共查询到20条相似文献，搜索用时 46 毫秒
1.
Natural forces and anthropogenic activities greatly alter land cover,deteriorate or alleviate forest frag-mentation and affect biodiversity.Thus land cover and forest fragmentation dynamics have become a focus of concern for natural resource management agencies and biodiversity con-servation communities.However,there are few land cover datasets and forest fragmentation information available for the Dhorpatan Hunting Reserve (DHR) of Nepal to develop targeted biodiversity conservation plans.In this study,these gaps were filled by characterizing land cover and forest frag-mentation trends in the DHR.Using five Landsat images between 1993 and 2018,a support vector machine algorithm was applied to classify six land cover classes:forest,grass-lands,barren lands,agricultural and built-up areas,water bodies,and snow and glaciers.Subsequently,two landscape process models and four landscape metrics were used to depict the forest fragmentation situations.Results showed that forest cover increased from 39.4％ in 1993 to 39.8％ in 2018.Conversely,grasslands decreased from 38.2％ in 1993 to 36.9％ in 2018.The forest shrinkage was responsible for forest loss during the period,suggesting that the loss of for-est cover reduced the connectivity between forest and non-forested areas.Expansion was the dominant component of the forest restoration process,implying that it avoided the occurrence of isolated forests.The maximum value of edge density and perimeter area fractal dimension metrics and the minimum value of aggregation index were observed in 2011,revealing that forests in this year were most fragmented.These specific observations from the current analysis can help local authorities and local communities,who are highly dependent on forest resources,to better develop local forest management and biodiversity conservation plans. 相似文献
2.
This study aims at developing a satellite-based methodology for the implementation of two Ministerial Conference on the Protection
of Forests in Europe indicators for the European Alpine Bio-geographic region, and their changes over time: (1) area of forest
cover and (2) forest spatial pattern. The northern Carpathians were selected as a study area due to the documented recent
increase of forest cover. Changes of forest cover were quantified using Landsat images for the years 1987 and 2000. Single-date
forest–non-forest maps were derived by image segmentation and supervised classification, including the use of ancillary data
(CORINE Land Cover and a digital elevation model). These maps were an input for the post-classification change detection.
The forest spatial pattern maps with four classes (core, patch, edge and perforated forest) were derived with morphological
image processing. A simple method to mask uncertainty areas on forest maps and related products was also developed. The accuracy
of the resulting forest–non-forest map was assessed with orthophotos and amounts to 93.9%. Uncertainty areas, for which change
assessment was judged more difficult and less reliable, were not considered for assessing forest cover change. The annual
forest cover change rate of 0.38% was found over the 1987–2000 period. For the 13-year time period, we found a decrease of
core forest and an increase of patch and perforated forest. We conclude that the proposed methodology allows to quantify changes
of forest cover and forest spatial pattern at ∼1 ha minimum mapping unit.
相似文献
| C. Estreguil |
3.
Satellite monitoring of land-use and land-cover changes in northern Togo protected areas 总被引：1，自引：0，他引：1
Fousseni Folega Chun-yu Zhang Xiu-hai Zhao Kperkouma Wala Komlan Batawila Hua-guo Huang Marra Dourma Koffi Akpagana 《林业研究》2014,25(2):385-392
Remote-sensing data for protected areas in northern Togo, obtained in three different years （2007, 2000, and 1987）, were used to assess and map changes in land cover and land use for this drought prone zone. The normalized difference vegetation index （NDVI） was applied to the images to map changes in vegetation. An unsupervised classification, followed by classes recoding, filtering, identifications, area computing and post-classification process were applied to the composite of the three years of NDVI images. Maximum likelihood classification was applied to the 2007 image （ETM＋2007） using a supervised classification process. Seven vegetation classes were defined from training data sets. The seven classes included the following biomes： riparian forest, dry forest, flooded vegetation, wooded savanna, fallows, parkland, and water. For these classes, the overall accuracy and the overall kappa statistic for the classi- fied map were 72.5% and 0.67, respectively. Data analyses indicated a great change in land resources; especially between 1987 and 2000 proba- bly due to the impact of democratization process social, economic, and political disorder from 1990. Wide-scale loss of vegetation occurred during this period. However, areas of vegetation clearing and regrowth were more visible between 2000 and 2007. The main source of confusion in the contingency matrix was due to heterogeneity within certain classes. It could also be due to spectral homogeneity among the classes. This research provides a baseline for future ecological landscape research and for the next management program in the area. 相似文献
4.
