Spatiotemporal heterogeneity of Moso bamboo aboveground carbon storage with Landsat Thematic Mapper images: a case study from Anji County,China

Bamboo forest, especially Moso bamboo forest (Phyllostachys heterocycla var. pubescens), has a powerful carbon sequestration capability and plays an important role in the global carbon sink. This research examines the spatiotemporal heterogeneity of Moso bamboo aboveground carbon storage (AGC) in Anji County of Zhejiang Province, China. Five dates of Landsat Thematic Mapper images and field measurements were used to estimate Moso bamboo AGC between 1986 and 2008. Geostatistics were used to analyse AGC spatiotemporal heterogeneity. The results indicate that Moso bamboo AGC increases gradually and its spatial heterogeneity over five dates has moderate autocorrelation. Spatial heterogeneity increased dramatically after 1986, implying the influence of random factors such as management on the spatiotemporal heterogeneity of AGC. This research provides an alternative insight into the carbon cycling process of a bamboo forest ecosystem, and has implications for ongoing efforts in regard to carbon sequestration management.     

A temporal analysis of urban forest carbon storage using remote sensing

Quantifying the carbon storage, distribution, and change of urban trees is vital to understanding the role of vegetation in the urban environment. At present, this is mostly achieved through ground study. This paper presents a method based on the satellite image time series, which can save time and money and greatly speed the process of urban forest carbon storage mapping, and possibly of regional forest mapping. Satellite imagery collected in different decades was used to develop a regression equation to predict the urban forest carbon storage from the Normalized Difference Vegetation Index (NDVI) computed from a time sequence (1985-1999) of Landsat image data. This regression was developed from the 1999 field-based model estimates of carbon storage in Syracuse, NY. The total carbon storage estimates based on the NDVI data agree closely with the field-based model estimates. Changes in total carbon storage by trees in Syracuse were estimated using the image data from 1985, 1992, and 1999. Radiometric correction was accomplished by normalizing the imagery to the 1999 image data. After the radiometric image correction, the carbon storage by urban trees in Syracuse was estimated to be 146,800 tons, 149,430 tons, and 148,660 tons of carbon for 1985, 1992, and 1999, respectively. The results demonstrate the rapid and cost-effective capability of remote sensing-based quantitative change detection in monitoring the carbon storage change and the impact of urban forest management over wide areas.     

Estimation of tropical forest biomass using Landsat TM imagery and permanent plot data in Xishuangbanna,China

Because of its complexity, it is very difficult to obtain information about distribution of biomass in tropical forests. This article describes the estimation of tropical forest biomass by using Landsat TM and forest plot data in Xishuangbanna, PR China. The method includes several steps. First, the biomass for each forest permanent plot is calculated by using field inventory data. Second, Landsat TM images are geometrically corrected by using topographic maps. Third, a map of the tropical forest is obtained by using data from a variety of sources such as Landsat TM, digital elevation model (DEM), temperature and precipitation layers and expert knowledge. Finally, the biomass and carbon storage of each forest vegetation type in the forest map is calculated by using the tropical forest map and the forest plot biomass GIS database. In the study area, forest area accounts for 57% of the total 1.7?×?10⁶ hectares. The total forest biomass is 2.0?×?10⁸ tonne. It is shown that the forest vegetation map, the forest biomass and the forest carbon storage can be obtained by effectively integrating Landsat TM, ancillary data including DEM, temperature and precipitation, forest permanent plots and knowledge using the method proposed here.     

Moso bamboo forest extraction and aboveground carbon storage estimation based on multi-source remotely sensed images

Using a combination of moso bamboo forest thematic maps derived from Landsat Thematic Mapper (TM) images, field inventory data, and Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) images, moso bamboo forest was extracted using the matched filtering (MF) technique and its aboveground carbon storage (AGC) was then estimated. This study presents a feasible method for extracting large-scale moso bamboo forests and for estimating moso bamboo forest AGC based on low-spatial resolution MODIS images. The results showed that moso bamboo forests in the majority of counties can be accurately estimated between actual area and estimates, with an R ² of 0.8453. The fitted accuracy of the AGC model was high (R ² = 0.491). The prediction accuracy of the AGC model was also evaluated using validation samples collected from Lin'an City, with an R ² and root mean square error prediction of 0.4778 and 3.06 Mg C ha^?1, respectively. The AGC in the majority of counties or cities in Zhejiang Province was between 0 and 15 Mg C ha^?1, and to a certain extent the predicted AGC estimates were close to observed ground truth data and representative of the study area.     

