Carbon sinks and tropical forest biomass estimation: a review on role of remote sensing in aboveground-biomass modelling

Tropical forest embraces a large stock of carbon and contributes to the enormous amount of above- and below-ground biomass and the global carbon cycle. The carbon kept in the above-ground living biomass of trees is typically the largest pool and the most directly impacted by deforestation and degradation. Hence, quantifying carbon stock and fluxes from tropical forests by estimating the above-ground forest biomass is the critical step that will be investigated further in this paper. Remote sensing technology can provide many advantages in quantifying and mapping forest structure and monitoring and mapping above-ground biomass, and is both temporally and spatially accurate. Therefore, a good data-set of biomass which comprises canopy height and canopy structure can provide carbon sequestration potential for forest reserves. This paper reviews a thorough research of biomass estimation using remote sensing and geospatial technologies.     

Assessing changes in the above ground biomass and carbon stocks of Lidder valley,Kashmir Himalaya,India

The changes in the land use and land cover (LULC), above ground biomass (AGB) and the associated above ground carbon (AGC) stocks were assessed in Lidder Valley, Kashmir Himalaya using satellite data (1980–2013), allometric equations and phytosociological data. Change detection analysis of LULC, comprising of eight vegetation and five non-vegetation types, indicated that 6% (74.5 km²) of the dense evergreen forest has degraded. Degraded forest and settlement increased by 20 and 52.8 km², respectively. Normalized difference vegetation index was assessed and correlated with the field-based biomass estimates to arrive at best-fit models for remotely sensed AGB estimates for 2005 and 2013. Total loss of 1.018 Megatons of AGB and 0.5 Megatons of AGC was estimated from the area during 33-year period which would have an adverse effect on the carbon sequestration potential of the area which is already facing the brunt of climate change.     

Predictive Analysis for Vegetation Biomass Assessment in Western Ghat Region (WG) Using Geospatial Techniques

Forests play a critical role in ecological functioning, global warming and climate change through its unique potential to capture and hold carbon (C). Biomass is one of the indicator of the status of forests hence accurate assessment and biomass mapping is important for sustainable forest management. The objectives of this study is to estimate above ground biomass (AGB) from field inventory data and to map AGB combining field inventory data, remote sensing and geo-statistical model. In the present study stratified random sampling were used for estimation of biomass in which 59 plots were laid down in different homogenous strata depending on the NDVI values for the region of Maharashtra Western Ghats. The above ground biomass from field ranged from 0.05 to 271 t-dry wt ha^?1 in which trees added maximum towards total biomass followed by shrubs and herbs. This paper evaluates the best vegetation indices to estimate biomass. This study was carried out by using Landsat TM satellite data and field inventory data in the Ratnagiri district of Maharashtra, India. A significant correlation was observed between biomass and vegetation indices. The best fit regression equation developed from field above ground biomass and NDVI with R² value of 0.61 was used for spectral modeling to estimate the geospatial distribution of AGB in the entire region. The results of spatial predictions Geostatistical technique and remotely sensed data as auxiliary variables were compared using statistical error methods. This study employed Mean error, Root-Mean-Square error, Average Standard error and Root-Mean Square Standardized error. The ME, RMSE, Average Standard error and Root-Mean Square Standardized error was 0.078, 8.032, 7.982 and 0.967 respectively. The results showed that cokriging technique is one of the geostatistical method for spatial predictions of biomass in the studied region. The present study revealed that remote sensing technique combined with field sampling provides quick and reliable estimates of above ground biomass and carbon pool and can be used as baseline information for further temporal studies of biomass status of the region and in planning of forest and natural resources management.     

Mapping and monitoring carbon stocks with satellite observations: a comparison of methods

Mapping and monitoring carbon stocks in forested regions of the world, particularly the tropics, has attracted a great deal of attention in recent years as deforestation and forest degradation account for up to 30% of anthropogenic carbon emissions, and are now included in climate change negotiations. We review the potential for satellites to measure carbon stocks, specifically aboveground biomass (AGB), and provide an overview of a range of approaches that have been developed and used to map AGB across a diverse set of conditions and geographic areas. We provide a summary of types of remote sensing measurements relevant to mapping AGB, and assess the relative merits and limitations of each. We then provide an overview of traditional techniques of mapping AGB based on ascribing field measurements to vegetation or land cover type classes, and describe the merits and limitations of those relative to recent data mining algorithms used in the context of an approach based on direct utilization of remote sensing measurements, whether optical or lidar reflectance, or radar backscatter. We conclude that while satellite remote sensing has often been discounted as inadequate for the task, attempts to map AGB without satellite imagery are insufficient. Moreover, the direct remote sensing approach provided more coherent maps of AGB relative to traditional approaches. We demonstrate this with a case study focused on continental Africa and discuss the work in the context of reducing uncertainty for carbon monitoring and markets.     

