Remote Sensing Concepts and Their Applicability in REDD+ Monitoring (original) (raw)
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Many scientists and policy makers consider payment for environmental services, particularly carbon payment for forest management, a cost-effective and practical solution to climate change and unsustainable development. In recent years an attractive policy has been discussed under the United Nation Framework Convention on Climate Change (UNFCCC): Reducing Emissions from Deforestation and Forest Degradation (REDD+), sustainable management of forest, and conservation and enhancement of carbon in developing countries. This could potentially reward forest-managing communities in developing countries. One of the challenging tasks for the successful implementation of this policy is setting up reliable baseline emissions scenarios based on the historical emissions as input for business as usual projections. Forest biomass measurements, the quantification of carbon stocks, their monitoring, and the observation of these stocks over time, are very important for the development of reference scenario and estimation of carbon stock. This paper reviews a numbers of methods available for estimating forest carbon stocks and growth rates of different forest carbon pools. It also explores the limitations and challenges of these methods for use in different geographical locations, and suggests ways of improving accuracy and precision that reduce uncertainty for the successful implementation of REDD+. Furthermore, the paper assesses the role of remote sensing (RS) and geographical information system (GIS) techniques in the establishment of a long-term carbon inventory.
Accurate and reliable monitoring of biomass in tropical forest has been a challenging task because a large proportion of forest is inaccessible. For effective implementation of REDD-plus and fair benefit sharing, monitoring methodology should be based on scientifically robust estimation of sources and sinks to meet MRV requirements. Though there have been major advances in satellite remote sensing technologies in recent years, none of them have been able to overcome the saturation problem that makes it hard to detect forests with high above-ground biomass volume and assess degradation. The saturation problem in biomass estimation can be overcome by adopting airborne LiDAR, because laser pulses penetrate even through a dense multi-layered canopy and there is a strong correlation between LiDAR data and biomass. Integrating different remote sensing and field reference data provides an accurate, precise, and affordable monitoring solution for tropical forests. In this regard, a two-phase sampling scheme optimizes field data collection efforts for model calibration and assists with objective and efficient positioning of sample plots. In the second sampling phase, estimates for a set of LiDAR transects are available, and the radiometric properties of satellite imagery are applied to identify the best estimators for target variables. Such a method is proposed here. It integrates sample plots with LiDAR transects and satellite images, and it attains a relative RMSE of 25 to 35 percent in aboveground biomass already on an area of 0.5 ha. Alternative methods, such as National Forest Inventories based on permanent sample plots, optical satellite imagery, and k-NN estimation or visual inspection, attain this error level only on areas of 100 ha or even more. Such high spatial resolution is crucial for awarding REDD credits to local forest owners. Arbonaut Ltd. has developed a forest inventory process and user-friendly tools (ArboLiDAR) to estimate above-and below-ground carbon stocks. The estimation process relies on a unified Bayesian statistical methodology, making it possible to incorporate various information sources such as direct measurements, quantities interpreted from remote sensing, and the results of modelling of carbon sinks such as below-ground carbon. These estimates can also be simply updated whenever new data becomes available.
Exploring multi-scale forest above ground biomass estimation with optical remote sensing imageries
IOP Conference Series: Earth and Environmental Science, 2017
Forest shares 80% of total exchange of carbon between the atmosphere and the terrestrial ecosystem. Due to this monitoring of forest above ground biomass (as carbon can be calculated as 0.47 part of total biomass) has become very important. Forest above ground biomass as being the major portion of total forest biomass should be given a very careful consideration in its estimation. It is hoped to be useful in addressing the ongoing problems of deforestation and degradation and to gain carbon mitigation benefits through mechanisms like Reducing Emissions from Deforestation and Forest Degradation (REDD+). Many methods of above ground biomass estimation are in used ranging from use of optical remote sensing imageries of very high to very low resolution to SAR data and LIDAR. This paper describes a multi-scale approach for assessing forest above ground biomass, and ultimately carbon stocks, using very high imageries, open source medium resolution and medium resolution satellite datasets with a very limited number of field plots. We found this method is one of the most promising method for forest above ground biomass estimation with higher accuracy and low cost budget. Pilot study was conducted in Chitwan district of Nepal on the estimation of biomass using this technique. The GeoEye-1 (0.5m), Landsat (30m) and Google Earth (GE) images were used remote sensing imageries. Object-based image analysis (OBIA) classification technique was done on Geo-eye imagery for the tree crown delineation at the watershed level. After then, crown projection area (CPA) vs. biomass model was developed and validated at the watershed level. Open source GE imageries were used to calculate the CPA and biomass from virtual plots at district level. Using data mining technique, different parameters from Landsat imageries along with the virtual sample biomass were used for upscaling biomass estimation at district level. We found, this approach can considerably reduce field data requirements for estimation of biomass and carbon in comparison with inventory methods based on enumeration of all trees in a plot. The proposed methodology is very cost effective and can be replicated with limited resources and time. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
Scientific Data, 2019
The Forest Observation System -FOS (http://forest-observation-system.net/) -is an international, collaborative initiative that aims to establish a global in situ forest AGB database to support Earth Observation (EO) and to encourage investment in relevant field-based measurements and research 4 . The FOS enables access to high-quality field data by partnering with some of the most well-established teams and networks responsible for managing permanent forest plots globally. In doing so, FOS is benefiting both the RS and ecological/forestry communities while facilitating positive interactions between them.
