基于多特征的高分辨率影像松材线虫病树识别

为了提高松材线虫病树的监测效率,减少其对林业生产造成的损失,利用在高分辨率遥感影像上提取松材线虫病树的光谱特征、空间特征等多特征,然后进行Relief特征选择算法,提取的合适特征为归一化植被指数NDVI(Normalized Vegetation Index)、差值植被指数DVI(Difference Vegetation Index)、OHTA颜色模型作为病树与非病树的光谱特征,对目标影像进行自动筛选,得到疑似病树像元。运用DBscan空间聚类算法对疑似病树像元进行聚类,并以周围一定范围内有一定数量的健康树像元为空间分布参考,对拍摄地点30°1′N/111°43′E附近、分辨率为0.1 m的3幅高分辨率遥感影像筛选病树。自动筛选耗时分别是人工筛选的43.99%、51.08%和46.62%,相对于人工筛选的数量准确度分别为79.37%、77.85%和82.56%。结果表明:采用光谱特征与空间特征相结合的方法在高分辨率遥感影像上识别松材线虫病树识别效率更高。     

北京永定河流域森林植被覆盖研究

利用1978～2009年间共6期TM遥感数据,采用归一化植被指数（NDVI）结合二值化分析方法确定植被覆盖度的阈值,对北京市永定河流域32年来的森林植被覆盖变化情况进行研究,揭示永定河流域森林植被覆盖变化的驱动力因子与作用机制．结果表明,永定河流域上游植被覆盖度较高,中下游覆盖度较低,整个研究时期内植被覆盖度呈现波动变化,1978～1987年间,植被覆盖度急剧下降,而1987～1995年间植被覆盖度略有提高,但之后又迅速下降,2000年植被覆盖度处于历年最低值,2000～2004年间植被覆盖度呈上升趋势,2004—2009年间植被覆盖度趋于平稳．驱动力分析表明,引起植被覆盖变化的主要驱动因素是人为因素,包括人类破坏和保护2方面,其次是气候因素,包括降水和温度,其中降水占主导地位．     

Relating MODIS vegetation index time-series with structure,light absorption and stem production of fast-growing Eucalyptus plantations

By allowing the estimation of forest structural and biophysical characteristics at different temporal and spatial scales, remote sensing may contribute to our understanding and monitoring of planted forests. Here, we studied 9-year time-series of the Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) on a network of 16 stands in fast-growing Eucalyptus plantations in São Paulo State, Brazil. We aimed to examine the relationships between NDVI time-series spanning entire rotations and stand structural characteristics (volume, dominant height, mean annual increment) in these simple forest ecosystems. Our second objective was to examine spatial and temporal variations of light use efficiency for wood production, by comparing time-series of Absorbed Photosynthetically Active Radiation (APAR) with inventory data.     

Monitoring responses of forest to climate variations by MODIS NDVI: a case study of Hun River upstream,northeastern China

This study analyzed the temporal variation of Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) of Hun River upstream forest in northeastern China and its correlation with climate parameters (temperature and precipitation) during the period of 2000–2009. We examined the interannual variation of forest, seasonal variation of forest and lag effects of climate variables (temperature and precipitation) on forest using simple regression and correlation. The objective of this paper was to compare the results of our research and previous researches and to show that the conclusions derived from broad-scale researches provided a direction of policy, but the local details were essential to local management. We found that the annual mean NDVI was significantly correlated with annual mean temperature. The forests studied in our research showed insignificant increase trends except for Fraxinus spp. forest. We concluded that the temperature was the limiting factor of vegetation growth in our study area and the forest which was in the core geographic area of its distribution was resilient to climate variation. When seasonal variation was examined, we found the largest increase trend of seasonal mean NDVI was in winter. The result was different from the outcome of previous research at national scale. There were 3 months lag effects of climate variables on vegetation of our study area in summer and autumn, which was consistent with researches at broad scales. The reasons of both difference and indifference were discussed in this paper. We also got information about tree species for local management using MODIS NDVI. The results of this work suggested that information from local scales would be important complements to researches at broad scales and were essential for local managers.     