基于地物光谱季节曲线特征的毛竹林分布提取

毛竹是我国南方广泛分布的重要竹种,具有良好的生态效益和经济价值。毛竹林与其他森林区分难度大,现有提取方法多直接采用已有的晴空观测,未充分考虑分类时相的影响,限制了提取精度。以浙江省庆元县为例,从地物光谱的季节曲线特征入手,利用MODIS高时间分辨率观测充分挖掘各植被类型光谱季节曲线特征和差异,结合多时相Landsat OLI影像进行分类实验,优选毛竹林与其他植被区分度最大的季相,并采用随机森林方法实现了毛竹林分布的有效提取。结果表明：①初、中秋是区分研究区毛竹林与其他植被的最优时相,夏季次之,春季与冬季较差;②当初、中秋无晴空影像时,结合夏冬季影像的毛竹林提取精度最佳,用户和制图精度分别达到85.57%和78.06%;③10月影像提取毛竹林分布精度最高,用户和制图精度分别达到89.00%和86.91%,与当地森林资源调查数据相比精度优于89.23%。实验表明：在类似亚热带地区毛竹林提取中,应优先选择秋季初、中期影像;若此时期无晴空观测,应优先采用夏季与冬季影像共同分类。     

Regional-scale boreal forest cover and change mapping using Landsat data composites for European Russia

Boreal forests are a critical component of the global carbon cycle, and timely monitoring allows for assessing forest cover change and its impacts on carbon dynamics. Earth observation data sets are an important source of information that allow for systematic monitoring of the entire biome. Landsat imagery, provided free of charge by the USGS Center for Earth Resources Observation and Science (EROS) enable consistent and timely forest cover updates. However, irregular image acquisition within parts of the boreal biome coupled with an absence of atmospherically corrected data hamper regional-scale monitoring efforts using Landsat imagery. A method of boreal forest cover and change mapping using Landsat imagery has been developed and tested within European Russia between circa year 2000 and 2005. The approach employs a multi-year compositing methodology adapted for incomplete annual data availability, within-region variation in growing season length and frequent cloud cover. Relative radiometric normalization and cloud/shadow data screening algorithms were employed to create seamless image composites with remaining cloud/shadow contamination of less than 0.5% of the total composite area. Supervised classification tree algorithms were applied to the time-sequential image composites to characterize forest cover and gross forest loss over the study period. Forest cover results when compared to independently-derived samples of Landsat data have high agreement (overall accuracy of 89%, Kappa of 0.78), and conform with official forest cover statistics of the Russian government. Gross forest cover loss regional-scale mapping results are comparable with individual Landsat image pair change detection (overall accuracy of 98%, Kappa of 0.71). The gross forest cover loss within European Russia 2000-2005 is estimated to be 2210 thousand hectares, and constitutes a 1.5% reduction of year 2000 forest cover. At the regional scale, the highest proportional forest cover loss is estimated for the most populated regions (Leningradskaya and Moskovskaya Oblast). Our results highlight the forest cover depletion around large industrial cities as the hotspot of forest cover change in European Russia.     

