基于TVX方法的南京市城区时空格局与地表温度的研究

随着全球气候变化和人类活动的不断加剧,导致生态、资源与环境问题日趋严重,区域空间格局变化与地表温度的研究已成为全球环境变化的热门和重点研究课题。采用2000年、2006年和2010年的TM遥感影像，结合GIS和RS技术研究南京市不同时期的时空分布特征、演变特征以及地表温度的变化。结果表明: 2000～2010年南京市城区建设用地面积增长幅度较大，而耕地面积却呈显著递减趋向，水体和草地面积基本保持稳定，林地面积略有增长。NDVI和MNDWI均与地表温度呈负相关，通过构建TVX空间观察南京城市化进程，发现植被覆盖度持续降低，地表温度急速上升，到2010年，所有土地利用类型的聚类点的转化轨迹趋于一点，变化向量幅度是：林地>草地>水体>耕地，研究结果表明林地和水域对城市地表热环境具有强大的缓解作用，在城市化进程中，要注重城市水域和林地的保护，同时要改善城市绿化、减少能耗和节制热源     

Snow cover and vegetation greenness with leaf water content control the global land surface temperature

The land surface temperature (LST) and land use land cover (LULC) are the major components of climate- and environment-related studies. The objective of this study was to assess the relationship of LST with remotely sensed LULC-derived vegetation indices during 2018 at the global, latitudinal and continental scales. Moderate resolution imaging spectroradiometer (MODIS) Aqua daytime LST and eight LULC MODIS indices (NDVI, EVI, LAI, DSI, NDWI, albedo, NDSI, NDBI) were processed using Earth Engine Code Editor. The analysis was conducted using correlation coefficient and significance of the relationship of variables based on 2050 randomly selected points at the global scale. Based on the univariate and geographically weighted regression methods, the research confirmed that vegetation greenness (NDVI), leaf water content (NDWI) and snow cover (DSI) are the codominant drivers of decreasing LST at the global scale including Europe, Asia, South America and North America at the continental scale. Snow cover during winter and vegetation greenness in summer seasons control the global LST. Although albedo shows an inverse relationship, NDBI and NDSI displayed a positive relationship to LST at the global scale. In conclusion, temporal seasonal and inter-annual dynamics of LST in response to snow cover and vegetation properties (greenness, moisture) should be focused on understanding and regulating LST at varying scales.

     

Landslide probability mapping by considering fuzzy numerical risk factor (FNRF) and landscape change for road corridor of Uttarakhand,India

Landslide poses severe threats to the natural landscape of the Lesser Himalayas and the lives and economy of the communities residing in that mountainous topography. This study aims to investigate whether the landscape change has any impact on landslide occurrences in the Kalsi-Chakrata road corridor by detailed investigation through correlation of the landslide susceptibility zones and the landscape change, and finally to demarcate the hotspot villages where influence of landscape on landslide occurrence may be more in future. The rational of this work is to delineate the areas with higher landslide susceptibility using the ensemble model of GIS-based multi-criteria decision making through fuzzy landslide numerical risk factor model along the Kalsi-Chakrata road corridor of Uttarakhand where no previous detailed investigation was carried out applying any contemporary statistical techniques. The approach includes the correlation of the landslide conditioning factors in the study area with the changes in land use and land cover (LULC) over the past decade to understand whether frequent landslides have any link with the physical and hydro-meteorological or, infrastructure, and socioeconomic activities. It was performed through LULC change detection and landslide susceptibility mapping (LSM), and spatial overlay analysis to establish statistical correlation between the said parameters. The LULC change detection was performed using the object-oriented classification of satellite images acquired in 2010 and 2019. The inventory of the past landslides was formed by visual interpretation of high-resolution satellite images supported by an intensive field survey of each landslide area. To assess the landslide susceptibility zones for 2010 and 2019 scenarios, the geo-environmental or conditioning factors such as slope, rainfall, lithology, normalized differential vegetation index (NDVI), proximity to road and land use and land cover (LULC) were considered, and the fuzzy LNRF technique was applied. The results indicated that the LULC in the study area was primarily transformed from forest cover and sparse vegetation to open areas and arable land, which is increased by 6.7% in a decade. The increase in built-up areas and agricultural land by 2.3% indicates increasing human interference that is continuously transforming the natural landscape. The landslide susceptibility map of 2019 shows that about 25% of the total area falls under high and very high susceptibility classes. The result shows that 80% of the high landslide susceptible class is contained by LULC classes of open areas, scrubland, and sparse vegetation, which point out the profound impact of landscape change that aggravate landslide occurrence in that area. The result acclaims that specific LULC classes, such as open areas, barren-rocky lands, are more prone to landslides in this Lesser Himalayan road corridor, and the LULC-LSM correlation can be instrumental for landslide probability assessment concerning the changing landscape. The fuzzy LNRF model applied has 89.6% prediction accuracy at 95% confidence level which is highly satisfactory. The present study of the connection of LULC change with the landslide probability and identification of the most fragile landscape at the village level has been instrumental in delineation of landslide susceptible areas, and such studies may help the decision-makers adopt appropriate mitigation measures in those villages where the landscape changes have mainly resulted in increased landslide occurrences and formulate strategic plans to promote ecologically sustainable development of the mountainous communities in India's Lesser Himalayas.