湖泊碳循环研究中遥感技术的机遇与挑战

湖泊碳循环是全球碳循环过程中的重要环节,随着全球碳循环研究的不断深入,湖泊碳循环对全球碳循环的影响,以及其对全球气候变化的调节作用越来越受到关注.然而,由于湖泊分布的破碎性(大于0.002 km2的湖泊约有1.17×108个,并零星地分布在全球)和多样性(流域生态多样性,湖泊类型多样性,分布的气候带多样性等),使得全面...     

Application of remote sensing technology for Impact Assessment of Watershed Development Programme

The current study was taken up to investigate the utility of remote sensing and GIS tools for evaluation of Integrated Wasteland Development Programme (IWDP) implemented during 1997–2001 in Katangidda Nala watershed, Chincholi taluk, Gulbarga district, Karnataka. The study was carried out using IRS 1C, LISS III data of December 11, 1997 (pre-treatment) and November 15, 2002 (post-treatment) covering the watershed to assess the changes in land use / land cover and biomass that have changed over a period of five years (1997–2002). The images were classified into different land use/land cover categories using supervised classification by maximum likelihood algorithm. They were also classified into different biomass levels using Normalized Difference Vegetation Index (NDVI) approach. The results indicated that the area under agriculture crops and forest land were increased by 671 ha (5.7%) and 1,414 ha (11.94%) respectively. This is due to the fact that parts of wastelands and fallow lands were brought into cultivation. This increase in the area may be attributed to better utilization of surface and ground waters, adoption of soil and water conservation practices and changes in cropping pattern. The area under waste lands and fallow lands decreased by 1,667 ha (14.07%) and 467 ha (3.94%), respectively. The vegetation vigour of the area was classified into three classes using NDVI. Substantial increase in the area under high and low biomass levels was observed (502 ha and 19 ha respectively). The benefit-cost analysis indicates that the use of remote sensing and GIS was 2.2 times cheaper than the conventional methods. Thus, the repetitive coverage of the satellite data provides an excellent opportunity to monitor the land resources and evaluate the land cover changes through comparison of images for the watershed at different periods.     

基于像元二分模型的地表植被覆盖度估算模块化设计与实现

地表植被覆盖度是的一种应用广泛的定量遥感产品,在水文、生态、区域变化等方面都具有重要的意义。像元二分模型是应用最多的一种遥感估算地表覆盖度的方法。目前,用遥感的方法进行地表植被覆盖度估算没有完整、系统的工具,用户只能逐步进行操作,效率低下,鉴于上述情况,本文运用IDL交互式数据语言,基于ENVI二次开发了一个植被覆盖度估算程序,取得了一定的成效,对遥感定量产品的生产、应用具有一定意义。     

利用GPS-R遥感技术反演植被生物量

为了验证利用GPS-R遥感技术反演植被生物量的可行性,分析了GPS反射信号反演植被生物量的原理与方法,通过试验利用干涉复合场数据（ICF）计算出信号反射率,利用已有文献拟合出的信号反射率与植被生物量之间的关系求出生物量,并与遥感技术结果进行了对比。试验结果表明：利用GPS-R遥感技术反演植被生物量是可行的,但是精度还需依靠时间反演模型;GNSS-R反演植被生物量对分析调查区域植被生长和碳排放情况具有重要意义。     

自然火灾碳排放估算模型参数的遥感反演进展

植被燃烧会产生大量的温室气体,为及时了解自然火灾对区域和全球尺度的碳循环和碳平衡模式的影响,有必要研究自然火灾碳排放的大尺度估算方法。结合遥感技术"宏观、快速、实时、客观"获取地表信息的独特优势,对利用遥感技术估算自然火灾碳排放的方法进行综述,概括了自然火灾碳排放使用的估算模型及其需要的主要输入参数,其次分别介绍了模型输入参数的遥感估算方法,并对各方法的优缺点进行分析和评价,最后提出现有方法的改进建议。     

