Debating the greening vs. browning of the North American boreal forest: differences between satellite datasets

A number of remote sensing studies have evaluated the temporal trends of the normalized difference vegetation index (NDVI or vegetation greenness) in the North American boreal forest during the last two decades, often getting quite different results. To examine the effect that the use of different datasets might be having on the estimated trends, we compared the temporal trends of recently burned and unburned sites of boreal forest in central Canada calculated from two datasets: the Global Inventory, Monitoring, and Modeling Studies (GIMMS), which is the most commonly used 8 km dataset, and a new 1 km dataset developed by the Canadian Centre for Remote Sensing (CCRS). We compared the NDVI trends of both datasets along a fire severity gradient in order to evaluate the variance in regeneration rates. Temporal trends were calculated using the seasonal Mann–Kendall trend test, a rank‐based, nonparametric test, which is robust against seasonality, nonnormality, heteroscedasticity, missing values, and serial dependence. The results showed contrasting NDVI trends between the CCRS and the GIMMS datasets. The CCRS dataset showed NDVI increases in all recently burned sites and in 50% of the unburned sites. Surprisingly, the GIMMS dataset did not capture the NDVI recovery in most burned sites and even showed NDVI declines in some burned sites one decade after fire. Between 50% and 75% of GIMMS pixels showed NDVI decreases in the unburned forest compared with <1% of CCRS pixels. Being the most broadly used dataset for monitoring ecosystem and carbon balance changes, the bias towards negative trends in the GIMMS dataset in the North American boreal forest has broad implications for the evaluation of vegetation and carbon dynamics in this region and globally.     

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.     

Spatiotemporal fire occurrence in Borneo over a period of 10 years

South-east Asia's tropical rainforests are experiencing the highest rate of deforestation worldwide and fire is one of the most important drivers of forest loss and subsequent carbon dioxide emissions. In this study, we analyzed all fire events in Borneo recorded by satellites over a period of 10 years. About 16.2 Mha, which corresponds to 21% of the land surface, have been affected by fire at least once and 6% more than one time. During El Niño conditions, which cause prolonged droughts in the region, the fire-affected area was on average three times larger than during normal weather conditions. Similarly, fires in forests affected 0.3 Mha in normal years and 1 Mha during El Niño years. Carbon rich peat swamp forest ecosystems were most severely affected. There is a pronounced difference in fire occurrence between different countries and provinces in Borneo although ecosystem and land use are very similar across the island. Compared with Sarawak, Sabah (Malaysia) and Brunei the relative annual fire-affected area in Kalimantan, the Indonesian part of Borneo, was on average five times larger. During El Niño conditions the fire-affected area increased only in Kalimantan and not in Brunei and the Malaysia. A similar pattern was observed in National Parks. This suggests, that El Niño related droughts are not the only cause of increased fire occurrence and do not necessarily lead to a higher number of fire events. These results improve our understanding of existing fire regimes and drivers of fire in SE Asian tropical ecosystems and may help to better protect the remaining rainforests.     

Remotely Sensed Interannual Variations and Trends in Terrestrial Net Primary Productivity 1981–2000

Spatial and temporal variations in net primary production (NPP) are of great importance to ecological studies, natural resource management, and terrestrial carbon sink estimates. However, most of the existing estimates of interannual variation in NPP at regional and global scales were made at coarse resolutions with climate-driven process models. In this study, we quantified global NPP variation at an 8 km and 10-day resolution from 1981 to 2000 based on satellite observations. The high resolution was achieved using the GLObal Production Efficiency Model (GLO-PEM), which was driven with variables derived almost entirely from satellite remote sensing. The results show that there was an increasing trend toward enhanced terrestrial NPP that was superimposed on high seasonal and interannual variations associated with climate variability and that the increase was occurring in both northern and tropical latitudes. NPP generally decreased in El Niño season and increased in La Niña seasons, but the magnitude and spatial pattern of the response varied widely between individual events. Our estimates also indicate that the increases in NPP during the period were caused mainly by increases in atmospheric carbon dioxide and precipitation. The enhancement of NPP by warming was limited to northern high latitudes (above 50°N); in other regions, the interannual variations in NPP were correlated negatively with temperature and positively with precipitation.     

