Regional analysis of indirect factors affecting the recovery,degradation and deforestation in the tropical dry forests of Oaxaca,Mexico

This study evaluates the dynamics and identifies the indirect biophysical and socio‐economic factors related to the recovery, degradation and deforestation of the tropical dry forest (TDF) cover in the municipality of Tehuantepec, Oaxaca, Mexico. Annual rates and transition matrices were determined to identify indirect factors; the cartographic information of 25 variables with shift points were overlaid and Generalized Linear Models (GLM) were applied. The change process with the greatest impact in TDF during the study period (1993–2011) was degradation, with 10468 ha degraded (12 per cent of the initial tropical cover); recovery of coverage was the second most important change process, with 4808 ha (5.5 per cent); and deforestation was the change process with the lowest impact, with a loss of 2800 ha (3.23 per cent). The net balance was negative, with a decrease (through land degradation and deforestation) of 8460 ha (9.75 per cent). The recovery of coverage was mainly associated with biophysical factors such as land suitability and accessibility to natural vegetation. On the other hand, deforestation and degradation of coverage were associated with both biophysical and socioeconomic factors such as land suitability, accessibility to natural vegetation, migration, marginalization, population pressure, economy, education and health. The findings of this study determined the spatial distribution of forest recovery, deforestation and degradation processes at a regional level, allowing for future researchers to focus their efforts at local and landscape levels. Also, the work allows for an approximation of the factors associated with the change processes studied, hence supporting the allocation of resources for the establishment of management, conservation, development and restoration strategies of tropical dry forests at the regional level.     

大渡河上游地区土地利用动态模拟分析

基于1967、1987和2000年三期遥感数据和1:25万数字高程模型,通过Logistic逐步回归分析,在地形、海拔、水系、道路交通、城镇和居民点分布等多种自然地理和社会经济因素中,筛选出不同时期对大渡河上游地区主要土地利用类型空间分布及其变化具有决定作用的驱动因子,并生成相应的土地利用空间分布概率适宜图,然后采用CLUE-S模型,模拟分析了1987年和2000年两个时点上金川、壤塘和马尔康三县18665 km2范围内的土地利用状况。用当年的现状图检验对比的结果表明,模拟取得了较为理想的结果,Kappa值分别达到0.86和0.89。在此基础上,针对三种政策情景,应用相同模型模拟预测了研究地区2010年时的土地利用时空变化。     

The tropical forest in south east Asia: Monitoring and scenario modeling using synthetic aperture radar data

Tropical forests play a major role in storing large carbon stocks and regulating energy, and water fluxes, but such forest cover is decreasing rapidly in spite of the policy attention on reducing deforestation. High-resolution spatiotemporal maps are unavailable for the forests in majority of the tropical regions in Asia because of the persistent cloud cover and haze cover. Recent advances in radar remote sensing have provided weather-independent data of earth surface, thus offering an opportunity to monitor tropical forest change processes with relatively high spatiotemporal resolutions. In this research, we aim to examine the tropical deforestation process and develop a spatial model to simulate future forest patterns under various scenarios. Riau Province from central Sumatra of Indonesia is selected as the study area; this province has received much attention worldwide because the highest CO₂ emission resulting from tropical deforestation has been recorded. Annual time series PALSAR data from 2007 to 2010 were analyzed for forest mapping and detecting land cover changes. A spatial model was calibrated using the Bayesian method. Modeling parameters were customized for the local subregions that allocate deforestation on the basis of their empirical relationships to physical and socioeconomic drivers. The model generated landscape spatial patterns mirrored the possible locations and extent of deforested areas by 2030 and provided time-series crucial information on forest landscape under various scenarios for future landscape management projects. The results suggested that the current deforestation process is in a critical stage where some subregions may face unprecedented stress on primary forest costing rivers and forest ecosystems by the end of 2020. The perspective views of Riau Province generated by the model highlighted the need for forest/environmental planning controls for the conservation of environmentally sensitive areas.     