基于国产卫星影像的自然资源动态监测

准确及时地掌握山、水、林、田、湖、草系统变化状况，是自然资源管理重点关注的问题。卫星遥感技术具有宏观、综合、动态、快速的特点，能够应用于自然资源领域开展目标识别、信息采集与处理、分析与评估等工作，可作为自然资源监测的有效手段。本文以“土地、矿产、海洋”等重要自然资源要素为动态监测研究目标，基于多源多时相国产卫星遥感影像数据和各类审批、规划专题资料数据，开展自然资源动态监测研究。研究结果表明，深入挖掘国产卫星影像资源潜力，实现自然资源精细遥感监测服务模式是切实可行与高效的。     

基于CBERS的黄河湿地生物量反演研究

本研究以黄河湿地郑州段为研究区,利用RS与GIS技术,基于2009年5月7日获得的CBERS 2B卫星CCD多光谱遥感影像和地面实测植被生物量信息,采用回归算法构建反演模型,经对比分析得到最优计算模型。结果表明,反演模型计算精度达到86.32%,利用CBERS 2B卫星CCD多光谱遥感影像可满足植被生物量的反演需要。     

Predicting soil organic carbon stocks under commercial forest plantations in KwaZulu-Natal province,South Africa using remotely sensed data

ABSTRACT

Commercial forest plantations are increasing globally, absorbing a large amount of carbon valuable for climate change mitigation. Whereas most carbon assimilation studies have mainly focused on natural forests, understanding the spatial distribution of carbon in commercial forests is central to determining their role in the global carbon cycle. Forest soils are the largest carbon reservoir; hence soils under commercial forests could store a significant amount of carbon. However, the variability of soil organic carbon (SOC) within forest landscapes is still poorly understood. Due to limitations encountered in traditional systems of SOC determination, especially at large spatial extents, remote sensing approaches have recently emerged as a suitable option in mapping soil characteristics. Therefore, this study aimed at predicting soil organic carbon (SOC) stocks in commercial forests using Landsat 8 data. Eighty-one soil samples were processed for SOC concentration and fifteen Landsat 8 derived variables, including vegetation indices and bands were used as predictors to SOC variability. The random forest (RF) was adopted for variable selection and regression method for SOC prediction. Variable selection was done using RF backward elimination to derive three best subset predictors and improve prediction accuracy. These variables were then used to build the RF final model for SOC prediction. The RF model yielded good accuracies with root mean square error of prediction (RMSE) of 0.704 t/ha (16.50% of measured mean SOC) and 10-fold cross-validation of 0.729 t/ha (17.09% of measured mean SOC). The results demonstrate the effectiveness of Landsat 8 bands and derived vegetation indices and RF algorithm in predicting SOC stocks in commercial forests. This study provides an effective framework for local, national or global carbon accounting as well as helps forest managers constantly evaluate the status of SOC in commercial forest compartments.     

The effects of land cover change on carbon stock dynamics in a dry Afromontane forest in northern Ethiopia

Background

Forests play an important role in mitigating global climate change by capturing and sequestering atmospheric carbon. Quantitative estimation of the temporal and spatial pattern of carbon storage in forest ecosystems is critical for formulating forest management policies to combat climate change. This study explored the effects of land cover change on carbon stock dynamics in the Wujig Mahgo Waren forest, a dry Afromontane forest that covers an area of 17,000 ha in northern Ethiopia.

Results

The total carbon stocks of the Wujig Mahgo Waren forest ecosystems estimated using a multi-disciplinary approach that combined remote sensing with a ground survey were 1951, 1999, and 1955 GgC in 1985, 2000 and 2016 years respectively. The mean carbon stocks in the dense forests, open forests, grasslands, cultivated lands and bare lands were estimated at 181.78?±?27.06, 104.83?±?12.35, 108.77?±?6.77, 76.54?±?7.84 and 83.11?±?8.53 MgC ha^?1 respectively. The aboveground vegetation parameters (tree density, DBH and height) explain 59% of the variance in soil organic carbon.

Conclusions

The obtained estimates of mean carbon stocks in ecosystems representing the major land cover types are of importance in the development of forest management plan aimed at enhancing mitigation potential of dry Afromontane forests in northern Ethiopia.

     

Monitoring winter wheat growth in North China by combining a crop model and remote sensing data

Both of crop growth simulation models and remote sensing method have a high potential in crop growth monitoring and yield prediction. However, crop models have limitations in regional application and remote sensing in describing the growth process. Therefore, many researchers try to combine those two approaches for estimating the regional crop yields. In this paper, the WOFOST model was adjusted and regionalized for winter wheat in North China and coupled through the LAI to the SAIL–PROSPECT model in order to simulate soil adjusted vegetation index (SAVI). Using the optimization software (FSEOPT), the crop model was then re-initialized by minimizing the differences between simulated and synthesized SAVI from remote sensing data to monitor winter wheat growth at the potential production level. Initial conditions, which strongly impact phenological development and growth, and which are hardly known at the regional scale (such as emergence date or biomass at turn-green stage), were chosen to be re-initialized. It was shown that re-initializing emergence date by using remote sensing data brought simulated anthesis and maturity date closer to measured values than without remote sensing data. Also the re-initialization of regional biomass weight at turn-green stage led that the spatial distribution of simulated weight of storage organ was more consistent to official yields. This approach has some potential to aid in scaling local simulation of crop phenological development and growth to the regional scale but requires further validation.     