     

不同土地利用类型的地表特征参量的遥感反演研究——以猫跳河流域为例

利用2007年5月Lansat5 TM数据,运用单通道算法,基于全国遥感监测土地利用/覆盖分类体系,对贵州省猫跳河流域的地表特征参量进行了反演,得到不同土地利用类型下的典型地表特征参数值。在3种主要的土地利用类型中,林地的植被指数最高、地表温度最低,农田的植被指数最低而地表温度最高,草地介乎林地和农田之间。城镇建设用地主要表现为热岛效应,而水域则主要表现为地表温度（LST）和归一化植被指数（NDVI）的双低特征。进一步对二级土地利用类型的分析表明,由于结构、功能的不同,土地利用二级类在地表特征参量方面也表现出了有规律地变化。有助于深入认识不同土地利用类型的物理特征,可以丰富主要土地利用类型的影像分类的先验知识,有望将不同土地利用方式的生态环境效应研究推进一步     

Impacts of predicted sea level rise on land use/land cover categories of the adjacent coastal areas of Mumbai megacity,India

Physical and ecological responses of the coastal areas in the vicinity of Mumbai, India, due to relative sea level rise are examined by different inundation scenarios. Evaluation of potential habitat loss under sea level rise was made by incorporating the land use/land cover (LULC) adopted from the digital elevation model with the satellite imagery. LULC categories overlaid on 1.0, 2.0, 3.0 and 4.0 m coastal elevation showed that the coastal areas of Mumbai were mostly covered by vegetation followed by barren land, agricultural land, urban areas and water bodies. For the relative sea level rise scenarios of 1.0, 2.0, 3.0 and 4.0 m, the tidal inundation areas were estimated to be 257.85, 385.58, 487.56 and 570.63 km², respectively, using GIS techniques. The losses of urban areas were also estimated at 25.32, 41.64, 54.61 and 78.86 km² for the 1.0, 2.0, 3.0 and 4.0 m relative sea level rise, respectively, which is most alarming information for the most populated city on the eastern coast of India. The results conclude that relative sea level rise scenario will lead profound impacts on LULC categories as well as on coastal features and landforms in the adjoining part of Mumbai. The sea level rise would also reduce the drainage gradients that promote flooding condition to rainstorms and subsequently increase saltwater intrusion into coastal regions. Alterations in the coastal features and landforms correlated with inundation characteristics that make the coastal region more vulnerable in the coming decades due to huge development activities and population pressures in Mumbai.     

基于Google Earth Engine的三峡库区土地利用与陆表参数变化研究

土地利用/覆盖变化(Land Use and Land Cover Change, LUCC)对全球有着重要影响,其已对植被覆盖、地表温度(Land Surface Temperature, LST)、反照率以及其它陆表参数产生显著影响。三峡工程自建设以来,库区的土地利用变化逐渐受到外界关注。利用欧空局300 m的土地覆盖分类数据分析三峡库区2000～2015年的土地利用变化;依托先进的Google Earth Engine(GEE)平台,获取MODIS(Moderate Resolution Imaging Spectroradiometer, MODIS)NDVI(Normalized Difference Vegetation Index, NDVI)、LST和反照率数据,并分析三者的时空变化趋势;此外,探究季节性归一化植被指数(Seasonally Integrated Normalized Difference Vegetation Index, SINDVI)与LST和反照率的关系;并分析土地利用变化对SINDVI、LST和反照率的影响。结果表明:2000～2015年,三峡库区土地利用变化显著,耕地、草地、灌木地分别减少2.4%,0.05%和0.62%;林地、水域和人造地表分别增加1.98%,0.04%和1.06%。研究期间SINDVI增加2.89,LST下降0.224℃,反照率减少0.002。总体来看,三峡库区SINDVI的空间分布格局与LST和反照率的相反,且库区大部分区域SINDVI与LST和反照率呈负相关。另外,不同土地类型对SINDVI、LST和反照率影响不同。该文系统地研究了LUCC与上述关键陆表参数的定量关系,可为更好地管理该地区自然环境和土地资源提供科学的依据。     