基于大型水利工程建设阶段的三峡库区土地利用变化遥感分析

使用5 期TM/ETM数据,借助动态度、综合指数和程度变化指标,旨在对比理解不同建设阶段三峡库区土地利用变化的特征与轨迹。结果表明：① 耕地与林草地间的转换、耕地和林地被建设占用、林草地互换和耕地、林地与草地被水体淹没是三峡工程整个建设20 年来土地利用转换的主要方式;② 不同建设阶段因驱动者出现的时序不同和作用程度的差异,土地利用及其驱动者在总体格局框架下体现出显著的细部轨迹;③ 整个工程建设的20 年土地利用程度综合指数相对平稳,处于中等以上水平,而利用程度的变化总体呈降低趋势,带有正“W”型的动态格局;④ 主要土地利用转换方式在空间上的分布广度和集聚度具有较大差异,主体性工程对主要土地利用转换方式的影响呈现强异质性。研究结论有助于丰富对水利工程胁迫下土地利用的理解,为未来适应性土地利用调控政策的制定提供科学依据。     

The spatial dynamics of deforestation and agent use in the Amazon

The objective of this study was to better understand the linkages between spatial patterns of forest use, land use, and rates of deforestation. We focused on a region of the Brazilian Amazon characterized by a variety of spatial patterns of deforestation and that experienced rapid deforestation in the last decade: the colonization project “Pedro Peixoto” in the extreme East of Acre State. By combining time series of remote sensing data and household survey data, we tested the hypothesis that each spatial pattern of deforestation is associated with specific socio-economic characteristics of land managers. The statistical results support our hypothesis. However, spatial patterns of deforestation are not a consequence of the main economic activities. Rather, spatial patterns of forest use are linked to the pre-conceived design of the settlement project. The main economic activities are influenced by factors such as the property size, their location and disposition, and the availability of infrastructure.     

Agricultural land use dynamics in the Brazilian Amazon based on remote sensing and census data

The potential impact of deforestation in the Brazilian Amazon on greenhouse gas emissions to the atmosphere calls for policies that take account of changes in forest cover. Although much research has focused on the location and effects of deforestation, little is known about the distribution and reasons for the agricultural uses that replace forest cover. We used Landsat TM-based deforestation and agricultural census data to generate maps of the distribution and proportion of four major agricultural land uses throughout the Brazilian Amazon in 1997 and 2007. We built linear and spatial regression models to assess the determinant factors of deforestation and those major agricultural land uses - pasture, temporary agriculture and permanent agriculture - for the states of Pará, Rondônia, and Mato Grosso. The data include 30 determinant factors that were grouped into two years (1996 and 2006) and in four categories: accessibility to markets, public policies, agrarian structure, and environment. We found an overall expansion of the total agricultural area between 1997 and 2007, and notable differences between the states of Pará, Rondônia, and Mato Grosso in land use changes during this period. Regression models for deforestation and pasture indicated that determinant factors such as distance to roads were more influential in 1997 than in 2007. The number of settled families played an important role in the deforestation and pasture, the effect was stronger in 2007 than 1997. Indigenous lands were significant in preventing deforestation in high-pressure areas in 2007. For temporary and permanent agricultures, our results show that in 1997 the effect of small farms was stronger than in 2007. The mapped land use time series and the models explain empirically the effects of land use changes across the region over one decade.     

Land use changes and trends in Human Appropriation of Above Ground Net Primary Production (HANPP) in India (1961–98)

Land use change has the potential to influence energy flows, standing crop and biomass turnover at a variety of scales. To understand and quantify the impact of land use change on natural systems, interdisciplinary approaches and concepts linking biophysical and socioeconomic factors are needed. One such approach for analysing the socioeconomic energy flows from land use changes is Human Appropriated Net Primary Production (HANPP). HANPP is defined as the difference between the net primary production of potential vegetation and the actual net primary production remaining in vegetation after harvest. In this study, we used HANPP to quantify the energy flows consequent upon land use/land cover change from 1961 to 1998 in India. Data from the Food and Agricultural Organization Statistics (FAOSTAT) covering land use/land cover estimates, crop production, harvest and fertilizer use data have been used to analyse the trends in HANPP. Land use changes between 1961 and 1998 indicate a small increase in agricultural areas from 58.2% to 60.8% and in forest and woodland areas from 19.0% to 23.2%. Actual above ground net primary production of vegetation increased from 1280.58 to 1818.23 Tg, an increase of approximately 1.4 times over 37 years. Although population increased exponentially, human appropriation of photosynthetic products did not rise as much due to increases in forest cover and agricultural production. Although the HANPP concept is a useful approach to analyse the trends in photosynthetic products brought about by land use changes and harvesting, more robust indicators are needed to understand the impacts associated with these changes. We discuss the relevance of the HANPP analysis results by focusing on land use/land cover change and exploring the inter-linkages between energy flows and environment.     

