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.     

积雪、土壤冻融与土壤水分遥感监测研究进展

积雪、土壤冻融与土壤水分是陆表能量与水分以及碳交换过程研究中的重要因子,为了更好地了解积雪覆盖、雪深/雪水当量、土壤冻融状态和土壤水分等参数的遥感监测领域的发展动态,对这些参数遥感监测方法的研究进展进行了梳理,总结了利用光学与微波遥感,以及多源遥感融合的监测方法,并对该研究领域的发展趋势进行了展望。积雪、土壤冻融与土壤水分的遥感监测能力不断提升,监测算法从单一传感器向多传感器、单波段单一模式向多波段多模式集成,以及卫星虚拟星座综合观测概念的提出,均促进了现有卫星观测地表参数能力的提升;长时间序列产品的开发,对于研究和掌握全球变化大背景下对气候的响应提供了很好的数据基础;同时有助于促进遥感在水文、气象、气候、生态等领域的应用。以上的研究综述,有望对陆表水循环遥感参数反演领域,以及水循环遥感关键参数的应用领域有一定的借鉴作用。     

Seasonal reflectance dynamics of common understory types in a northern European boreal forest

The influence of the seasonal cycle of boreal forest understory has been noticed in global remote sensing of vegetation, especially in remote sensing of biophysical properties (e.g. leaf area index) of the tree-layer in a forest. A general problem in the validation of operationally produced global biophysical vegetation products is the lack of ground reference data on the seasonal variability of different land surface types. Currently, little is known about the spectral properties of the understory layers of boreal forests, and even less is known about the seasonal dynamics of the spectra. In this paper, we report seasonal trajectories of understory reflectance spectra measured in a European boreal forest. Four study sites representing different forest fertility site types were selected from central Finland. The understory composition was recorded and its spectra measured with an ASD FieldSpec Hand-Held UV/VNIR Spectroradiometer ten times during the growing period (from May to September) in 2010. Our results show that the spectral differences between and within understory types are the largest at the peak of the growing season in early July whereas in the beginning and end of the growing season (i.e. early May and late September, respectively) the differences between the understory types are marginal. In general, the fertile sites had the brightest NIR spectra throughout the growing season whereas infertile types appeared darker in NIR. Our results also indicated that a mismatch in the seasonal development of understory and tree layers does not occur in boreal forests: the understory and tree layer vegetation develop at a similar pace in the spring (i.e. there are no or only few spring ephemerals present), and the forests with the strongest seasonal dynamics in tree canopy structure (LAI) have also the strongest dynamics in understory spectra.     

Bryophyte cover estimation in a boreal black spruce forest using airborne lidar and multispectral sensors

Bryophytes are the dominant ground cover vegetation layer in many boreal forests and in some of these forests the net primary production of bryophytes exceeds the overstory. Therefore it is necessary to quantify their spatial coverage and species composition in boreal forests to improve boreal forest carbon budget estimates. We present results from a small exploratory test using airborne lidar and multispectral remote sensing data to estimate the percentage of ground cover for mosses in a boreal black spruce forest in Manitoba, Canada. Multiple linear regression was used to fit models that combined spectral reflectance data from CASI and indices computed from the SLICER canopy height profile. Three models explained 63-79% of the measured variation of feathermoss cover while three models explained 69-92% of the measured variation of sphagnum cover. Root mean square errors ranged from 3-15% when predicting feathermoss, sphagnum, and total moss ground cover. The results from this case study warrant further testing for a wider range of boreal forest types and geographic regions.     