Estimation of area burned by forest fires in Mediterranean countries: A remote sensing data mining perspective

Forest fires throughout the world result in tree mortality that can cause substantial timber and carbon losses. There is a critical need to map the areas burned by such fires to guide forest management decisions. Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery provides inexpensive and frequent coverage over large areas, facilitating forest health monitoring. In this study a MODIS post-fire image at a spatial resolution of 250 m serves as the starting point of an image mining based method. It involves three algorithms: modeling as a sum of Gaussian functions, kernel based smoothing, and adaptive thresholding. Adaptive thresholding serves as the reference to be compared to the image mining based method. Three spectral indices specifically designed for burned area identification have been used: the Burned Area Index (BAI), the Burned Area Index adapted to MODIS bands (BAIM), and the Normalized Burn Ratio (NBR). The κ statistic is applied to quantify the accuracy of the burned areas estimations by relating the estimated area with burned area perimeters measured on the ground by Global Positioning System (GPS). In addition, the κ statistic allows us to identify both the optimal spectral index and the optimal algorithms’ parameters. In this work, an accurate estimation (κ > 0.8) of areas burned by forest fires in Mediterranean countries is achieved, in particular if the BAIM index is used. The accuracy of these estimates is compared with the accuracy obtained by using the reference method by a McNemar’s test. Results show that our image mining based method allows a higher accuracy (the average increase of κ equals to 16%) than the reference method. We conclude that this method adequately maps burned areas, and that it may help management agencies to better understand of landscape-scale burn patterns.     

基于SPOT—VGT NDVI的恩施自治州地表植被覆盖变化分析

利用1999-2008年SPOT—VGTNDVI时间序列数据集分析了10年间恩施州植被覆盖变化,研究结果表明：恩施州植被覆盖状况在10年内有较明显的改善,NDVI年平均值从1999年的0．564增长到了2008年的0．604,99％以上的区域植被指数趋向于正向增长;东部地区植被覆盖状况优于西部地区,在全州8个县市中鹤蜂县2008年NDVI年平均值最高,利川市最低;10年间恩施州西南部的来凤县与咸丰县地表植被变化率最大,西北部的利川市与东部的巴东县和鹤峰县地表植被变化率明显落后于其他县市。区域植被监测变化表明天然林保护工程与退耕还林工程等林业工程的实施,恩施州植被覆盖状况有了较明显的提高,有效地改善了恩施州区域生态环境,区域经济社会可持续发展能力在稳步提高。     

Analysis of forest cover change at Khadimnagar National Park,Sylhet, Bangladesh,using Landsat TM and GIS data

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.     

Scale effects of vegetation and topography on burn severity under prevailing fire weather conditions in boreal forest landscapes of Northeastern China

Understanding the controlling factors of burn severity requires consideration of the scale at which these factors work. This investigation explored how well topography and vegetation factors can explain variation of burn severity in a boreal forest landscape of northern China under prevailing fire weather conditions. Eight grain sizes were examined that ranged from 30 to 2500 m. A burn severity map was derived from calculating the difference between pre- and post-fire Normalized Difference Vegetation Index of two Landsat Thematic Mapper images. Results indicate that (1) burn severity in the boreal forest landscape of northern China was mainly controlled by vegetation at grain sizes smaller than 500 m. At grain sizes larger than 1000 m, topography accounted for more variation in burn severity; (2) the relative importance of topography factors was stable with increasing grain sizes and generally ranked in order of aspect, slope, and elevation; (3) stand age appeared to be more important where canopy cover and understory cover substantially fluctuated with increasing grain sizes; and (4) the linear relationships between burn severity and specific factors of topography and vegetation decreased with increasing grain sizes. Our study can help managers to design fire management plans according to vegetation characteristics that are found important in controlling burn severity and prioritize management locations based on the relative importance of vegetation and topography.     