Rural sustainability under threat in Zimbabwe – Simulation of future land use/cover changes in the Bindura district based on the Markov-cellular automata model

Spatially explicit land use/cover models are indispensable for sustainable rural land use planning, particularly in southern African countries that are experiencing rapid land use/cover changes. Using Zimbabwe as an example, we simulated future land use/cover changes up to 2030 based on a Markov-cellular automata model that integrates Markovian transition probabilities computed from satellite-derived land use/cover maps and a cellular automata spatial filter. A multicriteria evaluation (MCE) procedure was used to generate transition potential maps from biophysical and socioeconomic data. Dynamic adjustments of transition probabilities and transition potential map thresholds were implemented in the Markov-cellular automata model through a multi-objective land allocation (MOLA) procedure. Using the normalised transition probabilities, the Markov-cellular automata model simulated future land use/cover changes (up to 2030) under the 2000 calibration scenario, predicting a continuing downward trend in woodland areas and an upward trend in bareland areas. Future land use/cover simulations indicated that if the current land use/cover trends continue in the study area without holistic sustainable development measures, severe land degradation will ensue.     

Driving forces of tropical deforestation: The role of remote sensing and spatial models

Remote sensing technologies are increasingly used to monitor landscape change in many parts of the world. While the availability of extensive and timely imagery from various satellite sensors can aid in identifying the rates and patterns of deforestation, modelling techniques can evaluate the socioeconomic and biophysical forces driving deforestation processes. This paper briefly reviews some emerging spatial methodologies aimed at identifying driving forces of land use change and applies one such methodology to understand deforestation in Mexico. Satellite image classification, change analysis and econometric modelling are used to identify the rates, hotspots and drivers of deforestation in a case study of the southern Yucatán peninsular region, an enumerated global hotspot of biodiversity and tropical deforestation. In particular, the relative roles of biophysical and socioeconomic factors in driving regional deforestation rates are evaluated. Such methodological approaches can be applied to other regions of the forested tropics and contribute insights to conservation planning and policy.     

基于RS和GIS的西双版纳土地覆被动态变化

人口增加、经济发展导致滇南热带地区西双版纳土地利用/土地覆被发生显著变化,这些变化对该区的生态环境及生物多样性保护有着重要影响。基于RS和GIS方法,通过对1976、1988和2003年三个时期遥感影像解译分析,试图了解西双版纳近27 a间土地利用/土地覆被时空变化特征及影响因素。结果表明,近27 a间有林地在西双版纳分布最广、占绝对优势,其中以亚热带常绿阔叶林面积最大;但在研究时段内,有林地面积不断减少、呈现破碎化,尤其是热带季节雨林、山地雨林面积减少最为明显和破碎化最为严重。相应,橡胶园、灌木林面积不断扩大并聚集成片,其中橡胶园在1988~2003年间是面积增长最快、最多的地类,其面积扩张主要来源于对热带季节雨林的砍伐。轮歇地面积在1976~1988年是所有地类中增长最快、最多的,在1988~2003年由于向橡胶园转变而大幅度减小。粗放式的刀耕火种和橡胶种植园的不断扩大是主导本区土地利用/土地覆被变化的主要因素,土地利用变化导致了本区生态环境的退化和生物多样性的丢失。     

