Regional scale relationships between ecosystem structure and functioning: the case of the Patagonian steppes

Aims 1. To characterize ecosystem functioning by focusing on above‐ground net primary production (ANPP), and 2. to relate the spatial heterogeneity of both functional and structural attributes of vegetation to environmental factors and landscape structure. We discuss the relationship between vegetation structure and functioning found in Patagonia in terms of the capabilities of remote sensing techniques to monitor and assess desertification. Location Western portion of the Patagonian steppes in Argentina (39°30′ S to 45°27′ S). Methods We used remotely‐sensed data from Landsat TM and AVHRR/NOAA sensors to characterize vegetation structure (physiognomic units) and ecosystem functioning (ANPP and its seasonal and interannual variation). We combined the satellite information with floristic relevés and field estimates of ANPP. We built an empirical relationship between the Landsat TM‐derived normalized difference vegetation index (NDVI) and field ANPP. Using stepwise regressions we explored the relationship between ANPP and both environmental variables (precipitation and temperature surrogates) and structural attributes of the landscape (proportion and diversity of different physiognomic classes (PCs)). Results PCs were quite heterogeneous in floristic terms, probably reflecting degradation processes. Regional estimates of ANPP showed differences of one order of magnitude among physiognomic classes. Fifty percent of the spatial variance in ANPP was accounted for by longitude, reflecting the dependency of ANPP on precipitation. The proportion of prairies and semideserts, latitude and, to a lesser extent, the number of PCs within an 8 × 8 km cell accounted for an additional 33% of the ANPP variability. ANPP spatial heterogeneity (calculated from Landsat TM data) within an 8 × 8 km cell was positively associated with the mean AVHRR/NOAA NDVI and with the diversity of physiognomic classes. Main conclusions Our results suggest that the spatial and temporal patterns of ecosystem functioning described from ANPP result not only from water availability and thermal conditions but also from landscape structure (proportion and diversity of different PCs). The structural classification performed using remotely‐sensed data captured the spatial variability in physiognomy. Such capability will allow the use of spectral classifications to monitor desertification.     

Seasonal Variation in Aboveground Production and Radiation-use Efficiency of Temperate rangelands Estimated through Remote Sensing

Aboveground net primary production (ANPP) of grasslands varies spatially and temporally. Spectral information provided by remote sensors is a promising new tool that may be able to estimate ANPP in real time and at low cost. The objectives of this study were (a) to evaluate at a seasonal scale the relationship between ANPP and the normalized difference vegetation index (NDVI), (b) to estimate seasonal variations in the coefficient of conversion of absorbed radiation into aboveground biomass (ε_a), and (c) to identify the environmental controls on such temporal changes. We used biomass-based field determinations of ANPP for two grassland sites in the Flooding Pampa, Argentina, and related them with NDVI data derived from the NOAA Advanced Very High Resolution Radiometer (AVHRR) satellites using three different models. Results were compared with data obtained from the new Moderate Resolution Imaging Spectroradiometer (MODIS) sensor at an additional site. The first model was based solely on NDVI; the second was based on the amount of photosynthetically active radiation absorbed by the green vegetation (APAR_g), which was derived from NDVI and incoming photosynthetically active radiation (PAR); the third was based on APAR_g and ε_a, which was in turn estimated from climatic variables. NDVI explained between 63 and 93% of ANPP variation, depending on the site considered. Estimates of ANPP were not improved by considering the variation in incoming PAR. At both sites, ε_a varied seasonally (from 0.2 to 1.2 g DM/MJ) and was significantly associated with combinations of precipitation and temperature. Combining ε_a variations with APAR_g increased our ability to account for seasonal ANPP variations at both sites. Our results indicate that NDVI produces good, direct estimates of ANPP only if NDVI, PAR, and ε_a are correlated throughout the seasons. Thus, in most cases, seasonal variations of ε_a associated with temperature and precipitation must be taken into account to generate seasonal ANPP estimates with acceptable accuracy.     