Quantification of land use/land cover changes in Pearl River Delta and its impact on regional climate in summer using numerical modeling

This study quantified land use/land cover (LULC) changes in Pearl River Delta (PRD) of South China and its impact on regional climate over the last two decades. The LULC change analyses were accomplished by applying a change detection method to a set of Landsat imagery and ancillary data acquired from 1970s to 2000. The results indicate that the urban expansion is the prevailing LULC change in the PRD. Impact of LULC change on regional climate was simulated by using a mesoscale climate model. Two different land cover datasets circa 1990 and 2000 were input to the model to investigate the impact of urbanization on regional weather and climate condition in summer 2005. The simulation results show that rapid urban expansion can substantially alter regional climate conditions in the PRD region including monthly mean temperature, precipitation, moisture, and surface heat fluxes.     

基于知识与规则的土地利用信息分层提取研究——以南京市都市区为例

为了克服利用统一分类模型难以有效提高土地利用分类精度的问题,为土地利用/覆被定性、定量信息的提取提供一种高精度、高效率的提取方法,将分层信息提取法和基于知识规则的信息提取方法相结合,基于对南京市都市区的土地利用时空特点和研究区TM影像数据中各地类波谱信息的分析,结合了地类提取指数模型、DEM数据、城市建成区边界等,充分利用地学先验知识,设计了一套土地信息分层提取的流程,对土地利用信息进行了提取。利用该方法对2012年南京市都市区的土地利用/土地覆被信息提取的总体精度达到了88.67%,Kappa系数达到了0.85。实践证明,基于知识与规则的土地利用信息分层提取方法提取精度较高,适用性强,对其他地区的土地利用/覆被信息分类提取也有一定的借鉴意义。     

Examination of land use/land cover changes,urban growth dynamics,and environmental sustainability in Chittagong city,Bangladesh

As in many other developing countries, cities in Bangladesh have witnessed rapid urbanization, resulting in increasing amounts of land being taken over and therefore land cover changing at a faster rate. Until now, however, few efforts have been made to document the impact of land use and land cover changes on the climate, environment, and ecosystem of the country because of a lack of geospatial data and time-series information. By using open source Landsat data integrated with GIS technologies and other ancillary data, this study attempts to classify land use and create land cover maps, enabling post-classification change detection analysis. By this method, we document the spatial and temporal trajectory of urban expansion in Chittagong, the second largest city in Bangladesh, over a 36-year period. The findings suggest that, over the study period, 56 % of the land cover has undergone change, mainly because of the expansion of built-up areas and other human activities. During the 36-year period, the built-up area around Chittagong city has expanded by 618 %, with an average annual rate of increase of 17.5 %. As a result of rapid urbanization, the vegetated hills near urban development areas face serious threats of further encroachment and degradation, given that 2178 ha of hills have already been intruded over the study period. Because urbanization processes in Bangladesh have traditionally been viewed as the result of population growth and economic development, very little work has been done to track the potential growth trajectory in a physical or spatial context. This study, therefore, will contribute to the current understanding of urban development in Bangladesh from a temporal and spatial point of view. Findings will be able to assist planners, stakeholders, and policy makers in appreciating the dynamism of urban growth and therefore will facilitate better planning for the future to minimize environmental impacts.     