A 3D approach to reconstruct continuous optical images using lidar and MODIS

Background: Monitoring forest health and biomass for changes over time in the global environment requires the provision of continuous satellite images. However, optical images of land surfaces are generally contaminated when clouds are present or rain occurs.Methods: To estimate the actual reflectance of land surfaces masked by clouds and potential rain, 3D simulations by the RAPID radiative transfer model were proposed and conducted on a forest farm dominated by birch and larch in Genhe City, Da Xing'An Ling Mountain in Inner Mongolia, China. The canopy height model(CHM) from lidar data were used to extract individual tree structures(location, height, crown width). Field measurements related tree height to diameter of breast height(DBH), lowest branch height and leaf area index(LAI). Series of Landsat images were used to classify tree species and land cover. MODIS LAI products were used to estimate the LAI of individual trees. Combining all these input variables to drive RAPID, high-resolution optical remote sensing images were simulated and validated with available satellite images.Results: Evaluations on spatial texture, spectral values and directional reflectance were conducted to show comparable results.Conclusions: The study provides a proof-of-concept approach to link lidar and MODIS data in the parameterization of RAPID models for high temporal and spatial resolutions of image reconstruction in forest dominated areas.     

Vegetation inventory of a temperate biosphere reserve in China by image fusion of landsat TM and SPOT HRV

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.     

Satellite estimate of grass biomass in a mountainous range in central Italy

One of the main problems in managing ranges used for extensive pastoralism is the difficulty of obtaining reliable estimates of grass biomass over very large areas. Estimates of grass biomass are useful as an indicator of both available forage and risk of soil erosion. Nevertheless, large scale field measurements are expensive and time-consuming. The use of satellite images may provide a complementary means of estimating grass biomass over very large areas at a reasonable cost. The aim of this study was to test the use of Landsat satellite data for estimating grass biomass in a mountainous range in central Italy used primarily for sheep breeding. During each of four ground campaigns carried out over two years, grass was cut and its biomass measured in 60-90 test plots. Four Landsat images taken simultaneously to the ground campaigns were processed to obtain several vegetation indexes calculated for each ground test plot. The vegetation indexes showed significant correlations with measured grass biomass. The Normalized Difference Vegetation Index (NDVI) provided the most accurate estimate of grass biomass. When data for each of the four ground campaigns were analyzed separately, correlations for early summer campaigns were higher than correlations for late summer campaigns, indicating that when the ratio of dry/green biomass increases, satellite estimate becomes less accurate. Overall, our results show that satellite data can provide a useful source of biomass information for the management of large ranges. This revised version was published online in June 2006 with corrections to the Cover Date.     

Assessing changes in forest fragmentation following infestation using time series Landsat imagery

The current epidemic of mountain pine beetle (Dendroctonus ponderosae Hopkins) in British Columbia, Canada, has impacted an area of over 13 million hectares presenting a considerable challenge to provincial forest resource managers. Remote sensing technologies offer a highly effective tool to monitor this impact due to very large areas involved and its ability to detect dead and dying tree crowns. Conventionally, change detection procedures based upon spectral values have been applied; however, analysis of landscape pattern changes associated with long-time series change detection approaches present opportunities for the generation of unique and ecologically important information. This study is focussed on the detection and monitoring of the shape and area characteristics of lodgepole pine stands during mountain pine beetle infestation to quantify the progression of forest fragmentation and related loss of landscape connectivity. A set of landscape pattern indices were applied to a set of images consisting of six Landsat satellite images spanning the period from 1993 to 2006. Our results indicate that the impacts of the mountain pine beetle infestation on forest spatial pattern consist of an increase in the number of patches, an increase in forest patch shape complexity, a reduction in forest patch size, an increase in forest patch isolation, and a decrease in interspersion. These findings demonstrate the unique information available from long-time series satellite imagery combined with pattern analysis to better understand the combined effects of insect infestation and forest salvage and harvesting.     

Mapping of the spatial distribution of carbon storage of the Pinus kesiya Royle ex Gordon (Benguet pine) forest in Sagada,Mt. Province,Philippines

The objective of this study is to map the spatial distribution of the aboveground biomass (AGB, tC/ha) storage of the Pinus kesiya Royle ex Gordon (Benguet pine) forest of Sagada, Mt. Province, Philippines by integrating Landsat image and the forest cover map. The data was obtained from 66 plots that were established in the different Benguet pine stands in Sagada. The AGB was estimated using the Digital Numbers (DN) and Normalized Difference Vegetation Index (NDVI) values (with filter and with no filter). The estimated aboveground biomass (AGB) density of the Benguet pine was determined to be 249.66 tonnes/ha corresponding to 112.35 tonnesC/ha.     