Landscape,soil and meteorological influences on canopy dynamics of northern flooding Pampa grasslands,Argentina

Question: How do meteorological variations at seasonal, interannual scales differentially affect the canopy dynamics of four contrasting landscape units within a region? Location: Flooding Pampa, Buenos Aires, Argentina. 5000 km². Central point: 35°15′S, 57°45′W. Methods: We used a 19‐year series of the normalized difference vegetation index (NDVI) derived from NOAA‐AVHRR PAL (Pathfinder AVHRR Land) images and meteorological data provided by a nearby weather station. The NDVI was used as surrogate of canopy photosynthetic status. The relationship between annually integrated NDVI and meteorological conditions was explored by stepwise multiple regressions for each defined unit. PC A was performed to compare units and growing seasons on a multivariate basis. Results: Mean seasonal NDVI curve was similarly shaped among landscapes. However, the absolute values differed widely. There was high interannual variation so that the mean seasonal pattern was seldom observed in any particular year. Annually integrated NDVI of all landscapes was negatively associated with summer temperature and positively with previous year precipitation. It was also directly related with current year winter precipitation in two landscapes and with summer precipitation in the others. NDVI response to September and March precipitation accounted for some of the differences in interannual variation among landscapes. Conclusions: Our results revealed a strong intra‐regional variation of canopy dynamics, closely linked to landscape (vegetation‐soil) and water availability (mainly in summer and during the previous year). These links may be used to predict forage production rates for livestock.     

Spatial variability of above‐ground net primary production in Uruguayan grasslands: a remote sensing approach

Question: How does above‐ground net primary production (ANPP) differ (estimated from remotely sensed data) among vegetation units in sub‐humid temperate grasslands? Location: Centre‐north Uruguay. Methods: A vegetation map of the study area was generated from LANDSAT imagery and the landscape configuration described. The functional heterogeneity of mapping units was analysed in terms of the fraction of photosynthetically active radiation absorbed by green vegetation (fPAR), calculated from the normalized difference vegetation index (NDVI) images provided by the moderate resolution imaging spectroradiometer (MODIS) sensor. Finally, the ANPP of each grassland class was estimated using NDVI and climatic data. Results: Supervised classification presented a good overall accuracy and moderate to good average accuracy for grassland classes. Meso‐xerophytic grasslands occupied 45% of the area, Meso‐hydrophytic grasslands 43% and Lithophytic steppes 6%. The landscape was shaped by a matrix of large, unfragmented patches of Meso‐xerophytic and Meso‐hydrophytic grasslands. The region presented the lowest anthropic fragmentation degree reported for the Rio de la Plata grasslands. All grassland units showed bimodal annual fPAR seasonality, with spring and autumn peaks. Meso‐hydrophytic grasslands showed a radiation interception 10% higher than the other units. On an annual basis, Meso‐hydrophytic grasslands produced 3800 kg dry matter (DM) ha^?1 yr^?1 and Meso‐xerophytic grasslands and Lithophytic steppes around 3400 kg·DM·ha^?1·yr^?1. Meso‐xerophytic grasslands had the largest spatial variation during most of the year. The ANPP temporal variation was higher than the fPAR variability. Conclusions: Our results provide valuable information for grazing management (identifying spatial and temporal variations of ANPP) and grassland conservation (identifying the spatial distribution of vegetation units).     

