Forest Cover Change in the Toledo District,Belize from 1975 to 1999: A Remote Sensing Approach*

Measuring land‐cover change is an essential part of sustainable conservation planning. This project uses Landsat MSS and ETM+ data to document forest cover change in the Toledo District, Belize, from 1975 to 1999 and provides an initial assessment of why these changes took place. Supervised and subpixel classification methods were employed. The results showed an aggregate forest loss of almost 10 percent, which is approximately 36,000 ha. Deforestation expanded significantly in the most populous Mayan areas of central Toledo District and along the Guatemalan border. Subpixel classification results showed that in 1999 the most densely forested areas were in northern Toledo District in the Maya Mountains.     

Urban Expansion Pattern and Land Use Dynamics in Dhaka, 1989–2014

Rapid change in land use and land cover (LULC) and unplanned urban expansion in Dhaka City, Bangladesh, receives continuous attention from local policymakers and the international community. This study employed a supervised classification procedure and postclassification change detection technique to estimate major changes between different LULC classes. The study revealed that built-up area increased significantly from 1989 to 2014. The total urban growth of 81.54 percent resulted in a substantial decrease in natural vegetation cover and agricultural land. In addition, water bodies have declined consistently over the last twenty-five years. The overall accuracy of LULC change maps produced from Landsat data ranged from 89.72 percent to 92.97 percent. The results should contribute to ongoing LULC information updates while forecasting possible future LULC change and sustainable development under greater population density.     

Assessment of spatial and temporal dynamics of tropical forest cover: A case study in Malkangiri district of Orissa, India

Tropical forests have been recognized as having global conservation importance. However, they are being rapidly destroyed in many regions of the world. Regular monitoring of forests is necessary for an adaptive management approach and the successful implementation of ecosystem management. The present study analyses the temporal changes in forest ecosystem structure in tribal dominated Malkangiri district of Orissa, India, during 1973–2004 period based on digitized forest cover maps using geographic information system (GIS) and interpretation of satellite data. Three satellite images Landsat MSS (1973), Landsat TM (1990) and IRS P6 LISS III (2004) were used to determine changes. Six land cover types were delineated which includes dense forest, open forest, scrub land, agriculture, barren land and water body. Different forest types were also demarcated within forest class for better understanding the degradation pattern in each forest types. The results showed that there was a net decrease of 475.7 km2 forest cover (rate of deforestation = 2.34) from 1973 to 1990 and 402.3 km2 (rate of deforestation = 2.27) from 1990 to 2004. Forest cover has changed over time depending on a few factors such as large-scale deforestation, shifting cultivation, dam and road construction, unregulated management actions, and social pressure. A significant increase of 1222.8 km2 agriculture area (1973–2004) clearly indicated the conversion of forest cover to agricultural land. These alterations had resulted in significant environmental consequences, including decline in forest cover, soil erosion, and loss of biodiversity. There is an urgent need for rational management of the remaining forest for it to be able to survive beyond next decades. Particular attention must be paid to tropical forests, which are rapidly being deforested.     

Comments On Mather,Field, Kalkstein,And Willmott'S “Climatology: The Challenge For The Eighties”

Abstract

Measuring land-cover change is an essential part of sustainable conservation planning. This project uses Landsat MSS and ETM+ data to document forest cover change in the Toledo District, Belize, from 1975 to 1999 and provides an initial assessment of why these changes took place. Supervised and subpixel classification methods were employed. The results showed an aggregate forest loss of almost 10 percent, which is approximately 36,000 ha. Deforestation expanded significantly in the most populous Mayan areas of central Toledo District and along the Guatemalan border. Subpixel classification results showed that in 1999 the most densely forested areas were in northern Toledo District in the Maya Mountains.