基于RS和GIS的木兰县植被盖度定量研究

以木兰县1989年和2011年两景Landsat TM遥感影像为主要数据,基于RS和GIS技术,在定量反演归一化植被指数（NDVI）的基础上,获取植被盖度等级图并进行动态分析。研究结果表明：近22年来NDVI在0.2～0.3面积增加的最多,为141.41km^2,在0.4～0.5减少的最多,为340.29 km^2,低盖度植被区域面积增加的最多,高盖度植被区域面积减少的最多。该研究成果对深入了解该区生态环境质量变化具有重要的意义。     

云南松林纵坑切梢小蠹灾害遥感监测的初步研究

以云南省曲靖市沾益、陆良县遭受纵坑切梢小蠹危害的云南松林为样区,应用NOAA/AVHRR、LandsatTM、CBERS-1 CCD和EOS/MODIS 4种卫星遥感数据,对其受纵坑切梢小蠹的重度危害区和健康林分进行了光谱信息的分析。据此,初步提出了两种监测模型,差变率(DR)模型和灾害指数(DI)模型,可用于区分典型的云南松健康林分和遭受纵坑切梢小蠹重度危害的林分。表明若DR值较大,云南松林的健康状况较好;反之DI值越大,林分受到纵坑切梢小蠹危害的程度越重。     

Factors driving growth responses to drought in Mediterranean forests

We lack information regarding the main factors driving growth responses to drought in tree species with different vulnerability against this stressor and considering sites with contrasting climatic conditions. In this paper, we identify the main drivers controlling growth response to a multi-scalar drought index (Standardized Precipitation Index, SPI) in eight tree species (Abies alba, Pinus halepensis, Quercus faginea, Pinus sylvestris, Quercus ilex, Pinus pinea, Pinus nigra, Juniperus thurifera). We sampled forests growing across a pronounced climatic gradient under Mediterranean conditions in north-eastern Spain. To summarize the patterns of growth responses to drought, we used principal component analysis (PCA). To determine the main factors affecting growth responses to drought, correlation and regression analyses were carried out using a set of abiotic (climate, topography, soil type) and biotic (Normalized Difference Vegetation Index, Enhanced Vegetation Index, tree-ring width, diameter at breast height) predictors and the PCs loadings as response variables. The PCA analysis detected two patterns of growth responses to drought corresponding to xeric and mesic sites, respectively. The regression analyses indicated that growth responses to drought in xeric forests were mainly driven by the annual precipitation, while in mesic sites the annual water balance was the most important driver. The management of Mediterranean forests under the forecasted warmer and drier conditions should focus on the main local factors modulating the negative impacts of drought on tree growth in xeric and mesic sites.     

Satellite-based estimation of biomass carbon stocks for northeast China''s forests between 1982 and 1999

Northeast China maintains large areas of primary forest resource and has been experiencing the largest increase in temperature over the past several decades in the country. Therefore, studying its forest biomass carbon (C) stock and the change is important to the sustainable use of forest resources and understanding of the forest C budget in China. In this study, we use forest inventory datasets for three inventory periods of 1984–1988, 1989–1993 and 1994–1998 and NOAA/AVHRR Normalized Difference Vegetation Index (NDVI) data from 1982 to 1999, to estimate forest biomass C stock and its changes in this region over the last two decades. The averaged forest biomass C stock and C density were estimated as 2.10 Pg C (1 Pg = 10¹⁵ g) and 44.65 Mg C ha⁻¹ over the study period. The forest biomass C stock has increased by 7% with an annual rate of 0.0082 Pg C. The largest increase in the C density occurred in two humid mountain areas, Changbai Mountains and northern Xiaoxing’anling Mountains. Climate warming is probably the key driving force for this increase, while anthropogenic activities such as afforestation and deforestation may contribute to variations in the C stocks.     