Spatiotemporal Patterns of Production Can Be Used to Detect State Change Across an Arid Landscape

Methods to detect and quantify shifts in the state of ecosystems are increasingly important as global change drivers push more systems toward thresholds of change. Temporal relationships between precipitation and aboveground net primary production (ANPP) have been studied extensively in arid and semiarid ecosystems, but rarely has spatial variation in these relationships been investigated at a landscape scale, and rarely has such information been viewed as a resource for mapping the distribution of different ecological states. We examined the broad-scale effects of a shift from grassland to shrubland states on spatiotemporal patterns of remotely sensed ANPP proxies in the northern Chihuahuan Desert. We found that the normalized difference vegetation index (NDVI), when averaged across an eight-year period, did not vary significantly between these states, despite changes in ecosystem attributes likely to influence water availability to plants. In contrast, temporal relationships between precipitation and time-integrated NDVI (NDVI-I) modeled on a per-pixel basis were sensitive to spatial variation in shrub canopy cover, a key attribute differentiating ecological states in the region. The slope of the relationship between annual NDVI-I and 2-year cumulative precipitation was negatively related to, and accounted for 71% of variation in, shrub canopy cover estimated at validation sites using high spatial resolution satellite imagery. These results suggest that remote sensing studies of temporal precipitation–NDVI relationships may be useful for deriving shrub canopy cover estimates in the region, as well as for mapping other ecological state changes characterized by shifts in long-term ANPP, plant functional type dominance, or both.     

Upscaling leaf area index in an Arctic landscape through multiscale observations

Monitoring and understanding global change requires a detailed focus on upscaling, the process for extrapolating from the site‐specific scale to the smallest scale resolved in regional or global models or earth observing systems. Leaf area index (LAI) is one of the most sensitive determinants of plant production and can vary by an order of magnitude over short distances. The landscape distribution of LAI is generally determined by remote sensing of surface reflectance (e.g. normalized difference vegetation index, NDVI) but the mismatch in scales between ground and satellite measurements complicates LAI upscaling. Here, we describe a series of measurements to quantify the spatial distribution of LAI in a sub‐Arctic landscape and then describe the upscaling process and its associated errors. Working from a fine‐scale harvest LAI–NDVI relationship, we collected NDVI data over a 500 m × 500 m catchment in the Swedish Arctic, at resolutions from 0.2 to 9.0 m in a nested sampling design. NDVI scaled linearly, so that NDVI at any scale was a simple average of multiple NDVI measurements taken at finer scales. The LAI–NDVI relationship was scale invariant from 1.5 to 9.0 m resolution. Thus, a single exponential LAI–NDVI relationship was valid at all these scales, with similar prediction errors. Vegetation patches were of a scale of ～0.5 m and at measurement scales coarser than this, there was a sharp drop in LAI variance. Landsat NDVI data for the study catchment correlated significantly, but poorly, with ground‐based measurements. A variety of techniques were used to construct LAI maps, including interpolation by inverse distance weighting, ordinary Kriging, External Drift Kriging using Landsat data, and direct estimation from a Landsat NDVI–LAI calibration. All methods produced similar LAI estimates and overall errors. However, Kriging approaches also generated maps of LAI estimation error based on semivariograms. The spatial variability of this Arctic landscape was such that local measurements assimilated by Kriging approaches had a limited spatial influence. Over scales >50 m, interpolation error was of similar magnitude to the error in the Landsat NDVI calibration. The characterisation of LAI spatial error in this study is a key step towards developing spatio‐temporal data assimilation systems for assessing C cycling in terrestrial ecosystems by combining models with field and remotely sensed data.     

1983～1992年中国陆地NDVI变化的气候因子驱动分析

利用1983－1992年NOAA／AVHRR逐月的归一化植被指数（NDVI）资料和中国国家气象局全国160个基本标准气象站的月均温和降雨数据,探讨气温,降水对中国植NDVI动态变化驱动作用。首先计算了NDVI与气温,降水偏相关和复相关系数,研究了中国植被NDVI变化的气候因子驱动的区域分异规律,并据此,对中国植被NDVI变化的气候因子驱动进行了分区,共分出4个一级区,6个二级区和14个三级区,进一步表明了中国植被NDVI变化气候因子驱动的区域差异。     