We mapped the forest cover of Khadimnagar National Park （KNP） of Sylhet Forest Division and estimated forest change over a period of 22 years （1988-2010） using Landsat TM images and other GIS data. Supervised classification and Normalized Difference Vegetation Index （NDVI） image classification approaches were applied to the images to produce three cover classes, viz. dense forest, medium dense forest, and bare land. The change map was produced by differencing classified imageries of 1988 and 2010 as before image and after image, respectively, in ERDAS IMAGINE. Error matrix and kappa statistics were used to assess the accuracy of the produced maps. Overall map accuracies resulting from supervised classification of 1988 and 2010 imageries were 84.6% （Kappa 0.75） and 87.5% （Kappa 0.80）, respec- tively. Forest cover statistics resulting from supervised classification showed that dense forest and bare land declined from 526 ha （67%） to 417 ha （59%） and 105 ha （13%） to 8 ha （1%）, respectively, whereas medium dense forest increased from 155 ha （20%） to 317 ha （40%）. Forest cover change statistics derived from NDVI classification showed that dense forest declined from 525 ha （67%） to 421 ha （54%） while medium dense forest increased from 253 ha （32%） to 356 ha （45%）. Both supervised and NDVI classification approaches showed similar trends of forest change, i.e. decrease of dense forest and increase of medium dense forest, which indicates dense forest has been converted to medium dense forest. Area of bare land was unchanged. Illicit felling, encroachment, and settlement near forests caused the dense forest decline while short and long rotation plantations raised in various years caused the increase in area of medium dense forest. Protective measures should be undertaken to check further degradation of forest at KNP. 相似文献
5.
Forest cover and land use change directly impact biological diversity worldwide, contribute to climate change and affect the
ability of biological systems to support human needs by altering ecosystem services. Given the forest land use characteristics
and ecosystem types in Luang Namtha Province, Lao PDR, the forest cover and land cover category of Luang Namtha Province were
divided into six classes, i.e., current forest (CF), potential forest (PF), other wooded areas (OW), permanent agricultural
land (PA), other non-forest areas (NF) and water (W). In first instance, earlier geographic information data (GIS data) of
forest cover and land use during 1992 and 2002 was obtained from the Ministry of Agriculture and Forestry (MAF), Lao PDR.
Two steps of forest land use change assessment were conducted by the MAF, i.e., plot sampling on satellite image maps (SIMs)
to detect the changes of forest cover and land use during 1992 and 2002 for the entire Luang Namtha Province and field verification
in order to identify causes of the changes. Secondly, dynamic information of the forest land cover changes during this ten-year
period was calculated by means of map algebra in ArcGIS 9.2. Thirdly, based on the theory of ecosystem service functions and
the service function values of different global ecosystems, the value of the six forest cover and land use categories in the
province was obtained. Finally, ecological environmental effects, produced by the regional land cover changes over the study
period, were calculated. 相似文献
6.