Using multi-source data from lidar,radar, imaging spectroscopy,and national forest inventories to simulate forest carbon fluxes

Terrestrial biosphere carbon dynamics are the most uncertain elements of the global carbon budget. Carbon stocks estimated using spatially extensive remote sensing are crucial in reducing this uncertainty, and using these stocks as initial conditions to biosphere models can improve carbon flux predictions beyond the site level. Yet remote-sensing data are not always consistently available for large regions, so methods assessing carbon uncertainty using data sources in one location may not be transferable to another. This study assesses the use of multiple-source data from lidar, radar, imaging spectroscopy, and national forest inventories to derive forest structure and composition necessary to initialise the Ecosystem Demography model (ED2), and hence evaluate short-term carbon flux uncertainty over Harvard Forest, Massachusetts. ED2 was initialized using forest structure and composition derived from lidar and national forest inventories, radar and national forest inventories, lidar and imaging spectroscopy, and radar and imaging spectroscopy resulting in net ecosystem productivity uncertainty of 26.3%, 41.9%, 19.6%, and 20.2%, respectively, compared to ground-based forest inventory initializations. This study uniquely offers a multitude of methods to estimate forest ecosystem state, with resulting carbon uncertainties, transferable to regions with potentially different data availability. Furthermore, in preparation for satellite radar, lidar, and imaging spectrometer, this study highlights the importance of combining techniques deriving forest structure and composition at different scales, binding regional to potentially global carbon-fluxes with remote sensing, reducing this uncertainty source in global climate models.     

Satellite monitoring of boreal forest phenology and its climatic responses in Eurasia

The fragile ecosystems in boreal Eurasia are sensitive to global climate change. Land surface phenology provides an important tool for us to better understand the current status of boreal forest and its climatic responses in this remote zone. This study utilizes the new-generation AVHRR GIMMS NDVI3g products in 1982–2011 to extract four phenological metrics in the study region, including start of season (SOS), end of season (EOS), season length (LOS), and middle of season (MOS). Linear and Mann–Kendall trend analyses are performed to examine their spatiotemporal patterns and relationships with climatic variables assisted with the Climate Research Unit re-analysis climatic data sets. While advanced spring greening is observed in agreement with past studies, our results reveal that the SOS advance mostly occurs in mixed forests in southern Eurasia. More importantly, this study extracts the opposite trends for the end of season – advanced EOS in coniferous forests above 60°N and delayed EOS in mixed forests below. Overall, temperature in May–October has consecutively increased in the past 30 years. Precipitation has also increased but with a fragmented pattern. The advanced SOS across Eurasia is highly correlated with a warmer spring (April and May) in Eurasia. The EOS has a strong, negative relationship with fall precipitation (September). Further investigation is suggested to examine the opposite EOS trends and their environmental/ecological consequences in different forest zones of boreal Eurasia.     

Studying the change in fAPAR after forest fires in Siberia using MODIS

Disturbance events such as fire have major effects on forest dynamics, succession and the carbon cycle in the boreal biome. This paper focuses on establishing whether characteristic spatio‐temporal patterns of the fraction of Absorbed Photosynthetically Active Radiation (fAPAR) occur in the initial two years after a fire event in Siberian boreal forests. Time‐series of MODIS fAPAR were used to study post‐fire dynamics during the year of the fire and the following two years. Three forest types (evergreen needle‐leaf, deciduous needle‐leaf and deciduous broadleaf) grouped into three latitudinal regions, ranging from 51° N to 65° N, were studied by analysing a sample of 14 burned areas. For each of the burned areas an adjacent unburned control plot was selected with the aim of separating inter‐annual variations caused by climate from changes in fAPAR behaviour due to a burn. The results suggest that (i) the forest types exhibit characteristic fAPAR change trajectories shortly after the fire, (ii) the differences in the fAPAR trajectories are related to the forest type, (iii) fAPAR changes are not significantly different among the latitudinal regions, and (iv) the limited temporal variability observed among the 3 years of observations indicates that fAPAR varies very little in the initial years after a fire event.     

