Evaluating vegetation recovery following large-scale forest fires in Borneo and northeastern China using multi-temporal NOAA/AVHRR images

This study evaluated whether a normalized difference vegetation index (NDVI), derived from 8-km-resolution National Oceanic Atmospheric Administration (NOAA) Pathfinder AVHRR (advanced very high resolution radiometer) land (PAL) data, is appropriate for monitoring recovery following large-scale forest fires. Recovery processes were examined after fires on the island of Borneo and in northeastern China in 1983 and 1987, respectively. Based on pre- and post-fire NDVI differences (NDVId), six damage classes were established. Post-fire changes in land cover were monitored using (1) the average NDVI of all pixels corresponding to each damage class (A-NDVI) and (2) the ratio of a fire-affected A-NDVI to a non-fire-affected A-NDVI (QNDVI). Burn areas located by an NDVId threshold value were similar to reported burn areas. Both A-NDVI and QNDVI values signaled vegetation recovery, but the QNDVI gave much better results. For both the 1983 Borneo and 1987 northeastern China fires, QNDVI values dropped at the time of the fire and increased for about 4 years afterwards, although a 4-year period is obviously less than the time required for biomass recovery. Trends at the two study sites diverged after this period, however. The QNDVI values for multiple fire events in Borneo (in 1983, 1987, 1991, 1997, and 1998) showed that recovery times varied with the size of the burn area, but not with the damage class of the same event, whereas the severe-class QNDVI values for the fire in northeastern China in 1987 were still lower than the control value 10 years after the fire.     

Spatial and temporal dynamics of Pai forest vegetation in Pakistan assessed by RS and GIS

Pai, an arid forest in Sindh Province of Pakistan,is important for the environmental, social, economic development and conservation of ecosystems of the province. Considering the significance of the forest for Sindh and the calls from the local population for its deforestation,we quantified the spatial and temporal variation in the vegetation of the forest and land surface temperature(LST)using optical and thermal Landsat satellite data. Our analysis of temporal(1987–2014) images with Arc GIS10.1 revealed that the dense forest area was greatest at725 ha(37 % of the total forest area) during 2013 while it was smallest at 217 ha(11 %) in 1992. The sparse forest area peaked during 1987 at 1115 ha(58 %) under shrubs whereas it was smallest at 840 ha(43 %) in 1992, and the maximum deforestation of Pai forest occurred during 1992.Spatial change in vegetation over a period of about27 years(1987–2014) revealed that vegetation increased on an area of 735 ha(37 %), decreased on 427 ha(22 %),and there was no change on 808 ha(41 %) of the forest.Variation in temperature between shaded(dense forest) and unshaded areas(bare land) of the forest was from 6 to10 °C. While the temperature difference between areas with sparse forest and bare land ranged from 4 to 6 °C. An inverse relationship between LST and NDVI of Pai forest with coefficients of determination of 0.944 and 0.917 was observed when NDVI was plotted against minimum and maximum LST, respectively. The vegetation in the forest increased with time and the areas of more dense Pai forest supported lower surface temperature and thus air temperature.     

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.     