     

基于RS和GIS的喀斯特山区景观生态格局

喀斯特地区生态脆弱性特征受各种自然和人为因素综合作用的影响。本文选取桂西南典型喀斯特山区，集成GIS和RS技术提取景观生态格局信息，运用景观生态学的基本理论和方法，在FRAGSTATS*ARC软件支持下，以分维数、形状指数、景观优势度、景观破碎度和相邻指数等景观格局指标，对该区脆弱生态环境的景观格局进行定量分析。结果表明石漠斑块已经占据景观的主导地位，景观格局呈现明显的破碎现象，必须采取综合措施进行生态重建。     

Land cover changes in the rural-urban interaction of Xi'an region using Landsat TM/ETM data

Landsat ETM/TM data and an artificial neural network (ANN) were applied to analyse the expansion of the city of Xi'an and land use/cover change of its surrounding area between 2000 and 2003. Supervised classification and normalized difference barren index (NDBI) were used respectively to retrieve its urban boundary. Results showed that the urban area increased by an annual rate of 12.3%, with area expansion from 253.37 km2 in 2000 to 358.60 km2 in 2003. Large areas of farmland in the north and southwest were converted into urban construction land. The land use/cover changes of Xi'an were mainly caused by fast development of urban economy, population immigration from countryside, great development of infrastructure such as transportation, and huge demands for urban market. In addition, affected by the government policy of "returning farmland to woodland", some farmland was converted into economic woodland, such as Chinese goosebeery garden, vineyard etc.     

利用Landsat TM/ETM数据分析西安地区城乡土地利用/覆被变化

Landsat ETM/TM data and an artificial neural network (ANN) were applied to analyse the expansion of the city of Xi'an and land use/cover change of its surrounding area between 2000 and 2003. Supervised classification and normalized difference barren index (NDBI) were used respectively to retrieve its urban boundary. Results showed that the urban area increased by an annual rate of 12.3%, with area expansion from 253.37 km2 in 2000 to 358.60 km2 in 2003. Large areas of farmland in the north and southwest were converted into urban construction land. The land use/cover changes of Xi'an were mainly caused by fast development of urban economy, population migration from countryside, great development of infrastructure such as transportation, and huge demands for urban market. In addition, affected by the government policy of "returning farmland to woodland", some farmland was converted into economic woodland, such as Chinese goosebeery garden, vineyard etc.     

Land cover changes in the rural-urban interaction of Xi＇an region using Landsat TM/ETM data

Landsat ETM/TM data and an artificial neural network （ANN） were applied to analyse the expansion of the city of Xi＇an and land use/cover change of its surrounding area between 2000 and 2003. Supervised classification and normalized difference barren index （NDBI） were used respectively to retrieve its urban boundary. Results showed that the urban area increased by an annual rate of 12.3%, with area expansion from 253.37 km^2 in 2000 to 358.60 km^2 in 2003. Large areas of farmland in the north and southwest were converted into urban construction land. The land use/cover changes of Xi＇an were mainly caused by fast development of urban economy, population immigration from countryside, great development of infrastructure such as transportation, and huge demands for urban market. In addition, affected by the government policy of “returning farmland to woodland”, some farmland was converted into economic woodland, such as Chinese goosebeerv garden, vineyard etc.     

Utility of Landsat 7 satellite data for continued monitoring of forest cover change in protected areas in Southeast Asia

Satellite instruments, particularly the Landsat TM (Thematic Mapper) and ETM+ (Enhanced Thematic Mapper Plus) series of sensors, are important tools in the interdisciplinary study of tropical forests that are increasingly integrated into studies that monitor changes in vegetation cover within tropical forests and tropical protected areas, and also applied with other types of data to investigate the drivers of land cover change. However, further advances in the use of Landsat to study and monitor tropical forests and protected areas are threatened by the scan line corrector failure on the ETM+ sensor, as well as uncertainty about the continuity of the Landsat mission. Given these problems, this paper illustrates how ETM+ data were used in an interdisciplinary study that effectively monitored forest cover change in Gunung Palung National Park in West Kalimantan, Indonesian Borneo. Following 31 May 2003, when the ETM+ sensor's scan line corrector failed, we analysed how this failure impedes our ability to perform a similar study from this date onwards. This analysis uses six simulated post-scan line corrector failure (SLC-off) images and reveals that data gaps caused by SLC-off introduce maximum errors of 1.47 per cent and 4.04 per cent in estimates of forest cover and rates of forest loss, respectively. The analysis also demonstrates how SLC-off has transformed ETM+ data from a complete inventory dataset to a statistical sample with variable sample fraction, and notes how this data loss will confound the use of Landsat data to model land cover change in a spatially explicit manner. We discuss potential limited uses of SLC-off data and suggest alternative sensors that may provide essential remotely sensed data for monitoring tropical forests in Southeast Asia.     