Sensitivity of mean annual primary production to precipitation

In many terrestrial ecosystems, variation in aboveground net primary production (ANPP) is positively correlated with variation in interannual precipitation. Global climate change will alter both the mean and the variance of annual precipitation, but the relative impact of these changes in precipitation on mean ANPP remains uncertain. At any given site, the slope of the precipitation‐ANPP relationship determines the sensitivity of mean ANPP to changes in mean precipitation, whereas the curvature of the precipitation‐ANPP relationship determines the sensitivity of ANPP to changes in precipitation variability. We used 58 existing long‐term data sets to characterize precipitation‐ANPP relationships in terrestrial ecosystems and to quantify the sensitivity of mean ANPP to the mean and variance of annual precipitation. We found that most study sites have a nonlinear, saturating relationship between precipitation and ANPP, but these nonlinearities were not strong. As a result of these weak nonlinearities, ANPP was nearly 40 times more sensitive to precipitation mean than variance. A 1% increase in mean precipitation caused a ?0.2% to 1.8% change in mean ANPP, with a 0.64% increase on average. Sensitivities to precipitation mean peaked at sites with a mean annual precipitation near 500 mm. Changes in species composition and increased intra‐annual precipitation variability could lead to larger ANPP responses to altered precipitation regimes than predicted by our analysis.     

中国典型植被类型NDVI动态变化与气温、降水变化的敏感性分析

利用 198 3～ 1992年覆盖全国范围的、多时相的、NOAA/AVHRR的 NDVI数字影像 ,结合我国 16 0个基本标准气象站逐月的气温、降水资料 ,对 10年来中国主要植被类型的遥感特征参数 NDVI的动态变化与同期气温、降水变化的关系进行了分析。结果显示 :就全国而言 ,从北到南 ,NDVI的变化与气候条件变化的相关系数逐渐降低 ;从东南到西北 ,NDVI的变化与气候条件变化的相关系数逐渐增加。     

Influence of climate variability on plant production and N‐mineralization in Central US grasslands

Abstract. We assessed the influence of annual and seasonal climate variability over soil organic matter (SOM), above‐ground net primary production (ANPP) and in situ net nitrogen (N) mineralization in a regional field study across the International Geosphere Biosphere Programme (IGBP) North American mid‐latitude transect (Koch et al. 1995). We hypothesized that while trends in SOM are strongly correlated with mean climatic parameters, ANPP and net N‐mineralization are more strongly influenced by annual and seasonal climate because they are dynamic processes sensitive to short‐term variation in temperature and water availability. Seasonal and monthly deviations from long‐term climatic means, particularly precipitation, were greatest at the semi‐arid end of the transect. ANPP is sensitive to this climatic variability, but is also strongly correlated with mean annual climate parameters. In situ net N‐mineralization and nitrification were weakly influenced by soil water content and temperature during the incubation and were less sensitive to seasonal climatic variables than ANPP, probably because microbial transformations of N in the soil are mediated over even finer temporal scales. We found no relationship between ANPP and in situ net N‐mineralization. These results suggests that methods used to estimate in situ net N‐mineralization are inadequate to represent N‐availability across gradients where microbial biomass, N‐immobilization or competition among plants and microbes vary.     

中国典型植被类型NDVI动态变化与气温、降水变化的敏感性分析

 利用1983～1992年覆盖全国范围的、多时相的、NOAA／AVHRR的NDVI数字影像,结合我国160个基本标准气象站逐月的气温、降水资料,对10年来中国主要植被类型的遥感特征参数NDVI的动态变化与同期气温、降水变化的关系进行了分析。结果显示：就全国而言,从北到南,NDVI的变化与气候条件变化的相关系数逐渐降低;从东南到西北,NDVI的变化与气候条件变化的相关系数逐渐增加。     

Environmental controls of NDVI and sheep production in the Tierra del Fuego steppe of Argentina