New vegetation type map of India prepared using satellite remote sensing: Comparison with global vegetation maps and utilities

A seamless vegetation type map of India (scale 1: 50,000) prepared using medium-resolution IRS LISS-III images is presented. The map was created using an on-screen visual interpretation technique and has an accuracy of 90%, as assessed using 15,565 ground control points. India has hitherto been using potential vegetation/forest type map prepared by Champion and Seth in 1968. We characterized and mapped further the vegetation type distribution in the country in terms of occurrence and distribution, area occupancy, percentage of protected area (PA) covered by each vegetation type, range of elevation, mean annual temperature and precipitation over the past 100 years. A remote sensing-amenable hierarchical classification scheme that accommodates natural and semi-natural systems was conceptualized, and the natural vegetation was classified into forests, scrub/shrub lands and grasslands on the basis of extent of vegetation cover. We discuss the distribution and potential utility of the vegetation type map in a broad range of ecological, climatic and conservation applications from global, national and local perspectives. We used 15,565 ground control points to assess the accuracy of products available globally (i.e., GlobCover, Holdridge’s life zone map and potential natural vegetation (PNV) maps). Hence we recommend that the map prepared herein be used widely. This vegetation type map is the most comprehensive one developed for India so far. It was prepared using 23.5 m seasonal satellite remote sensing data, field samples and information relating to the biogeography, climate and soil. The digital map is now available through a web portal (http://bis.iirs.gov.in).     

Combining airborne laser scanning and Landsat data for statistical modeling of soil carbon and tree biomass in Tanzanian Miombo woodlands

Background

Soil carbon and biomass depletion can be used to identify and quantify degraded soils, and by using remote sensing, there is potential to map soil conditions over large areas. Landsat 8 Operational Land Imager satellite data and airborne laser scanning data were evaluated separately and in combination for modeling soil organic carbon, above ground tree biomass and below ground tree biomass. The test site is situated in the Liwale district in southeastern Tanzania and is dominated by Miombo woodlands. Tree data from 15 m radius field-surveyed plots and samples of soil carbon down to a depth of 30 cm were used as reference data for tree biomass and soil carbon estimations.

Results

Cross-validated plot level error (RMSE) for predicting soil organic carbon was 28% using only Landsat 8, 26% using laser only, and 23% for the combination of the two. The plot level error for above ground tree biomass was 66% when using only Landsat 8, 50% for laser and 49% for the combination of Landsat 8 and laser data. Results for below ground tree biomass were similar to above ground biomass. Additionally it was found that an early dry season satellite image was preferable for modelling biomass while images from later in the dry season were better for modelling soil carbon.

Conclusion

The results show that laser data is superior to Landsat 8 when predicting both soil carbon and biomass above and below ground in landscapes dominated by Miombo woodlands. Furthermore, the combination of laser data and Landsat data were marginally better than using laser data only.

     

黑河流域遥感物候产品验证与分析

植被物候遥感产品对全球变化响应、农业生产管理、生态学的应用等多领域研究具有重要意义。但现有植被物候遥感产品还有较多问题,主要包括一方面使用不同参数的时间序列数据以及不同提取算法导致的产品结果差异较大,另一方面在地面验证中地面观测数据与遥感反演数据的物理含义不一致导致的验证方法的系统性误差。本文以黑河流域为研究区,对比验证基于EVI(Enhanced Vegetation Index)时间序列数据提取的MLCD(MODIS global land cover dynamics product)植被遥感物候产品和基于LAI(Leaf Area Index)时间序列数据提取的UMPM(product by universal multi-life-cycle phenology monitoring method)植被遥感物候产品的有效性及精度等。同时,通过验证分析进一步评估基于EVI和LAI时间序列提取的物候特征的差异及特点,探讨由于地面观测植被物候与遥感提取植被物候的物理意义的不一致问题导致的直接验证结果偏差。结果表明:UMPM产品有效性整体高于MLCD产品,但在以草地和灌木为主的稀疏植被区,由于LAI取值精度的原因,UMPM产品存在较多缺失数据,且时空稳定性较低;基于玉米地面观测数据表明,EVI对植被开始生长的信号比LAI更加敏感,更适合提取生长起点,但植被指数易饱和,峰值起点普遍提前,基于LAI提取的峰值起点更加合理。由于地面观测的物候期在后期更加关注果实生长,遥感观测仅关注叶片的生长,遥感定义的峰值终点和生长终点与玉米的乳熟期和成熟期差异较大。     