基于Landsat 8 OLI数据与面向对象分类的昆嵛山地区土地覆盖信息提取

利用2015年Landsat 8 OLI遥感影像和DEM作为分类数据源,结合野外调查数据,采用面向对象的分类方法对昆嵛山地区土地覆盖信息进行提取,并对分类结果进行精度评价与比较分析。研究表明：面向对象分类方法提取的各地类连续且边界清晰,分类效果与实际情况基本吻合。昆嵛山地区占主导地位的土地覆盖类型是针叶林,面积为1 546.81 km²。研究区土地覆盖分类的总体精度和Kappa系数分别为91.5%和0.88,其中针叶林、草地、水体和建设用地的生产者精度均达到87%以上。相对于监督分类方法,本研究提出的土地覆盖信息提取方法的总体分类精度和Kappa系数分别提高14.7%和0.17。基于面向对象的中分辨率遥感影像,能够获取较高精度的土地覆盖信息,为大范围土地覆盖分类研究提供方法参考。     

Land use and land cover change in Greater Dhaka, Bangladesh: Using remote sensing to promote sustainable urbanization

This study evaluates land use/cover changes and urban expansion in Greater Dhaka, Bangladesh, between 1975 and 2003 using satellite images and socio-economic data. Spatial and temporal dynamics of land use/cover changes were quantified using three Landsat images, a supervised classification algorithm and the post-classification change detection technique in GIS. Accuracy of the Landsat-derived land use/cover maps ranged from 85 to 90%. The analysis revealed that substantial growth of built-up areas in Greater Dhaka over the study period resulted significant decrease in the area of water bodies, cultivated land, vegetation and wetlands. Urban land expansion has been largely driven by elevation, population growth and economic development. Rapid urban expansion through infilling of low-lying areas and clearing of vegetation resulted in a wide range of environmental impacts, including habitat quality. As reliable and current data are lacking for Bangladesh, the land use maps produced in this study will contribute to both the development of sustainable urban land use planning decisions and also for forecasting possible future changes in growth patterns.     

Monitoring Growth in Rapidly Urbanizing Areas Using Remotely Sensed Data

Urbanization and the ability to manage for a sustainable future present numerous challenges for geographers and planners in metropolitan regions. Remotely sensed data are inherently suited to provide information on urban land cover characteristics, and their change over time, at various spatial and temporal scales. Data models for establishing the range of urban land cover types and their biophysical composition (vegetation, soil, and impervious surfaces) are integrated to provide a hierarchical approach to classifying land cover within urban environments. These data also provide an essential component for current simulation models of urban growth patterns, as both calibration and validation data. The first stages of the approach have been applied to examine urban growth between 1988 and 1995 for a rapidly developing area in southeast Queensland, Australia. Landsat Thematic Mapper image data provided accurate (83% adjusted overall accuracy) classification of broad land cover types and their change over time. The combination of commonly available remotely sensed data, image processing methods, and emerging urban growth models highlights an important application for current and next generation moderate spatial resolution image data in studies of urban environments.     

Monitoring Growth in Rapidly Urbanizing Areas Using Remotely Sensed Data

Urbanization and the ability to manage for a sustainable future present numerous challenges for geographers and planners in metropolitan regions. Remotely sensed data are inherently suited to provide information on urban land cover characteristics, and their change over time, at various spatial and temporal scales. Data models for establishing the range of urban land cover types and their biophysical composition (vegetation, soil, and impervious surfaces) are integrated to provide a hierarchical approach to classifying land cover within urban environments. These data also provide an essential component for current simulation models of urban growth patterns, as both calibration and validation data. The first stages of the approach have been applied to examine urban growth between 1988 and 1995 for a rapidly developing area in southeast Queensland, Australia. Landsat Thematic Mapper image data provided accurate (83% adjusted overall accuracy) classification of broad land cover types and their change over time. The combination of commonly available remotely sensed data, image processing methods, and emerging urban growth models highlights an important application for current and next generation moderate spatial resolution image data in studies of urban environments.     