Daxing'an Mountains was one of the most important forest areas in China, but it was also an area which was prone to suffering
forest fire. The catastrophic forest fire that occurred in Daxing'an Mountains on May 6, 1987 devastated more than 1.33×106 hm2 of natural forests, which leaded to the formation of some mosaic areas with different burn intensities. Two forest farms
of Tuqiang Forest Bureau (124°05′–122°18′E, 53°34′–52°15′N) were chosen as a typical area to analyze the post-fire landscape
change by drawing and comparing the two digital forest stand maps of 1987 and 2000. The landscape lands of forest were classified
into 12 types: coniferous forest, broadleaf forest, needle-broadleaf mixed forest, shrub, nursery, harvested area, burned
blanks, agricultural land, swamp, water, built-up, grass. The results showed that: 1) The burned blanks was almost restored,
some of them mainly converted into broadleaf forest land during the process of natural restoration, and coniferous forest
land by the artificial reforestation, and the others almost changed into swamp or grass land; 2) The proportion of forest
area increased from 47.6% in 1987 to 81.3% in 2002. Therefore, a few management countermeasures, such as the enhancing people's
consciousness of fire-proofing and constructing species diversity, were put forward for forest sustainable development.
Foundation item: Under the auspices of the National Science Foundation of China (No. 30270225, 40331008) and the Chinese Academy of Sciences
(SCXZY0102).
Biography: KONG Fan-hua (1975-), female, Ph.D. candidate of Hiroshima University in Japan, specialized in Landscape Ecology.
Responsible editor: Zhu Hong 相似文献
7.
【目的】利用多极化星载SAR数据,分析后向散射强度比值影像的概率密度分布特征,融合后向散射强度信息和影像空间上下文信息,提出一种具有较高检测正确率及较低虚警率和漏警率的森林覆盖变化检测方法,为多极化SAR卫星数据的业务化应用提供技术支撑。【方法】将"2期分别分类森林覆盖变化检测法"(CBFC)与"贝叶斯最大期望-马尔科夫随机场(EM-MRF)变化检测法"相结合,首先采用阈值分割法分别对2期多极化SAR影像进行森林-非森林分类得到初始森林覆盖变化图,然后以初始森林覆盖变化图作为训练数据对多极化比值影像进行Fisher特征变换和EM-MRF分类处理,2个时相的HH、HV极化比值影像经Fisher特征变换转化为一个综合差异影像,输入EM-MRF进行迭代分类得到森林覆盖变化检测结果。以黑龙江省逊克县为试验区,以2期ALOSPALSAR双极化数据为SAR遥感数据,以对2期Landsat-5影像、高空间分辨率遥感影像进行目视解译得到的森林覆盖变化图为参考,对本研究提出方法的有效性与CBFC方法及直接用CBFC提取的森林覆盖变化检测图掩膜EM-MRF地表覆盖变化检测图方法(CBFC-EM-MRF)进行比较评价。【结果】通过Fisher特征变换得到的差异影像可有效增强森林覆盖变化、未变化类别的对比度;CBFC通过阈值分割法进行森林-非森林分类,提取的森林覆盖变化图中出现很多面积很小的虚警检测,漏警率也很高,而本研究提出方法通过MRF加入影像空间上下文信息,提高了检测结果的空间连贯性,森林覆盖变化检测虚警率为1.58%,漏警率为11.87%,正确率为98.36%,检测效果和精度明显优于CBFC和CBFC-EM-MRF。【结论】多极化星载SAR森林覆盖变化检测方法具有收敛性好、检测结果可信度高、需要用户交互较少等特点,对我国高分三号及未来其他多极化SAR卫星的森林资源监测业务应用具有重要参考价值。 相似文献
8.
Remote sensing offers the potential to spatially map forest cover quickly and reliably for inventory purposes. We developed a new image analysis approach using an integrated methodology of “object-based” image classification techniques and field-based measurements to quantify forest cover in a degraded dry forest ecosystem on the leeward side of the Island of Hawaii. This new approach explicitly recognized the transitional areas between tree crowns and tree shades (tree shadows) as a unique class and fully utilized them for the quantification of canopy cover. Object-oriented classification of Ikonos-2 satellite images allowed delineation of tree shades and crowns and the transitional areas between them from objects with similar reflectance and size that were surrounding the trees. These included patches of fountain (Pennisetum setaceum) and kikuyu (Pennisetum clandestinum) grass, lava outcrops and lava–grass mixtures. Crown-shade transitions were clearly differentiated in spite of their wide range of spectral values and reflectance similarities with areas of lava–grass mixture. Segments representing tree shades and dark lava outcrops were also classified into their respective classes even if they were contiguous. The image estimates of canopy cover using the tree shade plus transition classes were linearly related with field estimates of canopy cover (R2 = 0.86 and slope = 0.976). Based on this relationship, dry forest cover throughout the 2627-ha area was estimated at 7.7 ± 1.9%. An immediate application of this new approach is to select and delineate areas with higher canopy cover in order to concentrate ecological restoration and conservation efforts. 相似文献
9.