An assessment of the MODIS collection 5 leaf area index product for a region of mixed coniferous forest

Canopy leaf area index (LAI), defined as the single-sided leaf area per unit ground area, is a quantitative measure of canopy foliar area. LAI is a controlling biophysical property of vegetation function, and quantifying LAI is thus vital for understanding energy, carbon and water fluxes between the land surface and the atmosphere. LAI is routinely available from Earth Observation (EO) instruments such as MODIS. However EO-derived estimates of LAI require validation before they are utilised by the ecosystem modelling community. Previous validation work on the MODIS collection 4 (c4) product suggested considerable error especially in forested biomes, and as a result significant modification of the MODIS LAI algorithm has been made for the most recent collection 5 (c5). As a result of these changes the current MODIS LAI product has not been widely validated. We present a validation of the MODIS c5 LAI product over a 121 km² area of mixed coniferous forest in Oregon, USA, based on detailed ground measurements which we have upscaled using high resolution EO data. Our analysis suggests that c5 shows a much more realistic temporal LAI dynamic over c4 values for the site we examined. We find improved spatial consistency between the MODIS c5 LAI product and upscaled in situ measurements. However results also suggest that the c5 LAI product underestimates the upper range of upscaled in situ LAI measurements.     

Evaluating the potential to monitor aboveground biomass in forest and oil palm in Sabah,Malaysia, for 2000–2008 with Landsat ETM+ and ALOS-PALSAR

We explored the potential of Landsat Enhanced Thematic Mapper (ETM+) imagery to quantify the expansion of planted oil palm area and changes in aboveground biomass (AGB) in plantation and forest in Sabah, Malaysian Borneo, from 2000 to 2008. For comparison, a classification layer derived from an Advanced Land-Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS-PALSAR) Fine Beam Dual (FBD)-polarized mosaic from 2008 was used for change detection analysis. Field-measured AGB values from 85 ha of forest and oil palm plantation plots were compared with 12 vegetation indices (VIs) and four spectral mixture analysis (SMA) derivatives. Correlations against indices using optical data were higher for oil palm biomass than for forest biomass. Change detection analysis of forest conversion to oil palm plantation was performed for areas designated as protected areas, commercial forest reserve and areas with no forest-use designation. This analysis found an increase in oil palm area of 38% (1450 km²) and a total decrease in forest area of 13.1% (1900 km²) for the whole study area from 2000 to 2008. The greatest area of forest loss was in areas not designated as forest reserve by the Sabah Forestry Department, although some oil palm expansion was detected in both commercial and protected areas. Using derived equations for biomass, we estimated that 46.6 Tg of carbon dioxide equivalents (CO₂e) were released in these three forest designations or 53.4 Tg CO₂e for the entire study area due to forest conversion to oil palm. These results are presented as relevant for on-going efforts to remotely monitor the carbon emission implications of forest loss as part of the United Nations Framework Convention on Climate Change's (UNFCCC's) proposed mechanism, Reduced Emissions from Deforestation and Degradation (REDD).     

Classifying successional forests using Landsat spectral properties and ecological characteristics in eastern Amazônia

Secondary forests may become increasingly important as temporary reservoirs of genetic diversity, stocks of carbon and nutrients, and moderators of hydrologic cycles in the Amazon Basin as agricultural lands are abandoned and often later cleared again for agriculture. We studied a municipality in northeastern Pará, Brazil, that has been settled for over a century and where numerous cycles of slash and burn agriculture have occurred. The forests were grouped into young (3-6 years), intermediate (10-20 years), advanced (40-70 years), and mature successional stages using 1999 Landsat 7 ETM imagery. Supervised classification of the imagery showed that these forest classes occupied 22%, 13%, 9%, and 6% of the area, respectively. Although this area underwent widespread deforestation many decades ago, forest of some type covers about 50% of the area. Row crops, tree crops, and pastures cover 8%, 20%, and 22%, respectively. The best separation among land covers appeared in a plot of NDVI versus band 5 reflectance. The same groupings of successional forests were derived independently from indices of similarity among tree species composition. Measured distributions of tree height and diameter also covaried with these successional classes, with the young forests having nearly uniform distributions, whereas multiple height and diameter classes were present in the advanced successional forests. Biomass accumulated more slowly in this secondary forest chronosequence than has been reported for other areas, which explains why the 70-year-old forests here were still distinguishable from mature forests using spectral properties. Rates of forest regrowth may vary across regions due to differences in edaphic, climatic, and historical land-use factors, thus rendering most relationships among spectral properties and forest age site-specific. Successional status, as characterized by species composition, biomass, and distributions of heights and diameters, may be superior to stand age as a means of stratifying these forests for characterization of spectral properties.     