南水北调中线水源区2000—2015年森林动态变化遥感监测

【目的】监测南水北调中线水源区2000—2015年森林空间分布格局,研究森林动态变化过程及其机制,为水源区森林生态系统保护和水质安全提供理论依据。【方法】基于2000、2010和2015年3期30 m分辨率国产环境灾害卫星HJ-1 A/B CCD以及Landsat TM影像数据,采用面向对象决策树分类方法,监测南水北调中线水源区的土地覆被,并运用像元二分模型和归一化植被指数估算植被覆盖度。【结果】3期土地覆被数据精度(K)分别为89. 4%、86. 9%和84. 4%,2000年用户精度为98%,2010年用户精度为96%,2015年用户精度为94%。土地覆被监测结果表明,受退耕还林和封山育林等生态工程项目实施影响,2000—2015年,南水北调中线水源区森林面积持续增加,增幅达9. 5%,增加面积主要来自草地、耕地和建设用地;然而,随着水源区大规模移民和区域经济不断发展,也使得约4 547 km^2的森林转化为耕地、草地和建设用地。分市统计分析表明,十堰市和安康市森林面积增量最大,均超过1 300 km^2,生态修复工程效果显著。2000—2015年,水源区植被覆盖度也呈增加趋势,其中森林植被覆盖度增幅达25. 4%,且植被覆盖度在0. 6以上的森林面积比例3期均超过70%。【结论】近15年来,南水北调中线水源区森林面积和森林植被覆盖度增加显著,森林密度得到提升,高密度的森林植被能够在地表形成植被保护层,降低水源区发生水力侵蚀和沟渠侵蚀的风险,从而提高水源区水土保持能力。     

唐山市植被分布及其变化分析

在全球变化和城市化日益发展的背景下,区域植被受到剧烈影响,但植被又具有多种生态服务功能,这使其成为生态学的研究热点。植被指数是指卫星探测数据的线性或非线性组合,是反映植被的存在、数量、质量、状态及时空分布特点的指数。目     

Using multitemporal satellite imagery to characterize forest wildlife habitat: The case of ruffed grouse

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.     

沙化土地信息提取研究

采用LandsatETM 多时相影像,在地类光谱特征分析的基础上,用分层分离的方法,提取出沙化土地信息。以全国沙漠化普查地类划分标准中的植被覆盖度为确定沙化土地程度的依据,通过建立NDVI和实测植被覆盖度的回归模型,确定不同程度沙化土地对应的植被覆盖度与NDVI的对应关系,用NDVI阈值进行划分,得到沙化土地程度图。     

Forest monitoring with TerraSAR-X: first results

Several TerraSAR-X satellite images acquired in high resolution spotlight mode with different polarisations for two study sites in southern Germany were used to distinguish forest from other land cover classes (agriculture, built-up, water bodies) using logistic regression models. In general, we observed that the mean and particularly the standard deviation of the backscatter were viable measures to discriminate land cover classes. Both measures were lowest for water bodies and highest for built-up areas, with agricultural areas and forest in intermediate positions. Trees outside forest were not differentiable from forest with the applied method. The HH-polarised image was better suited for a classification of built-up areas, whereas the VV-polarised image was more appropriate when classifying agricultural areas. Consequently, the combination of the two differently polarised images leads to a significantly better model. Since forests in one study area were generally found on steeper slopes in comparison to other land cover classes, the inclusion of terrain slope further improved the classification, which resulted in an overall accuracy of 92–95%. Systematic differences in the parameter values of the explanatory variables for one class between the study areas may be caused by differing class probabilities. Thus, transferring the model of one study area to the image of another resulted in a 7–9% loss of accuracy.     

基于3S技术的天保区植被变化监测方法研究

以甘肃省清水县天然林保护区植被时空变化为研究对象,以2期Landsat TM遥感影像为主要信息源,以植被面积和植被覆盖度为监测指标,采用最大似然方法进行监督分类获得土地利用分类图和植被面积,采用NDVI像元二分法获得植被覆盖度图。利用空间分析和统计分析方法将土地利用分类图和植被覆盖度图进行空间叠加分析,揭示研究区1996—2004年间土地利用和植被覆盖度的数量变化和空间变化特征,并对天保区与非天保区以及不同天保区内部的差异进行对比分析。研究结果表明：1996年—2004年间甘肃省清水县天然林保护区森林面积和植被覆盖度得到了明显改善,且天保区优于非天保区; 不同类型天保区植被变化状况有差异,3种类型天然林保护区中以重点生态保护区植被改善状况最为显著。     