Abstract. We analysed vegetation dynamics in Tierra del Fuego steppes using Normalized Difference Vegetation Index (NDVI) data provided by advanced very high‐resolution radiometer (AVHRR) on board the National Oceanic and Atmospheric Administration (NOAA) polar satellite. Our objective, at a regional scale, was to analyse the spatial variability of NDVI dynamics in relation to parent material and geographic location, representing the fertility and climate gradients respectively; at a local scale, it was to analyse the inter‐annual variability associated with climate and its relation with sheep production indices. The general pattern of NDVI dynamics was analysed with Principal Component Analysis. We found that the geographic location was more important than landscape type in explaining NDVI dynamics despite the fact that the variation in landscape type reflects a fertility gradient strongly associated with floristic composition and secondary productivity. Discriminant Analysis was performed to identify the variables that better distinguish geographic units. The Northern region (with the lowest precipitation and the highest temperatures) had lower NDVI values over the year. In the Central region, NDVI reached the highest value of the season, surpassing both other regions. The Southern region (the coldest and moistest) had its growth pattern displaced towards the summer. For the Central region we analysed 10 years of monthly NDVI data with PCA. We found that precipitation from August to December and winter temperature are the most important determinants of overall NDVI values. Lamb production was correlated with spring and early summer NDVI values. Sheep mortality is affected by low NDVI values in late summer and high annual amplitude. Satellite information allowed us to characterize the vegetation dynamics of three ecological areas across the Fuegian steppe.     

应用NOAA/AVHRR资料监测松毛虫危害研究初探

探讨了利用气象卫星定量监测松毛虫危害程度的可能性.以针叶被害率代表松毛虫的危害程度,轻度、中度、重度危害分别定义为针叶被害率<30%、30%～60%和>60%.根据地面光谱观测资料,建立了归一化植被指数与针叶被害率的相关方程,无松毛虫危害时NDVI为0.8823;为了消除大气等因子影响,利用松毛虫危害与未被危害的植被指数相对值表示松毛虫轻、中、重危害程度的遥感监测指标,无危害为1,0.78～1为轻度危害,0.57～0.78为中度危害,<0.57为重度危害.监测危害面积时,利用线性可加垂直植被指数进行混合象元分解.并分别对严重、中度、轻度3种类型发生年进行了定量监测分析,结果表明,AVHRR资料对中等以上松毛虫危害可进行定量监测分析,监测受灾面积比用同期的陆地卫星TM资料监测的受灾面积小12.1%～14.3%;对于轻度危害区域,采用气象卫星不易分辨,主要是由于不同下垫面和大气影响的差异,以及气象卫星空间分辨率较低.     

A global prognostic scheme of leaf onset using satellite data

Leaf phenology describes the seasonal cycle of leaf functioning. Although it is essential for understanding the interactions between the biosphere, the climate, and biogeochemical cycles, it has received little attention in the modelling community at global scale. This article focuses on the prediction of spatial patterns of the climatological onset date of leaf growth for the decade 1983–93. It examines the possibility of extrapolating existing local models of leaf onset date to the global scale. Climate is the main variable that controls leaf phenology for a given biome at this scale, and satellite observations provide a unique means to study the seasonal cycle of canopies. We combine leaf onset dates retrieved from NOAA/AVHRR satellite NDVI with climate data and the DISCover land‐cover map to identify appropriate models, and determine their new parameters at a 0.5° spatial resolution. We define two main regions: at temperate and high latitudes leaf onset models are mainly dependent on temperature; at low latitudes they are controlled by water availability. Some local leaf onset models are no longer relevant at the global scale making their calibration impossible. Nevertheless, we define our unified model by retaining the model that best reproduced the spatial distribution of leaf onset dates for each biome. The main spatial patterns of leaf onset date are well simulated, such as the Sahelian gradient due to aridity and the high latitude gradient due to frost. At temperate and high latitudes, simulated onset dates are in good agreement with climatological observations; 62% of treated grid‐cells have a simulated leaf onset date within 10 days of the satellite observed onset date (which is also the temporal resolution of the NDVI data). In tropical areas, the subgrid heterogeneity of the phenology is larger and our model's predictive power is diminished. The difficulties encountered in the tropics are due to the ambiguity of the satellite signal interpretation and the low reliability of rainfall and soil moisture fields.     