Urban Expansion and Driving Force Analysis of Ulaanbaatar in Mongolia in Recent Thirty Years

Since the reform of the regime in the 1990s，Mongolian urban experienced a rapid development.Understanding the characteristics of urbanization and development in Mongolia is much of significance to China’s implementation of the “Belt and Road” strategy and “China-Mongolia-Russia Economic Corridor”.This study was based on theLandsat TM/OLI remote sensing image，using object-oriented classification method，and obtained 1990，2001，2010，2017 land cover data set，the overall classification accuracy were 86%，89%，91.6%，94.80% respectively，Kappa coefficient were 0.83，0.869，0，898，0，935.based on the transfer matrix，the information of land cover change from 1990 to 2017 in Ulaanbaatar was mined，the results showed that：① the areas of built area，barren，and water showed an increasing trend，and the built area was increased most.On the contrary，the area of forest，cropland，and grassland showed a tendency to decrease，and the forest area decreased most.② the transfer between grassland and forest，grassland and built area，forest and grassland played a major role in land cover change of Ulaanbaatar.The change area from 1990 to 2001，2001 to 2010 and 2010 to 2017 accounted for nearly 71%，74%，and 79% of the total change area.③ the expansion trend of built area was significant，the area has increased from 99.87 km2to 216.16 km2，the growth rate has reached 216%，the expansion rate was 8.01 km2/a，which belonged to the rapid expansion mode.The middle-north with the type of summer house，northeast with the types of traditional houses based on the structure of home-household-yard，mixture of Mongolian yurts and low buildings，west with the types of industrial land and residential land.The urbanization in Ulaanbaatar caused by the interaction of external social economic development and national policies，among of which，land privatization and market economic were the main policy driving forces of urbanization     

Object-based classification using SPOT-5 imagery for Moso bamboo forest mapping

This study proposed a multi-scale, object-based classification analysis of SPOT-5 imagery to map Moso bamboo forest. A three-level hierarchical network of image objects was developed through multi-scale segmentation. By combining spectral and textural properties, both the classification tree and nearest neighbour classifiers were used to classify the image objects at Level 2 in the three-level object hierarchy. The feature selection results showed that most of the object features were related to the spectral properties for both the classification tree and nearest neighbour classifiers. Contextual information characterized by the composition of classified image objects using the class-related features assisted the detection of shadow areas at Levels 1 and 3. Better classification results were achieved using the nearest neighbour algorithm, with both the producer’s and user’s accuracy higher than 90% for Moso bamboo and an overall accuracy of over 85%. The object-based approach toward incorporating textural and contextual information in classification sequence at various scales shows promise in the analysis of forest ecosystems of a complex nature.     

Characterizing 40 years of natural Pinus sylvestris var. mongolica carbon stocks in Northeast China using stand age from remote sensing time series

ABSTRACT

Remote sensing (RS)-assisted estimates of forest carbon stocks are essential for modelling carbon budgets at large scales. Research on the dynamic variations in forest biomass and carbon stocks is important to improve the sustainable use of forest resources and understand forest carbon budgets in China. The Honghuaerji region of the Inner Mongolia Autonomous Region is where Pinus sylvestris var. mongolica Litv. (MP) originated. MP forests contained the most important coniferous silvicultural tree species and were later introduced to the desert regions of northern China. However, there has been very little research on carbon stocks (vegetation and soil carbon) over time in the original MP forests. Our goal was to estimate the changes in the carbon stocks of the natural MP forests in the Honghuaerji region using a 40-year RS time series, forest survey data and laboratory data. First, we mapped MP forests and estimated forest ages based on Key Hole 9 and Landsat time series stacks from 1975 to 2015, and the estimated forest ages were shown to be consistent with the surveyed tree ages with a coefficient of determination (R²) of 0.96. Second, fifteen vegetation indices were evaluated as indicators of diameter at breast height (DBH). Of these, tasselled cap greenness provided the best relationships, and an RS-based DBH equation was constructed with an R² of 0.65. Third, the carbon densities in the different ages of MP forests were obtained at regional scales. Finally, the dynamics of the carbon stocks in the natural MP forest ecosystem (including stems, branches, leaves, roots and soils) over 40 years were obtained. The results of this study indicated that the total MP forest ecosystem carbon stocks increased significantly from 7.36 ± 1.92 Tg C in 1975 to 11.52 ± 2.51 Tg C in 2015 at a rate of 1.42% year^–1, among which vegetation carbon stocks increased 5.54% year^–1 and soil carbon stocks increased 0.45% year^–1. The results reveal a notable carbon increase in the MP forests in the Honghuaerji region over the past four decades and provide information on the role of natural forest conservation and management in China in mitigating global climate change.     