基于MODIS-NDVI的中老缅交界区近16年植被覆盖时空变化特征

【目的】探讨中国、老挝、缅甸三国交界区2000—2015年植被覆盖的时空分异和演化趋势,为区内植被的科学管理和有效保护提供参考。【方法】基于中老缅交界区近16年MODIS-NDVI时序数据,借助3S技术以及均值、趋势、变异系数、Hurst指数等统计学相关方法,从多层次多角度探究区内植被覆盖的时空格局、演化规律、空间变异、可持续性及未来演化趋势等特征。【结果】NDVI年均最大值和最小值分别出现在2013(0.7794)和2002年(0.7259),整体呈增加趋势,增速表现为每10年增加1.05%;NDVI月均值以3和9月为折点,呈“S”形变化特征,其值为0.6986~0.8316,并呈上升趋势,月均增长率为0.50%;区内植被覆盖率较高,16年NDVI均值大于0.6的高植被覆盖区占比97.45%,多集中于热带雨林连片分布的山区,低于0.6的区域仅占2.55%,以各国境内主要城市中心及其外围、山区大面积裸地和澜沧江-湄公河流域沿线等区域为主;区内NDVI随海拔增加表现出持续降低趋势,高值区(NDVI≥0.6)多集中于1500m以下的中低海拔地区;2000—2015年,NDVI时间序列呈改善、退化和不变等变化趋势的区域分别占54.06%、15.62%和30.32%,其变异系数为0.0244~0.4688,空间分布上表现为较低波动变化区域>低波动变化区域>中波动变化区域>较高波动变化区域>高波动变化区域,低波动和较低波动变化区域占比合计78.49%;NDVI时间序列呈退化区域与呈高波动变化区域具有较明显的空间一致性,多集中于城镇、交通要道等建设用地以及山区大面积裸地等地区;未来,区内植被覆盖将延续过去16年变化趋势的区域占57.15%,与过去16年变化趋势相反的区域占41.09%;结合空间变化趋势特征,发现研究区未来将有39.63%、29.83%和28.98%的区域植被覆盖分别向良性、不变和恶性方向发展,1.56%的区域发展趋势不确定。【结论】中老缅交界区植被覆盖整体较好,16年来,区内植被覆盖随时间(年际、月际)变化幅度较小,并在此基础上整体呈现增加趋势,植被发展前景良好;然而,以建设用地和裸地为主的部分区域,其植被覆盖未来将出现退化趋势。区内各国应合理规划经济发展,节约、集约利用土地资源,并因地制宜地开展植树造林、退耕还林还草等生态修复工作,以促进区域生态环境的良性发展。     

Monitoring the effects of land use and cover changes on net primary production: A case study in China's Yongding River basin

Changing land uses and cover types influence vegetation composition and health, so understanding the effects of these changes on net primary production (NPP) provides an important tool for monitoring ecosystem responses to environmental change. Using remote-sensing images and precipitation, temperature, and total solar radiation data from 1978, 1987, 2000, and 2005, and a light utilization efficiency model, we studied the effects of changes in these parameters and land use and cover types on NPP in China's Yongding River basin. We determined the NDVI of vegetation in the basin, and used these results to estimate the NPP of vegetation in the basin and the influence of land use and cover type changes on NPP under two climate scenarios: one in which the precipitation and temperature of the previous period remain unchanged into the following period, that is, use the climate in 1978, 1987 and 2000 to analyze NPP data in 1987, 2000 and 2005 respectively, and another in which both parameters remain constant at their 1978 values throughout the study period. With the climate unchanged from the former period, NPP in 1987 decreased compared with the 1978 value by 20–50 gC/m², and then increased by more than 40 gC/m² in western and central parts of the basin from 1987 to 2000. From 2000 to 2005, NPP decreased in the northwestern, northern, and eastern parts of the basin. With climate unchanged from 1978 to 2005, NPP increased from 1987 to 2000 by from 10 to 30 gC/m² in most areas. From 2000 to 2005, some farmland in western and northwestern parts of the basin and some forest land were converted into grassland, decreasing NPP by 40–50 gC/m².     