A test of the use of NDVI data to predict secondary productivity

Question: How well does the use of NDVI predict secondary productivity at landscape scales? What is the influence of vegetation quality and phenology over secondary productivity? Location: Magellanic steppe in Tierra del Fuego, Argentina. (52°45’to 54° S, 68°15’to 67°30’W). Methods: Monthly and yearly integrated NDVI (NDVI‐I) were calculated from AVHRR/NOAA 14, as estimators of phenology and aerial net primary productivity respectively. From a vegetation map we obtained the proportional cover of different physiognomic types and calculated the palatable fraction (forage) productivity that were used as estimators of vegetation quality. Data were analysed through correlations and regressions. Results: NDVI‐I was not related with secondary productivity indices, while December and annual maximum NDVI, proportion of lawns and tussock grasslands and forage productivity were positively related with secondary productivity. A negative relationship was found between the proportion of heathlands and secondary productivity, but a positive relationship between heathland's proportion and NDVI‐I was found. Conclusions: NDVI‐I is not a good predictor of secondary productivity at the scale of our study. These results could be due to: (1) NDVI‐I is not related to primary productivity and (2) primary productivity is not related to secondary productivity.     

中国东北地区主要植被类型NDVI变化与气候因子的关系

利用1982～1992年时间序列的NOAA/AVHRR8km×8km分辨率的归一化植被指数（Normalizeddifferencevegetationindex,NDVI）,将东经120°～135°、北纬40°～55°区域的土地覆盖类型分为10类。然后研究了各类型的NDVI年平均值的变化规律。结合该地区的19个气象站1982～1992年的年平均气温、年最高温度、年最低温度、年降水量和年相对湿度研究了各类型NDVI年平均值的变化与气候因子之间的关系,进一步阐明了气候因子是NDVI动态变化的主要原因。     

基于GIS和RS的广东陆地植被生产力及其时空格局

在GIS和RS工具支持下,利用多时相遥感数据NOAA-AVHRRNDVI和地面气象数据研究了广东陆地植被净第一性生产力及其时空分布.结果表明广东陆地植被净第一性生产力的遥感估算值与实测值接近,效果较好;广东陆地植被净第一性生产力介于0～1568.9gC/(m2*a)之间,年平均净第一性生产力约为753.2(±277.0)gC/(m2*a),全省陆地生态系统每年约固定碳1.34×1014g.广东陆地植被净第一性生产力的地区差异显著,反映了广东陆地植被因受人类活动影响而比较破碎的特点;同样,广东陆地植被净第一性生产力的年变化显著,夏半年约为冬半年的4倍以上,这主要与该地区气温和水分条件的季节变化有关;即使是常绿阔叶林,其年净第一性生产力也有明显差异,且年变化显著.     

Modeling spatially explicit forest structural attributes using Generalized Additive Models

We modelled forest composition and structural diversity in the Uinta Mountains, Utah, as functions of satellite spectral data and spatially‐explicit environmental variables through generalized additive models. Measures of vegetation composition and structural diversity were available from existing forest inventory data. Satellite data included raw spectral data from the Landsat Thematic Mapper (TM), a GAP Analysis classified TM, and a vegetation index based on raw spectral data from an advanced very high resolution radiometer (AVHRR). Environmental predictor variables included maps of temperature, precipitation, elevation, aspect, slope, and geology. Spatially‐explicit predictions were generated for the presence of forest and lodgepole cover types, basal area of forest trees, percent cover of shrubs, and density of snags. The maps were validated using an independent set of field data collected from the Evanston ranger district within the Uinta Mountains. Within the Evanston ranger district, model predictions were 88% and 80% accurate for forest presence and lodgepole pine (Pinus contorta), respectively. An average 62% of the predictions of basal area, shrub cover, and snag density fell within a 15% deviation from the field validation values. The addition of TM spectral data and the GAP Analysis TM‐classified data contributed significantly to the models' predictions, while AVHRR had less significance.     

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.