闽北山区竹林分布及动态变化的地形分异特征研究

地形是影响植被分布和变化的重要因素之一,摸清竹资源的地形分异特征,对制定区域的竹资源可持续发展和生态环境建设战略具有重要意义。以福建省顺昌县为例,在GIS和遥感技术的支持下,结合遥感数据成图与地形因子叠加分析,对竹林分布及动态变化的地形分异特征进行了研究。研究结果表明：①竹林主要分布在海拔1 200 m以下,400~800 m区域竹林面积最大,在海拔800 m以下随着海拔的升高,竹林面积逐渐增大,在海拔800 m以上,随着海拔的升高竹林面积逐渐减少;②竹林集中分布在坡度6~25°区域,并且主要分布在半阳坡和半阴坡;③从1988~2007年间顺昌县竹林面积呈快速增长态势,净增加面积为1 6811.48 hm²;增加、减少和未变3种变化类型中,增加和未变的竹林面积随地形的变化呈先增加后减少的趋势;而减少的竹林面积随着海拔升高逐渐减少,随着坡度的变化先升高后减少,并且在各个坡向上变化不大。研究成果反映了自然与人为驱动力对顺昌县竹林面积的动态变化的影响,为该县竹林利用规划和生态建设提供了科学依据。     

Analysis of different polarimetric target decomposition methods in forest density classification using C band SAR data

The ability of synthetic aperture radar (SAR) C-band microwave energy to penetrate within forest vegetation makes it possible to extract information on crown components, which in turn gives a better approximation of relative canopy density than optical data-derived canopy density. Many studies have been reported to estimate forest biomass from SAR data, but the scope of C-band SAR in characterizing forest canopy density has not been adequately understood with polarimetric techniques. Polarimetric classification is one of the most significant applications of polarimetric SAR in remote sensing. The objective of the present study was to evaluate the feasibility of different polarimetric SAR data decomposition methods in forest canopy density classification using C-band SAR data. Landsat (Land Satellite) 5 TM (Thematic Mapper) data of the same area has been used as optical data to compare the classification result. RADARSAT (Radar Satellite)-2 image with fine quad-pol obtained on 27 October 2011 over tropical dry forests of Madhav National Park, India, was used for the analysis of full polarimetric data. Six decomposition methods were selected based on incoherent decomposition for generating input images for classification, i.e. Huynen, Freeman and Durden, Yamaguchi, Cloude, Van zyl, and H/A/α. The performance of each decomposition output in relation to each land cover unit present in the study area was assessed using a support vector machine (SVM) classifier. Results show that Yamaguchi 4-component decomposition (overall accuracy 87.66% and kappa coefficient (κ) 0.86) gives better classification results, followed by Van Zyl decomposition (overall accuracy 87.20% and κ 0.85) and Freeman and Durden (overall accuracy 86.79% and κ 0.85) in forest canopy density classification. Both model-based decompositions (Freeman and Durden and Yamaguchi4) registered good classification accuracy. In eigenvector or eigenvalue decompositions, Van zyl registered the second highest accuracy among different decompositions. The experimental results obtained with polarimetric C-band SAR data over a tropical dry deciduous forest area imply that SAR data have significant potential for estimating canopy density in operational forestry. A better forest density classification result can be achieved within the forest mask (without other land cover classes). The limitations associated with optical data such as non-availability of cloud-free data and misclassification because of gregarious occurrence of bushy vegetation such as Lantana can be overcome by using C-band SAR data.     