以山西省古交市嘉乐泉乡为试验区,采用SPOT-5的10m、5m和2.5m 3种影像数据对退耕还林地面积进行分类监测。所设计的2种方案分别是:1)将地物类型分为7类,退耕还林地作为一种单独地类,对3种影像数据进行计算机自动分类和2.5m影像的人工解译分类;2)借助退耕还林作业设计图,将退耕还林地块影像分割出来,对退耕还林地和未退耕还林地进行有监分类。精度验证表明,第一种方案中2.5m融合图像的人工解译分类,退耕还林地的分类精度在50%以下;第二种方案中3种影像数据的总体分类精度均大于90%。建议在退耕还林地的作业设计图电子化的基础上,应用SPOT-5数据监测退耕还林地的任务完成和植被覆盖情况。 相似文献
10.
Johannes Stoffels Sebastian Mader Joachim Hill Willy Werner Godehard Ontrup 《European Journal of Forest Research》2012,131(4):1071-1089
Due to high variation in forest communities, forest structure and the fragmentation of the forested area in Central Europe, satellite-based forest inventory methods have to meet particularly high-quality requirements. This study presents an innovative method to combine official forest inventory information at stand level with multidate satellite imagery using a spatially adaptive classification approach for producing wall-to-wall forest cover maps of important tree species and management classes across multiple ownership regions in a heterogeneous low mountain range in Germany. The classification approach was applied to a 5,200-km2 area (about 2,080?km2 of forest land, mostly mixed forests) located in the Eifel mountain range in Central Europe. In comparison with conventional classifiers, our results demonstrate a significant increase in classification accuracy in the order of 12%. The method was tested with ASTER images but holds the potential to be used for regular state forest inventories based on standard and novel earth observation data supplied for instance from the SPOT-5 and RapidEye sensors. 相似文献
11.
12.
Like many similar forest species, ruffed grouse (Bonasa umbellus; hereafter grouse) populations in the central and southern Appalachians (CSA) are strongly affected by forest composition at the landscape scale. Because these populations are in decline, managers require accurate forest maps to understand how stand level characteristics affect the survival and reproductive potentials of individual birds to design management strategies that improve grouse abundance. However, traditional mapping techniques are often labor-intensive and cost-prohibitive. We used a normalized difference vegetation index (NDVI) from each of 8 Landsat images and the digital elevation model (DEM)-derived variables of elevation and aspect in discriminant analyses to classify 7 study areas to 3 overstory classes (evergreen, hardwoods, and oak) and distinguish evergreen and deciduous understories in the CSA, 2000–2002. Overall accuracy was 82.08%, varying from 83.59% for oak to 79.79% for hardwoods overstories. Periods with large phenological differences among classes, particularly early and late spring, were most useful for discriminating overstory vegetation types. Alternatively, winter NDVI in combination with elevation was critical for differentiating evergreen and deciduous understories. Multitemporal image sets used in concert with DEMs provided a cost-effective alternative to hyperspectral sensors for improving wildlife habitat classification accuracy with Landsat imagery. This allowed for enhanced understanding of grouse-habitat relationships and habitat affects on grouse populations that allowed for improved management. With the incorporation of simple adjustments for local forest plant species phenology into the model, it may be used to better classify wildlife habitat of similar species in areas with comparable forest communities and topography. Multitemporal images can also be used to differentiate grassland communities, monitor wetlands, and serve as baseline data for detecting changes in land use over longer temporal scales, making their use in forest wildlife habitat studies cost-justifiable. 相似文献
13.