Evaluating Beech Tree Phenology in a Deciduous Broadleaf Forest in Northern Iran Using Ground Observation and MODIS Images

We selected 15 beech trees (Fagus orientalis) from the study area in a mountainous region from 520 to 1310 m above sea level. Ground observations of the beech tree growth process from January to December 2004 in 7- to 15-day intervals were performed both visually and by measuring leaf chlorophyll concentration (chlorophyll meter SPAD-502). Results for the regression analysis showed that the leaved period positively correlated with the air temperature (r?=?.894). Anthesis and SPAD measurements have a negative relationship with air temperature and as temperature increases they appear earlier in the year. Anthesis and SPAD measurements had correlation coefficients of r?=??.883 and r?=??.855, respectively. The anthesis and bud bursting and leaf tip appearance have a negative relationship with increasing altitude from sea level and as altitude increases they appear later in the year. To make phenological events in deciduous broadleaf forests recognizable, we used a seasonal 8-day composite Moderate Resolution Imaging Spectroradiometer/Normalized Difference Vegetation Index (MODIS/NDVI). Prior to leaf expansion, NDVI increased from the 109th day of the year or earlier. Existence of the evergreen plants (Ruscus hyrcanus) on the ground and snow-melting exert an influence on inconsistency of phenology monitoring of MODIS/NDVI images. Regression analysis showed that there is a positive relationship between NDVI and SPAD measurements (chlorophyll content) in beech trees.     

Forest Type,Diversity and Biomass Estimation in Tropical Forests of Western Ghat of Maharashtra Using Geospatial Techniques

Tropical forests are the world’s largest terrestrial storehouses of carbon and are recognized as rich, diverse and highly productive ecosystems. The present study was conducted to characterize the land use, diversity and biomass of tropical forest in Western Ghat of Maharashtra State in India through satellite remote sensing and GIS. The study has been designed and implemented to promote analysis on Western Ghat biodiversity resources including trees, shrubs and herbs based on inventorying, monitoring and mapping. Field measured biomass is integrated with spectral responses of various bands and indices of the Landsat TM satellite image for estimation of above-ground biomass in a 36,046 km² area of relic forest in the Central Western Ghat. The above-ground biomass from field-based inventory varied from 30.2 to 151.1 ton/ha in moist deciduous forest, 9.2–99.1 ton/ha in dry deciduous forest, 42.1–158.6 ton/ha in semi-evergreen forest, and 160.9–271 ton/ha in evergreen forest. The total above-ground biomass of the study area was estimated to be 95.2 M tons. A regression equation between field above-ground biomass and a Normalized Difference Vegetation Index was used for spectral modeling to estimate and prepare the above-ground biomass map in the region. A total 120 plant species in 81 genera and 31 families were identified in the study area. This study emphasizes the importance of relic forests for their biodiversity, carbon sequestration and total biomass.     

GIS-based forest fire susceptibility mapping in Iran: a comparison between evidential belief function and binary logistic regression models

The aim of this research was to produce forest fire susceptibility maps (FFSM) based on evidential belief function (EBF) and binary logistic regression (BLR) models in the Minudasht Forests, Golestan Province, Iran. At first, 151 forest fire locations were identified from Moderate-Resolution Imaging Spectero Radiometer data, extensive field surveys, and some reports (collected in year 2010). Out of these locations, 106 (70%) were randomly selected as training data and the remaining 45 (30%) cases were used for the validation goals. In the next step, 15 effective factors such as slope degree, slope aspect, elevation, plan curvature, Topographic Position Index, Topographic Wetness Index, land use, Normalized Difference Vegetation Index, distance to villages, distance to roads, distance to rivers, wind effect, soil texture, annual temperature, and rainfall were extracted from the spatial database. Subsequently, FFSM were prepared using EBF and BLR models, and the results were plotted in ArcGIS. Finally, the receiver operating characteristic curves and area under the curves (AUCs) were constructed for verification purposes. The validation of results showed that the AUC for EBF and BLR models are 0.8193 (81.93%) and 0.7430 (74.30%), respectively. In general, the mentioned results can be applied for land use planning, management and prevention of future fire hazards.