Assessing tundra-taiga boundary with multi-sensor satellite data

Monitoring the dynamics of the circumpolar boreal forest (taiga) and Arctic tundra boundary is important for understanding the causes and consequences of changes observed in these areas. This ecotone, the world's largest, stretches for over 13,400 km and marks the transition between the northern limits of forests and the southern margin of the tundra. Because of the inaccessibility and large extent of this zone, remote sensing data can play an important role for mapping the characteristics and monitoring the dynamics. Basic understanding of the capabilities of existing space borne instruments for these purposes is required. In this study we examined the use of several remote sensing techniques for characterizing the existing tundra-taiga ecotone. These include Landsat-7, MISR, MODIS and RADARSAT data. Historical cover maps, recent forest stand measurements and high-resolution IKONOS images were used for local ground truth.It was found that a tundra-taiga transitional area can be characterized using multi-spectral Landsat ETM+ summer images, multi-angle MISR red band reflectance images, RADARSAT images with larger incidence angle, or multi-temporal and multi-spectral MODIS data. Because of different resolutions and spectral regions covered, the transition zone maps derived from different data types were not identical, but the general patterns were consistent.     

Forest Above Ground Biomass Estimation from Remotely Sensed Imagery in the Mount Tai Area Using the RBF ANN Algorithm

Forest biomass is a significant indicator for substance accumulation and forest succession, and can provide valuable information for forest management and scientific planning. Accurate estimations of forest biomass at a fine resolution are important for a better understanding of the forest productivity and carbon cycling dynamics. In this study, considering the low efficiency and accuracy of the existing biomass estimation models for remote sensing data, Landsat 8 OLI imagery and field data cooperated with the radial basis function artificial neural network (RBF ANN) approach is used to estimate the forest Above Ground Biomass (AGB) in the Mount Tai area, Shandong Province of East China. The experimental results show that the RBF model produces a relatively accurate biomass estimation compared with multivariate linear regression (MLR), k-Nearest Neighbor (KNN), and backpropagation artificial neural network (BP ANN) models.     

AVHRR数据小火点自动识别方法的研究

利用NOAA-AVHRR数据,采用多因子分析方法,通过建立小火点自动识别模型来提取小火点燃烧信息。经实验验证,该方法能较好地减少云体、裸地对火点判断的干扰,从而在一定程度上提高了对小火点的监测精度。     

The role of remote sensing on studying mangrove forest extent change

ABSTRACT

Given the ability of effectively observing vegetation at a variety of spatial and temporal scales, remote sensing has been widely used to monitor and understand the change of mangrove forest extent. Yet, a systematic cognition of the unique contribution of remote sensing on studying mangrove forest extent dynamic is lacking, which prevents us from clearly identifying and further overcoming current deficiencies of studying the change of mangrove forest extent with remote sensing. Therefore, in this review, we summarized remotely sensed extraction methods and data related to mangrove forest extent change monitoring and all discoveries from mangrove forest extent change observation and driver analysis. We found that mangrove forest in most of the study areas has declined over the past decades, and the loss rate is accelerating. We also found that remote sensing plays an important role in revealing how the extent of mangrove forest responds to climate change and human impacts. In addition, we identified several recurring limitations from previous studies. This review highlights the role of remote sensing on studying mangrove forest extent change, and is helpful to make better use of remotely sensed data in this field.     

Classification of forest volume resources using ERS tandem coherence and JERS backscatter data

Considering recent progress in the development of techniques and methods to achieve biomass estimates and full carbon accounting, remote sensing research of forested ecosystems needs to be aimed towards the retrieval of information at global scales. In this paper, an algorithm for the estimation of growing stock volume, an important parameter for the commercial forest community and a proxy for woody biomass density, from ERS and JERS synthetic aperture radar (SAR) data is described. The algorithm is based on the information content of both ERS tandem coherence and JERS backscatter images and was developed using ground data, made available by the Russian Forestry Services. It is tested on SAR datasets of boreal forests in Siberia, a managed, temperate forest plantation in the United Kingdom and a semi-natural boreal forest at Siggefora in Sweden. Comparisons of the classified products, comprising three growing stock interval classes and one non-forest class are made with ground data. The results of this accuracy assessment exercise show that the algorithm yields, in all cases, overall classification accuracies of greater than 70%. A visual comparison is made of the algorithm performance over a tropical forest region of Brazil. The results indicate that the algorithm has the potential to retrieve growing stock volume estimates in forest ecosystems throughout the globe.     