黄河三角洲河道演变与景观格局分析

以黄河三角洲地区东营市部分为研究区域,研究了黄河三角洲河道演变规律与景观格局分布规律,并着重研究了该地区河道演变与景观格局分布的联系。结果表明:近20年间黄河总取水量呈上升趋势;2002~2010年裸地沼泽面积明显减少,植被面积增多。2010~2019年,建设用地面积明显增加;土地利用景观格局的形式从沿海区域开始向内陆演变呈现一定的规律,最外环为海洋、向内依次为是浅海滩涂、滩涂、水产湿地、盐田、旱地、沼泽湿地、植被、建设用地;受季节影响较大的图可以利用ENVI提取其对应的植被覆盖图来消除影响。在NDVI植被归一化指数的基础之上建立植被覆盖图,通过NDVI指数分析植被的长势和营养状况。     

Spatial pattern analysis for quantification of landscape structure of Tadoba-Andhari Tiger Reserve,Central India

Landscape structure is often regarded as an important factor that governs the distribution and abundance of species.Therefore it is critical to understand the landscapes and their dynamics.Patterns of landscape elements strongly influence the ecological characteristics.This study was designed to document and map the current status of the tropical dry deciduous forest of the Tadoba-Andhari Tiger Reserve(TATR),Central India,(using IRS P6 LISS IV data)and to describe its landscape structure at three levels of organization viz.landscape,class,and patch.The study area was classified into 10 land cover classes that include 6vegetation classes.The landscape structure was analyzed using FRAGSTATS using 12 set of indices.The TATR landscapes have a total of2,307 patches with a mean patch size of 25.67 ha and patch density of 1.7patches per km2.Amongst all land cover classes,mixed bamboo forest is dominant—it occupied maximum area(77.99%)—while riparian forest is least represented(0.32%).Mixed forest has maximum number of patches among all vegetation classes.Results have shown that despite being dominant in the area,mixed bamboo forest has low patch density(0.25/100 ha).Dominance of mixed bamboo forest is attributed to large patch sizes and not to the number of patches.This study has focussed on the approach of integrating satellite forest classification and forest inventory data for studying forest landscape patterns.     

基于遥感影像的大区域植被类型样本快速提取方法研究

[目的] 利用遥感影像的宏观性,基于植被分类资料数据,依据实验区域遥感影像及衍生影像本身特点,实现大区域样本快速提取。[方法] 实验参考1：100万植被图、WESTDC中国土地覆盖图,结合实验区域2001年MODIS时序NDVI影像的非监督分类结果,利用矢、栅数据的空间特征,将实验影像非监督分类的类型信息关联为随机样点属性,依据该属性中包含的非监督分类类型数和各类型的样点比例,对比类别间样本可分离性指标、标准差变化,实现样本纯化。[结果] 纯化后的植被样本与WESTDC中植被空间分布基本一致,主要植被类型空间分布精确程度为84.82%。将纯化前后的样本输入最大似然分类器,总体分类精度提高了32.52%。[结论] 该采样方法适用于宏观大区域植被样本数据的快速提取。同时,节省了大区域植被类型调查消耗的人力物力资源和时间,提高了采样效率。     

Forest cover and pattern changes in the Carpathians over the last decades

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.

基于全极化SAR影像土地覆盖分类研究

基于云南石林地区2009年4月23日的ALOS PALSAR的L波段全极化散射矩阵单视复数据,利用欧空局PolSARPro软件强大的极化处理功能,首先对数据进行极化分解,然后利用H/α/A—Wishart分类方法对该地区进行分类研究,利用2007年的森林资源二类调查数据作为真实样本数据进行精度检验。结果表明,全极化SAR数据可有效识别出建筑用地、林地、耕地、裸地和水域5种地物覆盖类型,总体分类精度达80.87%。     

Remote sensing analysis of forest damage by selection logging in the Kabaung Reserved Forest, Bago Mountains, Myanmar