北京市城区扩展的空间格局与影响因素

2000年以来北京市持续推进产业结构升级,由此引发了经济活动的空间重构,伴随着住宅郊区化、职住分离以及交通基础设施的配套建设,将引起新一轮城市空间扩展。构建顾及综合可达性因素、邻域因素、自然因素、规划因素以及社会经济因素的城市空间扩展模型,分析诊断北京城区扩展进程中的主要影响因素及问题,从而为指导与管治北京市城市空间的有序扩展提供科学支撑。研究表明：① 综合交通静态可达性（单一年份）与城市扩展呈显著负相关;综合交通动态可达性（年份之间的可达性变化）则在引导北京市城市空间发展方面并无显著影响。② 邻域建设用地百分比与城市扩展呈正相关,而农用地、林业用地及水域用地的比例将减少土地开发的概率。③ 城市总体规划在指导城市空间扩展的纲领性地位逐渐受到重视,其对城区扩展的影响程度最为显著。④ 常住人口的分布现状、第二产业企业数量、第三产业企业数量分布现状对土地开发无显著影响。⑤ 认为北京市静态可达性与土地利用变化呈负相关,即可达性条件较差的地区,土地开发的可能性越小,这与北京市“摊大饼式”空间蔓延和居住郊区化的发展现实相符;北京市动态可达性与土地利用变化亦呈负相关,表明可达性提高程度越大的区域并不一定带来更高的土地开发几率,这从侧面传递了沿着既有建成区进行土地扩展的概率远远大于由于诸如轨道交通建设而带来可达性极大改善区域的概率。因此,重视与发挥综合交通动态可达性指标对城市扩展的引导作用,将是有效破解城市蔓延式扩张、优化城市扩展的重要手段。     

广州市土地覆被格局异质性的尺度与等级特征

基于SPOT遥感数据源形成广州市土地覆被类型图,在中心城区选取互相垂直的南北和东西两条样带,用Fortran编程计算各样带在125m、250m、500m和1000m 4个幅度下的景观多样性指数,以及Moran I、Geary C系数和半变异函数。再将样带分别自北向南和自西向东等间距划分,形成一系列面积相等的区域,计算各区域以及样带整体上的多样性指数的尺度方差。结果表明,土地覆被景观多样性在不同的研究幅度下都存在正的空间自相关性,并具有方向性。尺度方差结果显示,尺度方差与尺度和幅度都有关,随着研究尺度增大,尺度方差都呈下降之势,而随幅度增大,尺度方差并非单调变化。如125m和250m两种幅度时,方差随着尺度增大而减少。尺度方差结果进一步揭示研究样带上土地覆被存在多尺度等级结构,并且具有方向性特点,同时也反映尺度方差不失为景观异质性研究的一种有效方法。     

Land use/cover, landforms and fragmentation patterns in a tropical dry forest in the southern Pacific region of Mexico

In spite of widely documented studies of deforestation rates and land use/cover changes in tropical dry forests in Mexico, relatively little is known about fragmentation patterns in such forests. This study defines the spatial distribution of landforms and land use/cover types the lower Papagayo River basin and examines their influence on fragmentation patterns and biological diversity in a tropical dry forest in that southern Pacific region. The land use/cover map was constructed from aerial photographs, Landsat TM imagery (2000) and fieldwork. Landform units were defined based on altitude, slope, lithology and morphology. Landscape fragmentation parameters were obtained using FRAGSTATS (version 3.3) considering the numbers of patches, mean, minimum and maximum patch size, edge density, total edge and connectivity. Results show tropical dry forest to be remnant vegetation (~11 per cent), characterized by isolation and low connectivity. Land use/cover types have different effects on fragmentation patterns. Agriculture and cattle raising produce similar numbers of patches, but with a different mean size; and human settlements have a scattered distribution pattern. The abandonment of rural agricultural livelihoods has favoured the expansion of secondary tropical dry forest characterized by continuity and high connectivity, which suggests a high regeneration potential from land abandonment. It can be concluded that tropical dry forest fragmentation and recovery at regional scales depend on such landscape attributes as lithology, slope, geomorphology and management.     

Broad scale forest cover reconstruction from historical topographic maps

Land cover change is one of the major contributors to global change, but long-term, broad-scale, detailed and spatially explicit assessments of land cover change are largely missing, although the availability of historical maps in digital formats is increasing. The problem often lies in efficiency of analyses of historical maps for large areas. Our goal was to assess different methods to reconstruct land cover and land use from historical maps to identify a time-efficient and reliable method for broad-scale land cover change analysis. We compared two independent forest cover reconstruction methods: first, regular point sampling, and second, wall-to-wall mapping, and tested both methods for the Polish Carpathians (20,000 km²) for the 1860s, 1930s and 1970s. We compared the two methods in terms of their reliability for forest change analysis, relative to sampling error, point location and landscape context including local forest cover, area of the spatial reference unit and forest edge-to-core ratio. Our results showed that the point-based analysis overestimated forest cover in comparison to wall-to-wall mapping by 1–3%, depending on the mapping period. The reasons for the differences were mainly the backdating approach and map generalisation rather than the point grid position or sampling error. When we compared forest cover trajectories over time, we found that the point-based reconstruction captured forest cover dynamics with a comparable accuracy to the wall-to-wall mapping. More broadly, our assessment showed that historical maps can provide valuable data on long-term land cover trends, and that point-based sampling can be an efficient and accurate way to assess forest area and change trends. We suggest that our point-based approach could allow land cover mapping across much of Europe starting in the 1800s. Our findings are important because they suggest that land cover change, a key component of global change, can be assessed over large areas much further back in time than it is commonly done. This would allow to truly understand path dependencies, land use legacies, and historical drivers of land cover change.     