借助ERDAS软件,运用遥感数据融合技术对四川省蓬安县进行基于ETM＋融合影像的土地覆盖分类初步研究。研究中采用相同的训练样本区及最大似然法分类方法,对融合前后影像分别进行土地覆盖分类,通过对分类影像的Producers Accuracy,Users Accuracy,Kappa Accuracy三者的精度数据对比分析,上述的影像融合方法对提高土地覆盖分类的精度较为明显;就3种融合方法及重采样方式而言,乘积法融合法和立方卷积重采样法相对更为可取。 相似文献
14.
Vegetation cover types on Changbai Mountain, a natural biosphere reserve (2,000 km2) in northeast China, were derived by using multisensor satellite imagery fused with Landsat TM and SPOT HRV-XS. DEM data
were used for improving classification accuracy. Cover types were classified into 20 groups. Bands 4 and 5 of Landsat TM image
acquired on July 18, 1997, and band 1 of SPOT HRV-XS image acquired on Oct. 19, 1992, were fused to a false color image, and
maximum likelihood supervised classification was performed. Data fusion showed high accuracy of identification, compared to
individual images. The overall accuracy of classification of individual images by SPOT HRV-XS reached 56%, and TM 66%, while
the fused data set provided accuracy of about 78%, which was raised to 81% after recoding by using DEM. There were five vegetation
zones on the mountain, from the base to the peak: hardwood forest zone, mixed forest zone, conifer forest zone, birch forest
zone, and tundra zone. Spruce-fir dominated conifer forest was the most prevalent (nearly 50%) vegetation type, followed by
Korean pine and mixed forest (17%) and larch forest (5%). HRV image taken in leaf-off season is useful for discriminating
forest from non-forest, and evergreen forest from hardwood forest, while the summer image (TM) provides detailed information
on the difference in similar vegetation types, like hardwood forest with different compositions. 相似文献
15.
In Maine and other heavily forested states, existing land cover maps quickly become dated due to forest harvesting and land use conversion; therefore, these maps may not adequately reflect landscape properties and patterns relevant to current resource management and ecosystem studies. By updating an older land cover product (the 1993 Maine GAP map) using Landsat imagery and established forest change detection techniques, we demonstrate a practical and accurate means of providing contemporary, spatially explicit forest cover data needed to quantify landscape change. For a 1.8 million hectares study area in northern Maine, we quantify the accuracy of forest harvest classes and compare mapped harvest and regeneration area between the 2004 GAP update product and the 2004 Maine Landcover Dataset (MeLCD), a map recently developed in coordination with the 2001 National Land-Cover Database (NLCD). For the period 1995–2004, the overall harvest/non-harvest accuracy of the GAP update map is 87.5%, compared to 62.1% for the MeLCD. Producer and user accuracy for harvest detection is 92.4% and 89.7%, respectively for the GAP update, and 48.8% and 92.5% for the MeLCD. Mapped harvest area differs considerably, reflecting a systematic under-representation of recent harvest activity on the part of the MeLCD. By integrating older land cover data, the GAP update retains the forest disturbance legacies of the late 1970s through the early 1990s while simultaneously depicting 2004 forest composition for harvested and regenerating stands. In contrast, the MeLCD (and 2001 NLCD) over-represents the area and connectivity of older forest (undisturbed since the late 1970s), and provides no forest composition information for mapped forest regeneration. Systematic misclassification of forest age classes and harvest history has serious implications for studies focused on wildlife habitat modeling, forest inventory, and biomass or carbon stock estimation. We recommend the integration of older land cover data and time-series forest change detection for retention of harvest or disturbance classes when creating new forest and land cover maps. 相似文献
16.
17.