热带森林覆被的时空动态评价——以印度Malkangiri district of Orissa为例

Tropical forests have been recognized as having global conservation importance. However,they are being rapidly destroyed in many regions of the world. Regular monitoring of forests is necessary for an adaptive management approach and the successful implementation of ecosystem management. The present study analyses the temporal changes in forest ecosystem structure in tribal dominated Malkangiri district of Orissa,India,during 1973-2004 period based on digitized forest cover maps using geographic information system (GIS) and interpretation of satellite data. Three satellite images Landsat MSS (1973),Landsat TM (1990) and IRS P6 LISS III (2004) were used to determine changes. Six land cover types were delineated which includes dense forest,open forest,scrub land,agriculture,barren land and water body. Different forest types were also demarcated within forest class for better understanding the degradation pattern in each forest types. The results showed that there was a net decrease of 475.7 km2 forest cover (rate of deforestation = 2.34) from 1973 to 1990 and 402.3 km2 (rate of deforestation = 2.27) from 1990 to 2004. Forest cover has changed over time depending on a few factors such as large-scale deforestation,shifting cultivation,dam and road construction,unregulated management actions,and social pressure. A significant increase of 1222.8 km2 agriculture area (1973-2004) clearly indicated the conversion of forest cover to agricultural land. These alterations had resulted in significant environmental consequences,including decline in forest cover,soil erosion,and loss of biodiversity. There is an urgent need for rational management of the remaining forest for it to be able to survive beyond next decades. Particular attention must be paid to tropical forests,which are rapidly being deforested.     

珠江三角洲土地覆盖变化对地表温度的影响

由于经济的快速增长,1978年以来的25年中,珠江三角洲地区的土地覆盖变化,特别是城市扩展速度非常之快。通过把遥感和地理信息系统相结合,研究了土地覆盖变化及城市增长状况,并且评估了城市增长对地表温度的影响。利用陆地卫星多时段数据监测了土地利用和土地覆盖变化;通过陆地卫星热红外单波段反演了地表温度,研究了土地覆盖变化特别是城市增长对地表温度的影响。结果发现,珠江三角洲地区土地覆盖变化明显,城市增长显著且不平衡。城市的发展使城市化地区的地表温度提高了8.9K。研究表明,遥感和地理信息系统的结合,在监测和分析土地覆盖变化,特别是城市增长状况以及评估城市化对地表温度的影响方面,是十分有效的。     

Impacts of land use and fire on the loss and degradation of lowland forest in 1983–2000 in East Kutai District, East Kalimantan, Indonesia

Deforestation and forest degradation are proceeding rapidly in the lowland forests of Indonesian Borneo. Time series analysis of satellite imagery provides an ideal means of quantifying landscape change and identifying the pathways which lead to the changes. This study investigates the forest and land cover changes by classifying Landsat MSS (Multispectral Scanner), TM (Thematic Mapper) and ETM+ (Enhanced Thematic Mapper Plus) images over three time periods (1983–90, 1990–98, and 1998–2000), creating land cover maps for each year and change trajectories for each year-pair. The study area chosen covers an area of 2160 km² of undulating topography and alluvial plains in the East Kutai District of East Kalimantan Province, which in the 1980s was covered mostly with lowland dipterocarp forest; today the landscape is a patchwork dominated by oil palm and timber plantations and degraded forest. We relate land cover change data to land use allocation and to fire impacts based on fire hotspot distribution and fire damage information. The multidate land cover change trajectories provide an insight into the forest loss and degradation pathways over the 17-year period spanning the first entry of commercial logging concessionaires, followed by a government-sponsored transmigration scheme, government-licensed timber and oil palm plantations and, finally, the devastating fires of 1998. The results show a mean deforestation rate of 42 km² or 6 per cent per year for 1983–2000, rising to 10 per cent per year for 1990–98; by 2000, 70 per cent of forest initially damaged by fire and drought during the 1982–83 El Niño event was classified as non-forest. Although our study area is perhaps a worst-case scenario in terms of land use planning outcomes, the lessons from this research are directly applicable to scenario prediction for informed forest and land use planning and monitoring.     