Selection logging is a principal management scheme in natural teak-bearing forests in Myanmar. Monitoring the spatial extent and intensity of selection logging is important for sustainable forest management. This study applied the normalized difference vegetation index (NDVI) image differencing method using two SPOT-5 pan-sharpened images (2.5 m spatial resolution) taken in October 2007 and January 2009 to analyze canopy changes associated with damage from forest harvesting. According to the pixel-based analysis, NDVI changes were larger in most logging road/log landing points whereas smaller NDVI changes were seen in most unlogged points. NDVI changes in teak stump areas were related to distance from a logging road and the number of stumps within the estimated crown area (a circular area with a 10 m radius from the center of each stump). A Fisher’s exact test showed that one of the main factors causing the high NDVI change in teak stump areas was the effect of road construction. The distribution pattern of teak stumps indicated that teak stumps with estimated crown areas that contained more than one stump had high mean change in NDVI. The spectral difference between before and after logging revealed that logging roads had a greater effect on canopy changes than teak stumps.     

皇甫川流域2000—2015年植被NDVI时空变化特征

【目的】分析黄土高原砒砂岩区植被覆盖的时空动态变化特征,探讨土地利用类型对归一化植被指数(NDVI)的影响及植被盖度与水热因子的空间和时滞关系,为黄土高原砒砂岩区退化植被恢复重建和黄河粗泥沙的综合治理提供思路,改善当地生存条件和生态环境。【方法】在皇甫川流域,利用2000—2015年MODIS13Q1共192期产品,以ENVI为技术平台,计算NDVI,采用一元线性回归分析法和Spearman相关分析法分析其在不同时间尺度上的动态变化和不同空间尺度上的分布格局,基于地理空间系统分析其在不同土地利用方式下(分辨率30m)的时空特性,并运用Spearman相关分析法详细分析NDVI和水热因子间的响应关系。【结果】2000—2015年,皇甫川流域NDVI平均值为0.429~0.630,呈线性增加趋势,增长速率为0.0042·a^-1,生长季与非生长季差别显著,5月出现最大值,气温和降水量亦呈增加趋势,增长速率为0.0348℃·a^-1和6.27mm·a^-1;空间上,纳林川子流域16年来NDVI为0.1~0.3,十里长川子流域为0.25~0.4,均呈“沟道高-坡面低”的空间格局;不同土地利用类型下NDVI为0.2~0.25,表现为林地>耕地>草地>未利用地,16年的增长速率表现为草地>未利用地>林地>耕地;纳林川支流沟道附近NDVI呈下降趋势,变化率为-0.09~0,而远离沟道的坡面地区呈上升趋势,变化率为0~0.12;十里长川支流沟道附近NDVI变化率为0~0.4,而沟道周围地区变化率为0.04~0.12;整个流域内NDVI的增长趋势在空间上呈坡面极显著、沟道附近显著、沟道不显著的空间格局;16年间生长季NDVI与气温无明显相关性,与降水的相关系数为-0.41~0.87,相比沟道附近植被而言,坡面上的植被更易受降水量影响;植被生长对降水的响应存在1年(P=0.036<0.05)和1个月(P=0.001<0.01)的滞后期,对气温存在2个月(P=0.002<0.01)的滞后期。【结论】时间上,皇甫川流域NDVI在不断波动中呈显著上升趋势,生长季与非生长季均值变化表现出明显不同。空间上,皇甫川流域NDVI在沟道均值高,但增长率低,甚至出现退化现象,在坡面均值低,但呈极显著增长。林地和草地增加最多,说明近年来退耕还林工程效果显著。NDVI与降水相关性较高并与气温和降水都存在一定滞后现象。     

Using Landsat images to detect oak decline in the Mark Twain National Forest, Ozark Highlands

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.     

Inclusion of forest harvest legacies, forest type, and regeneration spatial patterns in updated forest maps: A comparison of mapping results

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.