Land-use and land-cover effects on regional biodiversity distribution in a subtropical dry forest: a hierarchical integrative multi-taxa study

Latin American subtropical dry ecosystems have experienced significant human impact for more than a century, mainly in the form of extensive livestock grazing, forest products extraction, and agriculture expansion. We assessed the regional-scale effect of land use and land cover (LULC) on patterns of richness distribution of trees, birds, amphibians, and mammals in the Northern Argentine Dry Chaco (NADC) over c. 19 million hectares. Using species distribution models in a hierarchical framework, we modeled the distributions of 138 species. First, we trained the models for the entire Argentinean Chaco with climatic and topographic variables. Second, we modeled the same species for the NADC including the biophysical variables identified as relevant in the first step plus four LULC-related variables: woody biomass, distance to crops, density of livestock-based rural settlements (puestos), and vegetation cover. Third, we constructed species richness maps by adding the models of individual species and considering two situations, with and without LULC variables. Four, richness maps were used for assessing differences when LULC variables are added and for determining the main drivers of current patterns of species richness. We found a marked decrease in species richness of the four groups as a consequence of inclusion of LULC variables in distribution models. The main factors associated with current richness distribution patterns (both negatively) were woody biomass and density of livestock puestos. Species richness in present-day Semiarid Chaco landscapes is strongly affected by LULC patterns, even in areas not transformed to agriculture. Regional-scale biodiversity planning should consider open habitats such as grasslands and savannas in addition to woodlands.     

基于地理探测器的城市热岛驱动因素分析——以武汉市为例

城市热岛是由地表特征、社会经济和气象等因素综合引起的环境问题，具有显著的季节差异。大量的研究分析了土地覆盖类型、地表覆盖指数、景观指数等和城市热岛之间的相互关系，忽略了气象因素以及气象因素、地表特征与社会经济之间的交互作用对城市热岛的影响。在分析武汉市不同季节热岛强度及空间自相关分析的基础上，采用地理探测器研究了不同季节城市热岛强度的驱动因素及其交互作用和热岛风险区探测。结果表明：武汉市各季节热岛强度在空间上呈现强烈的集聚特征。按春夏秋冬顺序，武汉市4个季节热岛强度的主要驱动因素分别为地表特征、社会经济、社会经济和气象因素，四季对应的主要驱动因子分别是土地覆盖类型、夜间灯光、夜间灯光和气压。在11个驱动因子的交互影响中，四季热岛强度交互影响作用最大的分别为土地覆盖类型与夜间灯光、土地覆盖类型与NDVI、土地覆盖类型与夜间灯光、气压与土地覆盖类型或与NDVI。风险探测结果显示春夏秋冬平均热岛强度最高的分别为城市建成区、城市建成区、城市建成区、裸地和低植被覆盖区，最低的分别是水体、水体、水体、植被；四季的平均城市热岛强度随着夜间灯光的增加而增加；NDVI子区域平均热岛强度呈现先上升后下降的趋势；气压子区域平均热岛强度总体呈现逐步上升的趋势。这些结果揭示了热岛强度的季节变异特征，可为缓解城市热岛效应措施的制订提供依据。     

Land use and cover dynamics in Africa since the nineteenth century: warped terrestrial photographs of North Ethiopia

Quantitative research on land use and land cover (LUC) in Africa usually addresses the second half of the twentieth century, by using remote sensing data. Terrestrial photographs, which are available since 1868 in Ethiopia, are seldom used in a quantitative way. This paper presents a methodology that allows to produce land use and land cover (LUC) maps on the basis of old terrestrial photographs. Therefore, land use and land cover was investigated on historical and present-day photographs, and these interpretations were warped to the horizontal plane of the map. The resulting maps allow to gain better insights into LUC changes over a period of 140 years. The results show that woody vegetation increased strongly, together with an increase in built-up area. This occurred especially at the expense of bushland. The study validates pervious findings and shows that improved land management strategies in one of the world’s most degraded areas can lead to environmental rehabilitation.     

Impacts of land-use and land-cover changes on surface urban heat islands in Addis Ababa city and its surrounding

Environment, Development and Sustainability - Land-use and land-cover (LULC) change as a result of rapid urban expansion cause land surface temperature (LST) variations. The study aims to analyze...     