南京市热场分布特征及其与土地利用/覆被关系研究

文章采用Landsat ETM⁺热红外波段反演LST,分析南京市热场分布规律,构建了土地覆被指数LCI定量表示热场分布特征与土地利用/覆被的关系。结果表明:南京市存在三个连续分布且范围较广的热岛中心,主要分布在工业区;建成区平均温度比郊区高0.972℃;土地利用/类型的空间格局总体上决定了城市热场的空间分布,下垫面介质的热特征和生物学特征差异是地表温度不同的根本原因,工业热源是南京市热岛形成的重要因素;LCI可以和城市气候模型、水文模型结合起来运用,为城市热岛提供一种新分析方法。对某一特定区域而言,随着城市化水平的提高,单位面积内LCI值逐渐减小。     

Assessment of multifunctional landscapes dynamics in the mountainous basin of the Mo River (Togo,West Africa)

In this study, historical landscape dynamics were investigated to (i) map the land use/cover types for the years 1972, 1987, 2000 and 2014; (ii) determine the types and processes of landscape dynamics; and (iii) assess the landscape fragmentation and habitat loss over time. Supervised classification of multi-temporal Landsat images was used through a pixel-based approach. Post–classification methods included systematic and random change detection, trajectories analysis and landscape fragmentation assessment. The overall accuracies (and Kappa statistics) were of 68.86% (0.63), 91.32% (0.79), 90.66% (0.88) and 91.88% (0.89) for 1972, 1987, 2000 and 2014, respectively. The spatio-temporal analyses indicated that forests, woodlands and savannahs dominated the landscapes during the four dates, though constant areal decreases were observed. The most important dynamic process was the decline of woodlands with an average annual net loss rate of–2%. Meanwhile, the most important land transformation occurred during the transition 2000–2014, due to anthropogenic pressures. Though the most important loss of vegetation greenness occurred in the unprotected areas, the overall analyses of change indicated a declining trend of land cover quality and an increasing landscape fragmentation. Sustainable conservation strategies should be promoted while focusing restoration attention on degraded lands and fragmented ecosystems in order to support rural livelihood and biodiversity conservation.     

Land Cover Status in the Koshi River Basin,Central Himalayas

The Koshi River Basin is in the middle of the Himalayas, a tributary of the Ganges River and a very important cross-border watershed. Across the basin there are large changes in altitude, habitat complexity, ecosystem integrity, land cover diversity and regional difference and this area is sensitive to global climate change. Based on Landsat TM images, vegetation mapping, field investigations and 3S technology, we compiled high-precision land cover data for the Koshi River Basin and analyzed current land cover characteristics. We found that from source to downstream, land cover in the Koshi River Basin in 2010 was composed of water body (glacier), bare land, sparse vegetation, grassland, wetland, shrubland, forest, cropland, water body (river or lake) and built-up areas. Among them, grassland, forest, bare land and cropland are the main types, accounting for 25.83%, 21.19%, 19.31% and 15.09% of the basin’s area respectively. The composition and structure of the Koshi River Basin land cover types are different between southern and northern slopes. The north slope is dominated by grassland, bare land and glacier; forest, bare land and glacier are mainly found on northern slopes. Northern slopes contain nearly seven times more grassland than southern slopes; while 97.13% of forest is located on southern slopes. Grassland area on northern slope is 6.67 times than on southern slope. The vertical distribution of major land cover types has obvious zonal characteristics. Land cover types from low to high altitudes are cropland, forest, Shrubland and mixed cropland, grassland, sparse vegetation, bare land and water bodies. These results provide a scientific basis for the study of land use and cover change in a critical region and will inform ecosystem protection, sustainability and management in this and other alpine transboundary basins.     

Spatio-temporal characteristics of intra-urban land cover in the cities of China and USA from 1978 to 2010

Urban land cover has major impacts on a city''s ecosystem services and the inherent quality of its urban residential environment. The spatio-temporal distribution of impervious surface area and green areas in Chinese cities has exhibited a significantly marked difference in comparison with USA cities. This study focused on monitoring and comparing the spatio-temporal dynamics, land cover patterns and characteristics of functional regions in six Chinese (n=3) and USA (n=3) cities. The study data were collated from Landsat TM/MSS imagery during the period 1978-2010. Results indicate that Chinese cities have developed compactly over the past three decades, while development has been notably dispersed among USA cities. Mean vegetation coverage in USA cities is approximately 2.2 times that found amongst Chinese urban agglomerations. Land use types within Chinese cities are significantly more complex, with a higher density of impervious surface area. Conversely, the central business district (CBD) and residential areas within USA cities were comprised of a lower proportion of impervious surface area and a higher proportion of green land. Results may be used to contribute to future urban planning and administration efforts in both China and the USA.