Combining land transitions and trajectories in assessing forest cover change

One restriction of landscape studies is that land use and cover change is often regarded as irreversible. A highly dynamic landscape in southern Chile was selected to show that forest cover change involves a series of complex transitions and trajectories. Using Landsat images from 1976, 1985, 1999 and 2007 an in-depth analysis of the transition matrix was conducted to separate random and systematic transitions which were grouped into trajectories using a pixel-history approach. Main trajectories were linked to fragmentation indices and farming systems through cluster analysis. Of the 247 trajectories identified, old growth forest persistence comprised 22% of the landscape, whereas deforestation trajectories comprised 20.9% and were mostly composed of changes from old growth forest to shrubland (13.9%). Trajectories of forest degradation from old growth to secondary forest comprised 19.7% of the landscape. The periods 1976-1985 and 1999-2007 concentrated the most systematic deforestation and degradation transitions. In turn, random transitions predominated between 1985 and 1999, probably in response to economic factors that acted suddenly on the landscape during the 80’s, such as the woodchip export and aquaculture booms. A close relationship between landscape fragmentation and the proportion of systematic transitions and farming systems was found; specifically, the highest entropy indices occurred in clusters which exhibited the lowest proportion of systematic transitions and the highest proportion (>70%) of peasant agricultural systems. Understanding the complexity of forest cover change trajectories is relevant for improving the prediction of possible landscape evolutions and establishing landscape management priorities.     

Land tenure and forest cover change. The case of southwestern Beni,Bolivian Amazon, 1986–2009

As land use change continues to increase throughout the Amazon basin, there is a pressing need to accurately map, quantify and assess the effects of different factors on forest cover change (FCC). Land tenure may sometimes have important effects on forest cover, yet such effects remain poorly understood in Amazonia, particularly outside Brazil. In this paper we assess whether significant differences in trends of FCC can be partially explained by different land tenure arrangements, using a case study in southwestern Beni (Bolivian Amazon). We examine spatio-temporal dynamics of FCC across four land tenure systems (indigenous titled territory, protected area, logging concession, and private land) by classifying forests using a time-series of Landsat satellite imagery consisting of four dates (1986, 1996, 2001, 2009). Specifically, we unravel (1) trends in early growth and old-growth forest extent, including changes in total cover area, annual change rates, and spatial change dynamics, and (2) trends in old-growth forest fragmentation. To better understand the association between land tenure and FCC, we qualitatively assess the potential role that other underlying and proximate drivers may have had in FCC over the study period. We found that private lands underwent, by far, the largest FCC, that indigenous territories and the protected area had little FCC, and that logging concessions were responsible for the lowest FCC. Our findings suggest that land tenure played a key role in FCC except in private areas, where many other drivers had operated. Our study sheds light into the potential role of land tenure in FCC and has important implications for public policies aimed at socioeconomic development and environmental conservation in the Amazon. We give some policy recommendations drawn from a biocultural conservation perspective that could contribute to implement more inclusive conservation policies in the region.     

