR imagery and its relationship to changes in the local Santos, J. R., Mura, J. C., Paradella, W. R., Dutra, L. V., & Goncalves, F. G. (2008). hydrological environment. International Journal of Remote Sensing, 24, Mapping recent deforestation in the Brazilian Amazon using simulated L-band 4467e4479. MAPSAR images. International Journal of Remote Sensing, 29, 4879e4884. IPCC. (2007). The physical scientiﬁc basis. Contribution of Working group I to the Sen, O. L., Wang, Y., & Wang, B. (2004). Impact of Indochina deforestation on the Fourth assessment Report of the Intergovernmental Panel on climate change. east Asian summer monsoon. Journal of Climate, 17, 1366e1380. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Shukla, J., Nobre, C., & Sellers, P. (1990). Amazon deforestation and climate change. Press. Science, 247, 1322e1325. Kalnay, E., & Cai, M. (2003). Impact of urbanization and land-use change on climate. Taylor, C. M., Lambin, E. F., Stephenne, N., Harding, R. J., & Essery, R. L. H. (2002). The Nature, 423, 528e531. inﬂuence of land use change on climate in the Sahel. Journal of Climate, 15, Karl, T. R., & Trenberth, K. E. (2003). Modern global climate change. Science, 302, 3615e3629. 1719e1723. Trenberth, K. E. (2004). Rural land-use change and climate. Nature, 427,213. Kuntz, S., & Siegert, F. (1999). Monitoring of deforestation and land use in Indonesia Tucker, C. J., & Townshend, J. R. G. (2000). Strategies for monitoring tropical defores- with multitemporal ERS data. International Journal of Remote Sensing, 20, tation using satellite data. International Journal of Remote Sensing, 21, 1461e1471. 2835e2853. Werth, D., & Avissar, R. (2005). The local and global effects of Southeast Asian Molion, L. C. B. (1989). The Amazonian forests and climatic stability. Ecologist, 19, deforestation. Geophysical Research Letters, 32,1e4. 211e213. Wigley, T. M. L. (1993). Climate change and forestry. Commonwealth Forestry Review, Nair, U.S., Welch, R.M. Lawton, R.O., & Pielke Sr., R.A. (2000). Impact of land use on 72, 256e264. Costa Rican regional climate. International Geoscience and Remote Sensing Yin, Y. (2006). The change of Aridity/Humidity conditions and the response of the eco- Symposium (IGARSS 2000),v.5,1993e1995. Geographical regions to climate change in China, doctoral dissertation, Chinese Nobre, C. A., Sellers, P. J., & Shukla, J. (1991). Amazonian deforestation and regional Academy of Science, p. 128 (in Chinese). climate change. Journal of Climate, 4,957e988. Zeng, N., & Neelin, J. D. (1999). A land-atmosphere interaction theory for the Pitman, A. J., Narisma, G. T., Pielke, R. A., Sr., & Holbrook, N. J. (2004). Impact of land tropical deforestation problem. Journal of Climate, 12,857e872. cover change on the climate of southwest Western Australia. Journal of Zhang, Q., Devers, D., Desch, A., Justice, C. O., & Townshend, J. (2005). Mapping Geophysical Research D: Atmospheres, 109(D18109), 1e12. tropical deforestation in Central Africa. Environmental Monitoring and Assess- Reddy, S. C., Rao, R. M., Pattanaik, C. K., & Joshi, P. K. (2009). Assessment of large- ment, 101,69e83. scale deforestation of Nawarangpur district, Orissa, India: a remote sensing Zhang, S. F. (2008). Northeast timber e the pillar of new China. Chinese National based study. Environmental Monitoring and Assessment, 154, 325e335. Geography, 576, 240e247, (in Chinese). Ren, G.-Y., Xu, M.-Z., Chu, Z.-Y., Guo, J., Li, Q.-X., Liu, X.-N., et al. (2005). Changes of Zhao, Z.-C., Wang, S.-W., Xu, Y., Ren, G.-Y., Luo, Y., & Gao, X.-J. (2005). Attribution of surface air temperature in China during 1951-2004. Climatic and Environmental the 20th century climate warming in China. Climatic and Environmental Research, 10,717e727, (in Chinese). Research, 10, 808e817, (in Chinese).