Combining MODIS and Landsat imagery to estimate and map boreal forest cover loss

Estimation of forest cover change is important for boreal forests, one of the most extensive forested biomes, due to its unique role in global timber stock, carbon sequestration and deposition, and high vulnerability to the effects of global climate change. We used time-series data from the MODerate Resolution Imaging Spectroradiometer (MODIS) to produce annual forest cover loss hotspot maps. These maps were used to assign all blocks (18.5 by 18.5 km) partitioning the boreal biome into strata of high, medium and low likelihood of forest cover loss. A stratified random sample of 118 blocks was interpreted for forest cover and forest cover loss using high spatial resolution Landsat imagery from 2000 and 2005. Area of forest cover gross loss from 2000 to 2005 within the boreal biome is estimated to be 1.63% (standard error 0.10%) of the total biome area, and represents a 4.02% reduction in year 2000 forest cover. The proportion of identified forest cover loss relative to regional forest area is much higher in North America than in Eurasia (5.63% to 3.00%). Of the total forest cover loss identified, 58.9% is attributable to wildfires. The MODIS pan-boreal change hotspot estimates reveal significant increases in forest cover loss due to wildfires in 2002 and 2003, with 2003 being the peak year of loss within the 5-year study period. Overall, the precision of the aggregate forest cover loss estimates derived from the Landsat data and the value of the MODIS-derived map displaying the spatial and temporal patterns of forest loss demonstrate the efficacy of this protocol for operational, cost-effective, and timely biome-wide monitoring of gross forest cover loss.     

西南地区2001~2019年森林损失特征遥感监测与时空分析

森林在生态系统服务中起着重要作用,例如提供清洁空气、保护生物栖息地以及减少全球温室气体的排放等。全球森林变化数据集（Global Forest Change,GFC）每年以30 m的高空间分辨率绘制森林覆盖变化图,成为监测森林覆盖时空变化特征的有效工具。利用谷歌地球引擎（Google Earth Engine,GEE）,基于GFC产品,结合线性回归方法和空间自相关理论对西南地区2001~2019年森林变化情况进行研究,结果表明：近19 a来,西南地区森林损失面积为375.27万hm²,以2008年为拐点,2008年之前呈显著增加趋势（p<0.05）,在此之后波动下降,损失主要集中分布在广西、贵州东南和云南南部地区;森林损失与地形分析的关系表明损失主要分布在海拔2 000 m以下、坡度小于40°的区域,并逐渐向海拔更低坡度更缓的地方转移;森林损失面积具有一定的空间关联性,2001~2019年来Moran’s I 指数均为正,平均值为0.406,空间高值聚集在广西和贵州南部,低值聚集在重庆、四川和云南北部;政策因素在土地利用方式转变过程中发挥着重要作用,林业活动和农业扩张是影响损失现象发生的主要驱动因素,今后制定森林保护和管理战略时应充分考虑多方面因素造成的森林损失现象,研究结果可以为森林监测和保护提供更科学的指导。     

Leaf area index estimation of bamboo forest in Fujian province based on IRS P6 LISS 3 imagery

Leaf area index (LAI) is an important surface biophysical parameter as an input to many process-oriented ecosystem models. Much work has been reported in the literature on LAI estimation in boreal forests using remotely sensed imagery. However, few if any explicit LAI retrieval studies on bamboo forests in Asian subtropical monsoon-climate regions based on remote sensing technology have been performed. Our goal is to carry out a comparative study on the LAI estimation methods of bamboo forest in Fujian province, China, based on IRS P6 LISS 3 imagery. Both the traditional empirical–statistical approach and the newly proposed normalized distance (ND) method were employed in this study, and a total of 18 modelling parameters were regressed against ground-based LAI measurements. The results show that simple ratio (SR) is the best predictor for LAI estimation in this study area, with the highest R ² (coefficient of determination) value of 0.68; modified simple ratio (MSR) and normalized difference vegetation index (NDVI) ranked second and third, respectively. The good performance of these three vegetation indices (VIs) can be explained by the ratioing principle. The overall good modelling performance of the ND method in our study area also indicates it is a promising method.     