Estimation of land surface temperature using different retrieval methods for studying the spatiotemporal variations of surface urban heat and cold islands in Indian Punjab

Spatial patterns of land surface temperature (LST), surface urban heat island (SUHI), surface urban cold island (SUCI), and their seasonal variations during January (winter) and September (summer) were analyzed over the three cities of Indian Punjab (Balachaur, Ludhiana and Bathinda) using Landsat 5, 7 and 8 satellite data of the years 1991, 2001, 2011, and 2018. Urban hot spots and Urban Thermal Field Variance Index (UTFVI) were used to measure the ecological environment of these cities. Land surface temperature was retrieved from Landsat satellite data using Plank equation, mono-window algorithm (MWA), single-channel algorithm (SCA), and radiative transfer equation. The LST derived using these algorithms was validated with MODIS-LST product. The relationship between LST derived from Landsat 5, 7 and 8 using the four methods and MODIS-LST product was higher with the SCA algorithm (R²?>?0.75). Land surface temperature was significantly positively correlated with built-up but significantly negatively correlated with vegetation. The surface urban heat intensity was higher during September than January, and it was higher in Ludhiana followed by Bathinda and Balachaur, irrespective of the season. Besides built-up area and population density, soil moisture availability in surrounding rural areas has significant impact on increasing surface urban heat intensity during September than January. The SUCIs were formed in the center of Bathinda city during January 1991, but these were in Ludhiana and Balachaur cities during January 2011. The most critical areas for ecological environment based on UTFVI were identified and the critical UTFVI values (>?0.020) were highest in Bathinda city followed by Balachaur and Ludhiana cities. These results suggest that SUHIs and SUCIs are influenced by seasons and the mitigating plans to counteract the overheating of urban areas should be formulated taking into account soil moisture availability in surrounding rural areas, landscape pattern, seasonal variations, local climatic conditions, urban growth, and development plan etc.

     

Impacts of future land use/land cover on wildfire occurrence in the Madrid region (Spain)

This paper assesses the relative importance of socioeconomic factors linked to fire occurrence through the simulation of future land use/land cover (LULC) change scenarios in the Madrid region (Spain). This region is a clear example of the socioeconomic changes that have been occurring over recent decades in the European Mediterranean as well as their impact on LULC and fire occurrence. Using the LULC changes observed between 1990 and 2006 as a reference, future scenarios were run up to 2025 with the conversion of land use and its effects model. Simultaneously, the relationship between LULC arrangement (interfaces) and historical fire occurrence was calculated using logistic regression analysis and used to quantify changes in future fire occurrence due to projected changes in LULC interfaces. The results revealed that it is possible to explain the probability of fire occurrence using only variables obtained from LULC maps, although the explanatory power of the model is low. In this context, border areas between some LULC types are of particular interest (i.e., urban/forest, grassland/forest and agricultural/forest interfaces). Results indicated that expected LULC changes in Euro-Mediterranean regions, particularly given the foreseeable increase in the wildland–urban interface, will substantially increase fire occurrence (up to 155 %). This underlines the importance of future LULC scenarios when planning fire prevention measures.     

扬子江城市群土地利用时空变化及其对陆地生态系统碳储量的影响

扬子江城市群是江苏省城市化进程最快,土地利用变化最明显的区域。研究该地区地利用变化及其对陆地生态系统有机碳储量的影响,对江苏省低碳土地利用研究具有重要意义。该文利用五期30 m土地利用栅格数据、土壤样点数据、林地植被清查数据、农作物数据以及经验数据,分析了1995～2015年扬子江城市群土地利用时空变化、核算了其对有机碳储量的影响。主要结果如下:(1) 1995～2015年间,扬子江城市群约有15. 90%的土地发生了转移,其中,耕地作为主要的转出者,建设用地作为主要的转入者,耕地转移为建设用地的面积约为4 161. 78 km~2,占扬子江城市群耕地转出面积的85. 86%,是主要的土地转移类型;(2) 1995～2015年间,由于土地利用类型转移变化,扬子江城市群有机碳储量总量减少了472. 63×10~4t,其中土壤有机碳储量总量增加110. 28×10~4t,植被碳储量总量减少582. 91×10~4t;(3)建设用地占用耕地是区域有机碳储量减少的主要原因,导致有机碳储量减少406. 40×10~4t,占整个区域有机碳储量减少总量的85. 99%;(4)未来扬子江城市群可通过增加生态用地、控制建设用地、优化土地利用结构,提高区域碳储量,减少对陆地生态系统碳平衡的扰动。     

Evaluating land cover change and its impact on hydrological regime in Upper Shire river catchment, Malawi