中国土地利用变化生态环境效应的空间分异性与形成机理

中国复杂的自然要素、社会经济要素、区域发展战略和政策调控形成了当前中国不均衡的经济发展空间格局、土地利用空间格局和生态环境质量空间格局。厘清中国生态环境质量的空间格局及形成机理对中国土地资源的可持续利用和生态环境的有效保护具有重要的实践意义和价值。以往研究缺乏对中国县域尺度土地利用/土地覆被变化生态环境效应的空间分异性及形成机理的相关研究,本研究基于1995—2015年间中国土地利用现状遥感监测数据,尝试采用生态环境质量指数方法测度中国土地利用变化的生态环境效应,并且综合运用重心分析、热点分析工具（Getis-Ord G_i*）和地理探测器等研究方法,分析1995—2015年中国生态环境质量时空演变特征及形成机理。研究结果如下：① 东部季风区生态环境质量指数高于青藏高寒区和西北干旱区,东部季风区生态环境质量低值区主要分布在人口和经济集聚的城市群以及省会城市周边地区。研究期间中国生态环境质量重心持续向西北方向迁移;② 1995—2015年间中国生态环境质量变化的热点区域主要分布在西藏地区、新疆、重庆、贵州以及位于黄土高原的省份（青海、甘肃、宁夏、内蒙古、山西、陕西以及河南）境内,冷点区域主要分布在长江经济带沿线地区省份以及东南部沿海地区省份;③ 土地利用程度对于生态环境质量影响力显著强于其他因子,东部季风区的社会经济和交通区位因子对生态环境质量影响力强于青藏高寒区、西北干旱区和生态大区过渡带;④ 影响因子之间相互作用主要包括非线性增强作用和双因子增强作用两种类型,以非线性增强作用为主。     

北京市土地利用空间格局演化模拟及预测

土地利用空间格局的演化模拟可定量地从空间尺度揭示区域土地利用变化的驱动因素,是厘清未来时期内土地变化的重要途径。基于CLUE-S模型,以北京市为研究案例,结合1985、2000和2010年三期土地利用数据,运用Logistic逐步回归方法识别了北京市各种土地利用类型演化的驱动因素,对北京市土地利用空间格局进行模拟。在此基础上,基于北京市社会经济发展、土地利用规划、资源禀赋及生态保护等不同情景,对北京市2020年土地利用空间分布格局进行模拟及预测。结果表明：①不同的时期内,驱动因子对不同土地利用类型的影响呈现差异性,其中交通因素及社会经济因素对土地利用类型的转化率影响较显著,坡度对各个土地利用类型的影响较大。②通过对2010年北京市土地利用变化的模拟结果来看,Kappa指数为87.03%,说明预测结果与实际土地利用情况有较好的一致性。③预测结果显示,北京市的城市发展均表现为继续向外扩展,且以东南、东北为主要扩展方向,但扩张的程度存在差异。     

岷江上游近30年土地利用/覆被空间格局变化的图形信息分析

利用遥感的方法获取岷江上游1972-2000年土地利用／覆被状况及其动态变化数据,从图形表达与分析的角度人手,运用地理图形信息理论,结合GIS空间分析方法,探讨岷江上游近30a的土地利用空间格局变化的图形特征及其描述方法。研究认为：土地利用／覆被动态变化的图形信息是全面研究土地利用／覆被变化的一个有机组成成分,可以从图形的角度对土地利用／覆被变化信息进行挖掘;由于岷江上游人类活动对土地的扰动增加和土地的破碎度增加,该区重要的覆被类型——林地持续遭到破坏,林地的边缘趋于复杂化,反映了人文驱动力(包括人口压力、社会经济活动、政策调控等)对土地利用／覆被变化在30a时间尺度内的重要影响。要恢复和重建岷江上游生态屏障功能,必须提高全民的环境保护意识以及制定和执行相应的政策、法规,加强资源和环境的保护和合理利用,恢复森林、草地生态系统,搞好退耕还林还草,加强天然林保护以及大面积有规划的植树造林,控制畜牧数量,改良天然草地,治理退化草地,控制水土流失。     

Landscape change in the Calakmul Biosphere Reserve,Mexico: Modeling the driving forces of smallholder deforestation in land parcels

This article uses remote sensing and spatial modeling to quantify and analyze land change in Mexico's largest protected area, the Calakmul Biosphere Reserve. Change trajectories are identified within distinct property regimes and between the Reserve's core and buffer zones. A parcel-level spatial econometric model identifies the driving forces of land use change in two communities located along the eastern edge of the Reserve, the locus of increased deforestation in 1987–1996. The study assesses the role of biophysical variables, locational context, household socioeconomics and institutional factors in driving deforestation. The results address the effectiveness of reserves and other state policy instruments in protecting forests.