小滦河流域复杂地表碳循环遥感综合试验

遥感综合试验对于遥感科学技术的发展起到重要作用,无论是基础研究还是遥感应用都需要试验提供支撑。从2018年开始,遥感科学国家重点实验室针对遥感自身发展和遥感面向地表圈层深化应用面临的科学问题,在滦河上游流域组织了小滦河流域复杂地表碳循环遥感综合观测试验,本文旨在介绍该试验的目标、区域、观测参数、观测方法以及对未来研究的展望,以期望为今后开展其他遥感试验及相关研究提供有益的参考和帮助。该试验采用星机地协同综合观测的方式,选择主要的在轨运行卫星数据及覆盖此流域的遥感产品作为主要数据;针对典型区域开展航空及无人机遥感试验,搭载光学传感器设备,获取典型区域水热循环、碳循环等关键参数;并同步开展地面观测试验,在典型实验区开展大气、植被和土壤关键参数的精细观测。目前已系统的开展了地面测量试验、无人机遥感试验及航空遥感试验,并同步收集了卫星遥感数据,形成了一套丰富的星—机—地配套遥感实测数据集。在试验的推动下,遥感科学国家重点实验室于2020年在试验区架设了多座综合观测塔,并配置了多种观测设备,开启了长时间序列观测任务,虚拟试验场的构建和机理生态模型的运行也在同步开展。小滦河流域复杂地表碳循环遥感综合试验利用星—机—地一体化的监测方法有效获取了地表水、能量和碳循环的关键参数,为遥感机理模型发展、反演方法检验和尺度转换研究提供了关键的基础数据。目前已用来建立遥感机理模型的综合检验平台,分析和改进传统模型和遥感产品在复杂地表的适用性,阐明流域尺度碳水耦合的物理过程。     

日光诱导叶绿素荧光遥感反演及碳循环应用进展

在植被遥感领域,遥感植被指数在过去30年极大地促进了从宏观尺度上来理解和认识地球生物圈,但是以"绿度"观测为主的植被指数仅表征植被"潜在光合作用",而不能直接量化"实际光合作用"。植被叶绿素荧光在光合作用探测上具有优势,是"实际光合作用"的直接探测方法。日光诱导叶绿素荧光(SIF)遥感是近年快速发展起来的新型遥感技术,尤其是2011年实现全球尺度卫星反演以来,在反演算法、植被监测和碳循环应用等方面发展迅速,是近10年来植被遥感领域最具突破性的研究前沿。本文阐述了现阶段(2011年以来)SIF遥感反演及其在碳循环应用方面的进展。本文首先介绍了卫星SIF遥感的发展及其反演算法现状;然后重点剖析了其在陆地生态系统总初级生产力(GPP)估算、全球碳循环监测、物候和植被胁迫监测等方面的应用现状和特点;最后从卫星SIF反演算法优化、SIF-GPP关系机理、SIF多尺度综合观测和全球碳循环监测等方面对今后植被SIF遥感的发展前景进行了展望。     

Modeling the spatial distribution of above-ground carbon in Mexican coniferous forests using remote sensing and a geostatistical approach

Forest conservation is considered an option for mitigating the effect of greenhouse gases on global climate, hence monitoring forest carbon pools at global and local levels is important. The present study explores the capability of remote-sensing variables (vegetation indices and textures derived from SPOT-5; backscattering coefficient and interferometric coherence of ALOS PALSAR images) for modeling the spatial distribution of above-ground biomass in the Environmental Conservation Zone of Mexico City. Correlation and spatial autocorrelation coefficients were used to select significant explanatory variables in fir and pine forests. The correlation for interferometric coherence in HV polarization was negative, with correlations coefficients r = −0.83 for the fir and r = −0.75 for the pine forests. Regression-kriging showed the least root mean square error among the spatial interpolation methods used, with 37.75 tC/ha for fir forests and 29.15 tC/ha for pine forests. The results showed that a hybrid geospatial method, based on interferometric coherence data and a regression-kriging interpolator, has good potential for estimating above-ground biomass carbon.