MDPI, 2021
This paper provides a comprehensive literature review on forest aboveground biomass (AGB) estimation and mapping through high-resolution optical satellite imagery (≤5 m spatial resolution). Based on the literature review, 44 peer-reviewed journal articles were published in 15 years (2004–2019). Twenty-one studies were conducted in Asia, eight in North America and Africa, five in South America, and four in Europe. This review article gives a glance at the published methodologies for AGB prediction modeling and validation. The literature review suggested that, along with the integration of other sensors, QuickBird, WorldView-2, and IKONOS satellite images were most widely used for AGB estimations, with higher estimation accuracies. All studies were grouped into six satellite-derived independent variables, including tree crown, image textures, tree shadow fraction, canopy height, vegetation indices, and multiple variables. Using these satellite-derived independent variables, most of the studies used linear regression (41%), while 30% used linear multiple regression and 18% used non-linear (machine learning) regression, while very few (11%) studies used non-linear (multiple and exponential) regression for estimating AGB. In the context of global forest AGB estimations and monitoring, the advantages, strengths, and limitations were discussed to achieve better accuracy and transparency towards the performance-based payment mechanism of the REDD+ program. Apart from technical limitations, we realized that very few studies talked about real-time monitoring of AGB or quantifying AGB change, a dimension that needs exploration.
Land Use Policy, 2018
Reducing emissions from deforestation and forest degradation and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks in developing countries (REDD+), is still a promising mechanism of the UNFCCC for many tropical countries that would like to receive a fair financial compensation for their historical and current efforts to avoid forest conversion at the expense of more economically land uses. Brazil has a great opportunity to successfully participate in REDD+ not only because of its huge Amazon forest area (ca. 4 million km 2) but also because of its advanced forest monitoring system "PRODES". However, this opportunity could be threatened due to the current differentiated monitoring capacities of most Brazilian Amazon states, markedly in High-Forest and Low-Deforestation (HFLD) regions. This is evident in the State of Amapá, which despite its political will to support actions towards the design of its REDD+ strategy, is still struggling with key technical aspects of forest monitoring. To address this issue and to strengthen the ongoing REDD+ design process we assessed a) land use and land cover (LULC) changes for period of 23 years (1985-2008); b) estimated CO 2 emissions associated to these LULC changes; c) identified the main drivers and agents of deforestation, and d) discussed policy implications for REDD+ implementation in a HFLD area. We applied a methodology, which is capable of reducing cloud cover using temporal filters on the classified images, detecting deforestation (and forest degradation) in areas as small as 1 ha, and used the decision tree method to identify different LULC types. This methodology was able to demonstrate that forest cover in northern Amapá has remained almost untouched during the observed period of 23-years. As many other HFLD areas, this region has a great potential to receive financial benefits from the REDD+ mechanism, especially from voluntary markets that are largely interested in the conservation value of these areas. However, the use of high accuracy LULC classification approaches, with appropriate Measuring, Reporting and Verification systems should be part of the REDD+ implementation strategy of HFLD areas towards having high standards for certified carbon, and therefore improved chances to receive better prices for carbon offsets. The potential of REDD+ to be a fair and efficient mechanism will also depend on the recognition of the historical efforts to avoid deforestation in HFLD areas, mainly by Federal Governments, as an incentive for low-carbon development.
Background: The reliable monitoring, reporting and verification (MRV) of carbon emissions and removals from the forest sector is an important part of the efforts on reducing emissions from deforestation and forest degradation (REDD+). Forest-dependent local communities are engaged to contribute to MRV through community-based monitoring systems. The efficiency of such monitoring systems could be improved through the rational integration of the studies at permanent plots with the geospatial technologies. This article presents a case study of integrating community-based measurements at permanent plots at the foothills of central Nepal and biomass maps that were developed using GeoEye-1 and IKONS satellite images. Results: The use of very-high-resolution satellite-based tree cover parameters, including crown projected area (CPA), crown density and crown size classes improves salience, reliability and legitimacy of the community-based survey of 0.04% intensity at the lower cost than incr...
Remote Sensing in Forest Management
International conference KNOWLEDGE-BASED ORGANIZATION, 2019
Forest management, as a component of the management of natural protected areas, has the mission to adopt the most effective measures in relation to climate change. The forest management activity is based on management plans drawn up for a period of 10 years, a period appreciated, until recently, to be sufficient for management plans to be considered up to date. Their updating is done with the data provided in field by the rangers through direct observations and measurements, but the accuracy of these data is complemented by data obtained through modern technologies. Analyses of the distribution of forest vegetation, its composition and its evolution, both in time and in the area, and last but not least, the technical measures for implementing the most effective treatments for preserving its health, the optimal structure and biomass production is made using the latest technology, such as Free Open Source Software and GIS. Recent services provided free of charge by some remote sensing...
Improving Aboveground Forest Biomass Maps: From High-Resolution to National Scale
Remote Sensing, 2019
Forest aboveground biomass (AGB) estimation over large extents and high temporal resolution is crucial in managing Mediterranean forest ecosystems, which have been predicted to be very sensitive to climate change effects. Although many modeling procedures have been tested to assess forest AGB, most of them cover small areas and attain high accuracy in evaluations that are difficult to update and extrapolate without large uncertainties. In this study, focusing on the Region of Murcia in Spain (11,313 km2), we integrated forest AGB estimations, obtained from high-precision airborne laser scanning (ALS) data calibrated with plot-level ground-based measures and bio-geophysical spectral variables (eight different indices derived from MODIS computed at different temporal resolutions), as well as topographic factors as predictors. We used a quantile regression forest (QRF) to spatially predict biomass and the associated uncertainty. The fitted model produced a satisfactory performance (R2 ...