荒漠绿洲NDVI与气候、水文因子的相关分析

 通过利用1992～1996年NOAA/AVHRR逐旬的归一化植被指数（NDVI）数据和阜康气候、水文资料,分别对绿洲和荒漠进行了NDVI与气候、水文因子间的相关分析,得出了一些初步结论：绿洲与荒漠NDVI具有不同的季节变化规律;与绿洲NDVI相关显著的因子依次为气温、地下水位和降水,     

Environmental Controls of Primary Production in Agricultural Systems of the Argentine Pampas

We studied the aboveground net primary productivity (ANPP) of wheat crops in the Argentine Pampas. Our specific objectives were to determine (a) the response of ANPP to changes in water availability (b) the regional patterns of ANPP and (c) the interannual variability and environmental controls of ANPP. We used ground and satellite data to address these questions. Wheat ANPP was calculated as the ratio between grain yield and harvest index. We developed a simple model that took into account environmental and genetic improvement effects upon harvest index. We used the normalized difference vegetational index (NDVI) as a surrogate for ANPP at the county level. Straight-line regression models were fitted to single-year and average values of ANPP and precipitation to derive temporal and spatial models for wheat. For grasslands, we used spatial and temporal models already published. At any given site, there was no difference between modeled wheat and grassland average ANPP. The response of ANPP to changes in interannual water availability decreased along the precipitation gradient when vegetation structure (for example, species composition, density, and total cover) was held constant (wheat crops). Wheat ANPP and total production variability, estimated from remotely sensed data, decreased as mean annual precipitation (MAP) increased. The percentage of soils without drainage problems was the variable that explained most of the wheat ANPP spatial variability as shown by stepwise linear regression. Precipitation variability accounted for 49% of wheat ANPP variability. Remotely sensed estimates of ANPP variability showed lower and wheat ANPP higher temporal variability than annual precipitation.     

AVHRR NDVI与气候因子的相关分析

对中国１６０个气象站１０ａ的连续ＡＶＨＲＲ ＮＤＶＩ数据、气象观测数据进行相关分析,并结合植被覆盖类型资料深入探讨了ＡＶＨＲＲ ＮＤＶＩ／气温和ＡＶＨＲＲ ＮＤＶＩ／降水相关系数的地区差异及其随植被类型变化规律。研究结果表明,对中国的大部分地区,气温对植被的影响超过降水。就自然植被而言,其对降水的敏感性趋势为草本植被大于灌木植被,灌木植被大于乔木植被。就农作物而言,降水影响取决于耕作制度、作物种类     

发展NECT土地覆盖特征数据集的原理、方法和应用

着重探讨了建立中国东北样带 (NortheastChinatransect, NECT) 土地覆盖特征数据集的原理、方法及其在全球变化研究方面的重要应用。NECT土地覆盖特征数据集是以多时相的 1km分辨率的NOAA/AVHRR归一化植被指数NDVI (Normalizeddifferencevegetationindex) 数字影像为基础, 同时采用高程、气候、土壤、植被、土地利用、土地资源、生态区域、行政边界、经济、社会等多源数据作为数据源, 并经过标准化处理 (如数字化、空间插值、几何配准、投影转换 ) 集成而成。在土地覆盖特征数据集的主要应用方面, 如 :1) 利用多时相、1km分辨率的NOAA/AVHRR影像完成了中国东北样带土地覆盖分类图。一级分类系统包括森林、草原、荒漠和沙地、灌丛、农田、混合覆盖 类型、城镇和水体等 8类, 二级分类体系包括 12类。经过地面采样进行精度检验, 分类精度达到 81.6 1%。 2 ) 对主要植被类型的植物生长季变化进行的研究。利用多时相的遥感影像构造了能够反映植被年际、季节生长变化的遥感植被指数ND VImax、NDVI变幅xam以及NDVI的标准偏差x′s 等, 分析这 3个参数 1983~ 1999年的 17年中的变化情况。该数据集的建立是研究该样带土地覆盖特征及其变化规律的基础, 对基于样带的全球变化研究有重要的意义。     