Following the severe drought in 1999–2000 there was a widespread outbreak of oak decline in the Ozark Highlands. Over 400,000 ha of dead and dying oak trees were observed by the USDA Forest Service in this region. Although oak forests that are dead can be easily interpreted from air photos or classified from satellite images, it is difficult to detect dying trees that are still green but will die back or recover in the following years. In this study, we applied a normalized difference water index (NDWI) to map the continuous forest dynamics related to oak decline. The Landsat TM image in 1992 and the ETM+ image in 2000 were processed to calculate the differential NDWI which revealed moisture variation primarily caused by the drought and the associated red oak borers. A simple thresholding method was used to map oak dying back, recovery and non-change areas in the study area. The died-back areas were extracted from the modified land use/land cover maps created by the Missouri Resource Assessment Partnership (MoRAP). The forest dynamics map was compared with the online FIA database in which tree species at randomly selected sites were recorded in 1989 and 2003. The overall accuracy of forest dynamics mapping with remote sensing imagery was 75.95%. The user's accuracy of dying/recovery area mapping was also high although the producer's accuracy is questionable because of the limitation in ground data collection. The continuous dying/recovery map in this study could provide valuable information on the prediction of oak decline and evaluation of damage when another period of environmental stresses occurs. 相似文献
18.
基于GIS的老挝-中国边境土地适用性评估 总被引：1，自引：0，他引：1
对老挝-中国边境林地使用变更进行了评估,并提出了茶叶的土地适用性。建立一个综合的基于GIS的分析系统（IGAS）,该系统支持老挝-中国边境的林地使用和土地适用性的评估研究。采用多标准分析和系统动态技术评估林地使用和土地适用性,同时预测了潜在的茶叶用地。收集到的土地数据和数据分析表明,整体研究区域为10325.07 km2,当前的森林覆被由1992年的6337.33 km2（占61.38%）减少到2002年的5106.28 km2（占49.46%）。当前的森林主要被转为潜在林地和专门的永久农业用地,甚至国家生物多样性保护区域被转为橡胶种植区。林地使用转变的主要原因是贫穷。为了解决这个问题,根据多标准方法,我们对研究区域中适用茶叶种植土地提出了2种分类方案。 相似文献
19.
高光谱数据森林类型统计模式识别方法比较评价 总被引：4，自引：0，他引：4
在我国东北地区获取EO-1 Hyperion高光谱数据,以高空间分辨率的全色SPOT-5数据及其影像分割结果为辅助,通过外业测量获取真实可靠的森林类型空间分布数据.以这些数据为地面实状数据,对现代先进的统计模式识别方法用于森林类型识别的效果进行比较评价,总结可以有效解决有限样本条件下高光谱分类问题的基于统计模式识别的森林类型分类技术方案.评价结果表明:对高光谱数据进行降维处理,并采用更加有效的二阶统计量估计方法,进而应用将空间上下文信息和光谱信息相结合的分类算法,如ECHO,可以有效提高高光谱数据森林类型的识别精度. 相似文献
20.
Information on land use and cover changes (LUCC) is important for planning of conservation and development and thus ensure forest sustainability. The current paper assesses LUCC for the whole of the mainland Tanzania. The analyses were done using land use and cover maps covering the whole of mainland Tanzania for 1995 and 2010. For 1995, forest, bushland, grassland, cultivation and other land use and cover (built up areas, bare land, etc.) covered 43.5%, 19.8%, 23.5%, 11.2%, and 2.0% of the study area, respectively. For 2010, the same land use and cover classes covered 38.0%, 14.5%, 6.9%, 36.5%, and 4.1% of the study area, respectively. The annual rate of deforestation was 320,067 ha, which is equivalent to 0.9%. Bushland and grassland were lost at 313,745 and 969,982 ha/year, respectively. Most forest was converted to cultivation and least to other land use and cover. In conclusion, the net changes were deforestation and loss of bushland and grassland primarily due to expansion of cultivation. Further research on how to reduce or halt expansion of cultivation may shed light on improving sustainability of forest, bushland, and grassland in mainland Tanzania. 相似文献