A study was conducted to investigate hydrological impacts of land cover changes in the degradation of the hydrological on flow regimes of the Upper Shire river, Malawi. Remote sensing techniques were used to inventory temporal changes of land cover changes in the catchment. Hydrological data were analyzed to reveal the alterations and trends for two periods; 1989 and 2002. The study revealed significant changes in magnitude and direction that have occurred in the catchment between 1989 and 2002, mainly in areas of human habitation. Trends in land cover change in the Upper Shire river catchment depict land cover transition from woodlands to mostly cultivated/grazing and built-up areas. The land cover mapping showed that 23% of the land was covered by agricultural land in 1989. Subsistence agricultural area has increased by 18%, occupying 41% of the study area in 2002. The effects of the derived land cover changes on river flow in the Upper Shire river were investigated using the semi distributed soil and water assessment tool (SWAT) model. River flows were found to be highly variable and sensitive to land cover changes. Simulation results show that 2002 land cover data produces higher flow peaks and faster travel times compared to the 1989 land cover data. The changes detected indicate the effects of land use pressure in the catchment. The study highlights the importance of considering effects of land use and land cover changes on ecosystems, and water resources for an informed decision on proper catchment planning and management.     

长三角地区植被退化的空间格局及影响因素分析

植被退化是陆地生态系统功能下降的重要表征。利用2000~2010年SPOT-VEGETATION NDVI遥感数据、土地利用数据,采用趋势分析、叠加分析和格网计算方法,对长三角植被退化的空间格局及其影响因素进行分析。结果表明:10a间长三角地区最大化NDVI均值空间分布南北高、中间低,区域14%的植被覆盖面积显著退化。植被退化呈现一定的空间分异,而人类活动是导致植被退化的主要原因。在长江以南、太湖的北部与东部,环杭州湾地区及南京、南通和台州等城市快速无序的城镇化,造成了建设用地面积增加和耕地的破碎化,使植被退化区域集中连片分布。经济利益驱动农民将水田转化为向水产养殖,引起植被退化。部分区域植被面积未减少而呈退化态势的原因有待研究。     

2000～2015年丹江口库区植被覆盖时空变化趋势及其成因分析

基于MODIS NDVI、Landsat遥感影像及气象观测数据，应用趋势分析、偏相关分析和土地利用转移矩阵等方法，阐明了2000~2015年丹江口库区植被覆盖时空变化趋势，并探讨了气候变化和人类活动对库区植被覆盖的影响。结果表明：（1）近16 a来丹江口库区植被覆盖度呈增加的趋势，增速为4.73%/10 a（p < 0.001）；（2）40.94%的区域植被覆盖度增加显著，主要分布在库周丘陵和平原地带；10.04%的区域植被覆盖度减少显著，主要位于西北部伏牛山区及库区建成区周边；49.02%的区域变化不显著；（3）丹江口库区植被覆盖度受气候变化影响不显著，但受人类活动影响较大，其中灌草地和农业用地转变为林地是库区植被覆盖度升高的主要原因，农业用地转变为水体和建设用地是部分区域植被覆盖度降低的重要因素，这些土地利用/覆被变化主要受造林、退耕还林、水库蓄水以及建设活动的驱动；（4）生态建设工程和项目的实施对库区植被覆盖度的稳步增加起到了积极作用。 关键词： 植被覆盖度；变化趋势；土地利用；丹江口库区     

Forest land conversion dynamics: a case of Pakistan

The present research focuses on estimating forest area change with respect to the ongoing forest land conversion. The study tests the hypothesis that forest land is being converted to the selected land use categories with high growth tendency and controlling deforestation rate to its half of the present level would significantly improve the land cover under forest. The rate of forest land conversion to other land use categories is analyzed and then compared with the total area expanded under three land use classes. We used Systems dynamic modeling approach to estimate forest area by simulating forest land conversion in Pakistan, projecting to 2030. The results show that rangeland, built-up area and cultivated land have increased over time; growth rate of built-up area (9%) is highest followed by rangeland area (1.7%) and cultivated area (0.3%), respectively; rangeland is the dominant category in terms of area expanded (0.74 m. ha). Sensitivity analysis showed that reducing deforestation rate to half of the past level of 1% could not significantly improve forest area; therefore, it is suggested that controlling deforestation alone is not an effective strategy unless drastic measures, e.g., demarcation of forest boundaries, land use control policy and afforestation campaign would be started in the country.