Variogram analysis of high spatial resolution remote sensing data: An examination of boreal forest ecosystems

The site-specific Northwestern Ontario Forest Ecosystem Classification (NWO FEC) was adapted to provide a landscape-scale (1:20 000) forest ecosystem classification for a study area located in the boreal forest of northwestern, Ontario, Canada. High spatial resolution remote sensing data were collected at two altitudes (600 m and 1150 m AGL) using the Compact Airborne Spectrographic Imager (CASI). Variogram analyses were performed on these data to determine the nature of spatial dependence of spectral reflectance for selected forest ecosystems. It was determined that an optimal size of support for characterizing forest ecosystems, as estimated by the mean ranges of a series of variograms, differed based on the altitude of the remote sensing system, indicating that different ecological units and/or processes are captured at these two altitudes. Results imply a linear aggregation of reflectance when discrete objects are not resolvable. This observation has significant implications for scaling of reflectance data, albeit restricted to a narrow range of spatial scales.     

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.     

Improving the estimation of leaf area index by using remotely sensed NDVI with BRDF signatures

A new vegetation index, the Normalized Hotspot-signature Vegetation Index (NHVI), is proposed for a better quantitative estimation of leaf area index (LAI) than with the remotely sensed normalized difference vegetation index (NDVI), especially in the boreal forest. To obtain this new index, the Hotspot-Dark-spot index (HDS) (Lacaze et al., 2002) was introduced. HDS is calculated by the difference between the strongest vector (hotspot) and the weakest vector (dark-spot) of bi-directional reflectance, a given tract of vegetation returns in the reflecting solar position, and the geometric structure of the vegetation canopy, which are poorly represented by NDVI alone. The validity of NHVI was statistically tested using two field data sets of multi-angular observations and LAI from the boreal forests of Canada; one set was our own observations, and the other was from the Boreal Ecosystem-Atmosphere Study (BOREAS). The range of linear correspondence of NHVI with LAI is much wider than that of NDVI alone, indicating significant representation of leaf biomass in the canopy geometry captured by HDS. With the technical innovation of multi-angular remote-sensing and kernel-driven models in the future, this index has the potential to provide a more accurate evaluation of regional and global LAIs.     

Evaluate Quantitatively Effects of Understory on Leaf Area Index (LAI) Estimation Combining Active and Passive Remote Sensing Data

Natural forests have the vertical three\|dimensional structure of canopy and understory vegetation (shrubs,grasslands,and bare soil).Accurate and quantitative separation of understory vegetation is of great scientific significance and practicality on improving the precision of inversion of forest canopy leaf area index.value.Due to the limitations of traditional passive optical remote sensing data on directly acquiring 3D information,this study intends to combine active and passive ALS and HyperMap data with the Washington Botanic Garden as the key research area.On the basis of individual tree segmentation,the vertical stratification of the forest (forest canopy and undergrowth vegetation layer) is achieved.On this basis,the forest canopy laser point cloud data was used to remove the understory information from the optical image data.By comparing the results of the forest effective leaf area index obtained from aerial optical images and ground measurements,it was found that：(1) forest canopy density has a significant impact on the penetration of ALS data;(2) removal of understory information can effectively improve the forest crown accuracy of LAIe estimated.The correlation between Normalized Difference Vegetation Index (NDVI) and ground surface measured effective leaf area index increased from 0.087 to 0.591.In addition,the optical remote sensing image based on the removal of understory vegetation information was compared with the Simple Ratio vegetation index (SR) (the correlation increased from 0.209 to 0.559) and the simplified simple Ratio vegetation index (RSR) (the correlation increased from 0.147 to 0.358).The NDVI was most sensitive to changes in canopy leaf area index (correlation increased by 0.5).The method of quantitatively separating understory vegetation with the combined active and passive remote sensing data proposed in this study can effectively improve the accuracy of inversion of forest canopy leaf area index,and provide a solid foundation for quantitative and accurate estimate of forest biophysical parameters and study of carbon and water cycle processes.     