Identification of current ecosystem functional types in the Iberian Peninsula

Aim To examine the geographical patterns of the interception of photosynthetically active radiation by vegetation and to describe its spatial heterogeneity through the definition of ecosystem functional types (EFTs) based on the annual dynamics of the Normalized Difference Vegetation Index (NDVI), a spectral index related to carbon gains. Location The Iberian Peninsula. Methods EFTs were derived from three attributes of the NDVI obtained from NOAA/AVHRR sensors: the annual integral (NDVI‐I), as a surrogate of primary production, an integrative indicator of ecosystem functioning; and the intra‐annual relative range (RREL) and month of maximum NDVI (MMAX), which represent key features of seasonality. Results NDVI‐I decreased south‐eastwards. The highest values were observed in the Eurosiberian Region and in the highest Mediterranean ranges. Low values occurred in inner plains, river basins and in the southeast. The Eurosiberian Region and Mediterranean mountains presented the lowest RREL, while Eurosiberian peaks, river basins, inner‐agricultural plains, wetlands and the southeastern part of Iberia presented the highest. Eurosiberian ecosystems showed a summer maximum of NDVI, as did high mountains, wetlands and irrigated areas in the Mediterranean Region. Mediterranean mountains had autumn–early‐winter maxima, while semi‐arid zones, river basins and continental plains had spring maxima. Based on the behaviour in the functional traits, 49 EFTs were defined. Main conclusions The classification, based on only the NDVI dynamics, represents the spatial heterogeneity in ecosystem functioning by means of the interception of radiation by vegetation in the Iberian Peninsula. The patterns of the NDVI attributes may be used as a reference in evaluating the impacts of environmental changes. Iberia had a high spatial variability: except for biophysically impossible combinations (high NDVI‐I and high seasonality), almost any pattern of seasonal dynamics of radiation interception was represented in the Peninsula. The approach used to define EFTs opens the possibility of monitoring and comparing ecosystem functioning through time.     

Decadal‐scale aspen changes: evidence in remote sensing and tree ring data

Question: (1) Which remote sensing classification most successfully identify aspen using multitemporal Landsat 5 TM images and airborne lidar data? (2) How has aspen distribution changed in southwestern Idaho? (3) Are topographic variables and conifer encroachment correlated with aspen changes? Location: Reynolds Creek Experimental Watershed in southwestern Idaho, USA. Methods: Multi‐temporal Landsat 5 TM and lidar data were used individually and fused together. The best classification model was compared with a 1965 aspen map and tree ring data. Conifer encroachment was examined via image‐based change detection and field mapping. Lidar‐derived topographic variables were correlated with aspen change patterns using quantile regression models. Results: The best Landsat 5 TM classification was a normalized difference vegetation index (NDVI)‐based approach with 92% overall accuracy. The lidar classification of tree presence/absence performed with 100% overall accuracy. Fusing the lidar classification with various Landsat 5 TM classifications improved overall accuracies 3 to 6%. Among the fusion models, the NDVI‐lidar fusion performed best with 96% overall accuracy. Change detection indicated 69% decline in aspen cover, but 179% increase in aspen cover in other areas of the watershed. Conifers have completely replaced 17% of the aspen, while 93% of the remaining aspen stands have young Douglas‐fir and western juniper trees underneath the aspen canopy. Aspen significantly decreased (P‐values <0.05) with increasing elevation (up to 2150 m) and decreasing slope. Conclusions: Landsat 5 TM data used with a NDVI‐based approach provide an accurate method to classify aspen distribution. Landsat 5 TM classifications can be further improved via fusion with lidar data. Aspen change patterns are spatially variable: while aspen is drastically declining in some parts of this watershed, aspen is increasing in other areas.