Combining lidar estimates of aboveground biomass and Landsat estimates of stand age for spatially extensive validation of modeled forest productivity

Extensive estimates of forest productivity are required to understand the relationships between shifting land use, changing climate and carbon storage and fluxes. Aboveground net primary production of wood (NPP_Aw) is a major component of total NPP and of net ecosystem production (NEP). Remote sensing of NPP and NPP_Aw is generally based on light use efficiency or process-based biogeochemistry models. However, validating these large area flux estimates remains a major challenge. In this study we develop an independent approach to estimating NPP_Aw, based on stand age and biomass, that could be implemented over a large area and used in validation efforts. Stand age is first mapped by iterative unsupervised classification of a multi-temporal sequence of images from a passive optical sensor (e.g. Landsat TM). Stand age is then cross-tabulated with estimates of stand height and aboveground biomass from lidar remote sensing. NPP_Aw is then calculated as the average increment in lidar-estimated biomass over the time period determined using change detection. In western Oregon, productivity estimates made using this method compared well with forest inventory estimates and were significantly different than estimates from a spatially distributed biogeochemistry model. The generality of the relationship between lidar-based canopy characteristics and stand biomass means that this approach could potentially be widely applicable to landscapes with stand replacing disturbance regimes, notably in regions where forest inventories are not routinely maintained.     

基于多时相TM影像的冬小麦面积变化监测

利用北京1992年、2000年、2004年、2009年的多时相Landsat TM5影像数据,结合实际调查数据,分析了近20年来北京冬小麦种植面积的变化趋势及演变特征。采用决策树、PCA、缨帽变换等手段对地物进行分类,利用多时相影像,NDVI组合阈值提取小麦种植区面积。研究结果表明:北京地区1992年、2000年、2004年、2009年冬小麦种植面积分别为:113671ha,84322ha,40410ha,61529ha。北京冬小麦种植面积呈现为明显的先减少后增加的趋势。从1992年到2009年共减少52143ha。其中,从1992年到2000年冬小麦种植面积减少了29349ha,减少的面积中城区扩张占用和转变为裸地的最多,分别为39.7%和42.8%,另外有13.3%变为设施用地,3%成为水体(鱼塘和水田);从2000年到2004年冬小麦种植面积共减少43921ha,减少的面积中转变为裸地和城区扩张占用的最多,分别为39.8%和33.1%;从2004年到2009年冬小麦种植面积共增加了21119ha,其中裸地转变为小麦种植区面积最大。     

Characterizing 25 years of change in the area,distribution, and carbon stock of Mediterranean pines in Central Spain

Mediterranean pines are subject to continuous change under the influence of natural and human factors. Remotely sensed data provide a means to characterize these changes over large areas. In this study we used a time series of Landsat imagery to capture 25 years (1984–2009) of change in the pine-dominated forests of the Central Range in Spain. Object-based image analysis methods were used to identify landscape-level changes in the area and the distribution of forests. We also propose that in the absence of disturbance, biomass accrual is occurring (or depletion in cases where removal is evident) and may be related to changes to the carbon stock; we describe the detected spectral changes in terms of biomass changes as the carbon stocking process. The primary inputs for the identification of changes in the area and distribution of pine stands were Landsat bands 3, 4 and 5 and the Tasseled Cap Angle (TCA) – a metric derived from the greenness and brightness components of the Tasseled Cap Transformation (TCT). In the identification of carbon stocking processes the temporal derivative of the TCA, the Process Indicator (PI), was used to inform on the rate and directionality of the change present. Our results show that the total area of pine forest has increased by 40%, from 1211 km² to 1698 km², during this period, with a variable rate of change. The distribution of pine-dominated forest has changed as well: there is an area of 765 km² permanently covered with pines and 945 km² found to be temporarily occupied. Following the logic of carbon stocking processes, our findings show that at the end of the analysis period, 20% of the potential pine area is increasing its carbon stock and 40% of this area is experiencing a decrease.