Data-Driven Estimation of Gross Primary Production

Gross Primary Production (GPP) of vegetation refers to the assimilation of all organic matter produced by green plants through photosynthesis and fixed carbon dioxide per unit time and unit area. Accurate estimation of GPP is helpful for the study of carbon cycle. In order to improve the estimation accuracy of GPP, this study combines machine learning technology and remote sensing technology. First, the remote sensing data under the GEE platform and the flux tower measurement data of the China Terrestrial Ecosystem Flux Observation Research Network are used to establish a data set. Then use random forest as the estimation model, and adjust the model according to the data characteristics after modeling. Finally, the prediction results of the model are obtained, the determination coefficient R² is 0.87, and the root mean square error RMSE is 1.132 gC·m^-2·d^-1. This shows that the random forest model can estimate GPP more accurately.From the results of this study, we can see that the rapid development of computer technology represented by big data and artificial intelligence will inject new vitality into remote sensing technology and make remote sensing technology enter a more mature stage of development and application.     

Comparison and assessment of coarse resolution land cover maps for Northern Eurasia

Information on land cover at global and continental scales is critical for addressing a range of ecological, socioeconomic and policy questions. Global land cover maps have evolved rapidly in the last decade, but efforts to evaluate map uncertainties have been limited, especially in remote areas like Northern Eurasia. Northern Eurasia comprises a particularly diverse region covering a wide range of climate zones and ecosystems: from arctic deserts, tundra, boreal forest, and wetlands, to semi-arid steppes and the deserts of Central Asia. In this study, we assessed four of the most recent global land cover datasets: GLC-2000, GLOBCOVER, and the MODIS Collection 4 and Collection 5 Land Cover Product using cross-comparison analyses and Landsat-based reference maps distributed throughout the region. A consistent comparison of these maps was challenging because of disparities in class definitions, thematic detail, and spatial resolution. We found that the choice of sampling unit significantly influenced accuracy estimates, which indicates that comparisons of reported global map accuracies might be misleading. To minimize classification ambiguities, we devised a generalized legend based on dominant life form types (LFT) (tree, shrub, and herbaceous vegetation, barren land and water). LFT served as a necessary common denominator in the analyzed map legends, but significantly decreased the thematic detail. We found significant differences in the spatial representation of LFT's between global maps with high spatial agreement (above 0.8) concentrated in the forest belt of Northern Eurasia and low agreement (below 0.5) concentrated in the northern taiga-tundra zone, and the southern dry lands. Total pixel-level agreement between global maps and six test sites was moderate to fair (overall agreement: 0.67-0.74, Kappa: 0.41-0.52) and increased by 0.09-0.45 when only homogenous land cover types were analyzed. Low map accuracies at our tundra test site confirmed regional disagreements and difficulties of current global maps in accurately mapping shrub and herbaceous vegetation types at the biome borders of Northern Eurasia. In comparison, tree dominated vegetation classes in the forest belt of the region were accurately mapped, but were slightly overestimated (10%-20%), in all maps. Low agreement of global maps in the northern and southern vegetation transition zones of Northern Eurasia is likely to have important implications for global change research, as those areas are vulnerable to both climate and socio-economic changes.     

数据驱动的植被总初级生产力估算方法研究

植被总初级生产力（Gross Primary Production,GPP）是指在单位时间和单位面积上,绿色植物通过光合作用固定二氧化碳所产生的全部有机物同化量,对GPP的准确估算有助于碳循环的研究。为了提高GPP的估算精度,将机器学习技术与遥感技术相结合,首先利用GEE平台下的遥感数据以及中国陆地生态系统通量观测研究网络的通量塔实测GPP数据,建立数据集。然后使用随机森林作为估算模型,建模后根据数据特点对模型调参。最后获得模型的预测结果,决定系数R²为0.87,均方根误差RMSE的值为1.132 gC·m^-2·d^-1。这说明随机森林模型可以较为精确地估算GPP。结果发现,以大数据以及人工智能为代表的计算机技术飞速发展,将为遥感技术注入新的活力,使遥感技术走向更加成熟的发展应用阶段。