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Remote Sensing


 

Consulting Study 7

Review of global literature on the application of remote sensing for delineation and stratification of tropical forest ecosystems

Remotely-sensed data, appropriately calibrated to ground observations, provides the only practical option for mapping forest carbon stocks over the extensive areas that need to be examined in this study. Remote sensing allows the discrimination of forest types, forest degradation stages, the precise location and spatial extent of forest, as well as the up-scaling of ground based carbon stock assessment with reduced uncertainty.

The objective of this consultancy is to review the current state-of-the-art of the application of remote sensing for the delineation and stratification of tropical forests and the development of a remote sensing approach on concession level for the identification of HCS forests and characterisation of forest patches. The study comprises the following tasks:

 

Tasks:
  • Summarise available recent literature on the application of high and very high resolution satellite imagery for the delineation and stratification of tropical forests for biomass/carbon accounting.
    • A review of the recent scientific literature (peer-reviewed journals, conference proceedings, project reports and others) must be conducted for state-of-the-art remote sensing methods for the mapping of tropical forests.
    • A particular focus must lie on the identification of different levels of forest degradation and the identification of forest patches and forest fragmentation. In the recent past, the application of remote sensing technology for biomass and carbon accounting has strongly increased, particularly in the context of REDD.
    • The output of this task will be a summary of the recent advances on local, regional and global scale and an extensive set of references.
  • Derive a best practice approach for the identification and delineation of HCS forests and assessment of forest patches.
    • Based on the results of Task 1, a best practice approach for the delineation of HCS forests and the assessment of forest patches on the concession (local) level will be developed.
    • The approach shall define the type of remote sensing data to be used, the required pre-processing of the data, the analysis approach, the expected outcomes as well as the approach for assessing the accuracy of the results. The approach shall be applicable in Southeast Asia and Western/Central Africa.
    • The approach shall be able to accurately delineate forest types and forest biomass/carbon density strata. Furthermore, it shall allow the identification and classification of forest patches and forest fragmentation.
  • Prepare a high quality report covering Tasks 1 und 2 above and guideline for a best practice approach. This report will be made publicly available.
    • The report will summarise the current state-of-the-art of the application of remote sensing for the delineation and stratification of tropical forest ecosystem by forest biomass/carbon density. The synthesis of the summary will lead to a guideline for a best-practice remote sensing approach for the application at concession level. This guideline shall be written in such a way that a methodology for the delineation and stratification of HCS forests and the assessment of forest patches can be derived.
    • The results of the demonstration study will be shown and the applicability of the remote sensing approach will be summarised.
    • The consultant will be expected to contribute to teamwork involving the TC and other selected consultants, including attending a project ‘Synthesis’ meeting in April 2015.
  • Expected outputs
    • High quality report covering the outputs of Tasks 1 and 2.

Consulting Study 8

Review of global literature on the use of LiDAR technology for the assessment of aboveground biomass and carbon stocks at different spatial scales

Remotely-sensed data, appropriately calibrated to ground observations, provides the only practical option for mapping forest carbon stocks over the extensive areas that need to be examined in this study. Remote sensing allows the discrimination of forest types, forest degradation stages, the precise location and spatial extent of forest, as well as the up-scaling of ground based carbon stock assessment with reduced uncertainty.

The objective of this consultancy is to review the current state-of-the-art of the application of LiDAR technology for the assessment of aboveground biomass and carbon stocks on different spatial scales. The study comprises the following tasks:

Tasks:
  • Summarise available recent literature on the application of LiDAR technology for assessing and modelling of aboveground biomass of tropical forests.
    • A review of the recent scientific literature (peer-reviewed journals, conference proceedings, project reports and others) must be conducted for state-of-the-art LiDAR approaches to quantify tropical forest aboveground biomass.
    • In the recent past, the application of LiDAR technology to assess aboveground biomass of has strongly increased, particularly for tropical forests. A special focus must lie on assessing biomass of for the identification of HCS forests and the possibility to detect aboveground biomass variability due to different ecological growing conditions and levels of forest degradation.
    • The output of this task will be a summary of the recent advances in LiDAR technology to assess tropical forest biomass and carbon stock on local, regional and global scale and an extensive set of references.
  • If possible, derive default technical parameters for airborne LiDAR data acquisition and forest inventory design for the assessment of forest carbon/aboveground biomass.
    • Based on Task 1, if possible, identify the most suitable default technical parameters for airborne LiDAR data acquisition and forest inventory design with regard to assessing and modelling aboveground biomass of tropical forests.
    • The selection of the most suitable technical LiDAR parameters and forest inventory design should particular focus on assessing aboveground biomass of HCS forests and the detection of aboveground biomass variability due to different ecological growing conditions and levels of forest degradation.
    • The output of the task will be an up to date summary of the most suitable technical parameters for airborne LiDAR data acquisition and forest inventory design to assess and model tropical forests’ aboveground biomass on a local, regional and global scale.
  • Derive a best practice approach for the modelling of aboveground biomass by LiDAR.
    • Based on the results of tasks 1 and 2, a best practice approach for modelling aboveground biomass of tropical forests by LiDAR will be developed.
    • The approach shall define the type of technical LiDAR parameters to be used, the required pre-processing and processing of the LiDAR data, the most suitable forest inventory design, the spatial applicability (local, regional or global), the analysis approach, the expected outcomes as well as the accuracy assessment of the results. The developed approach should focus on the biomass assessment HCS forests and the detection of biomass variability due to different ecological growing conditions and levels of forest degradation. The approach shall be applicable in Southeast Asia and Western/Central Africa.
    • The approach should be able to estimate aboveground biomass of tropical forests with high carbon stocks and detect aboveground biomass variability due to different ecological growing conditions and levels of forest degradation.
  • Prepare a high quality report covering Tasks 1, 2 and 3 above and guideline for a best practice approach. This report will be made publicly available.
    • The report will summarise the current state-of-the-art of the application of LiDAR technology for the assessment of aboveground biomass and carbon stocks on different spatial scales (local, regional and global).
    • The synthesis of the summary will lead to a set of default technical parameters for airborne LiDAR data and forest inventory design and a guideline for a best-practice remote sensing approach for the modelling of aboveground biomass by airborne LiDAR. This guideline shall be written in such a way that a methodology for the quantification of aboveground carbon stocks for the identification of HCS forests can be derived.
    • Eventually, the results of the demonstration study will be shown and the applicability of the remote sensing approach will be summarised.
    • The consultant will be expected to contribute to teamwork involving the TC and other selected consultants, including attending a project ‘Synthesis’ meeting in April 2015.
  • Expected outputs
    • High quality report covering the outputs of Tasks 1, 2 and 3.

Consulting Study 9 (closely linked to Biomass tasks)

Synthesis of the state of the art of aboveground biomass estimation using remote sensing

Quantification of tropical forest aboveground biomass over large areas is challenging. The biotic and structural complexity of tropical ecosystems make forest inventories difficult, time consuming and expensive. The most accurate method of aboveground biomass estimation is based on forest inventories with destructive and non-destructive sampling. Aboveground biomass is then calculated through allometric equations. Although this approach provides quite reasonable aboveground biomass estimations it is likely that the generic the models currently applied may not properly reflect the diversity and complexity of tropical rainforest ecosystems and its various stages of degradation. Further, there is considerable uncertainty about the spatial variability of aboveground biomass within a specific forest type and the impact of logging and fire. Aboveground biomass can also be estimated by remote sensing technologies, but no such instrument can measure aboveground biomass directly; therefore in situ data collection is always necessary.

The objective of this consultancy is to review the current state-of-the-art of up-scaling aboveground biomass estimates from forest inventory data by remote sensing data to different spatial scales (local, regional and global). The study comprises the following tasks:

Tasks:
  • Review scientific literature on the carbon assessment by remote sensing with special focus of available approaches for up-scaling inventory data from local to regional to national scale.
    • A review of the recent scientific literature (peer-reviewed journals, conference proceedings, project reports and others) must be conducted for state-of-the-art aboveground biomass estimates of tropical forest (focus on Southeast Asia and Western/Central Africa) based on remote sensing data. Special focus should be on available approaches for up-scaling aboveground biomass estimates from forest inventory data by remote sensing data to different spatial scales (local, regional equals to 10 times the size of a concession area, e.g. 500.000 ha and national). Another focus should be the aboveground biomass estimation of HCS forests by remote sensing data (e.g. RADAR and saturation problem of forest stands with high aboveground biomass values). Another question that should be discussed is the influence of the forest inventory design on the up-scaled biomass estimates.
    • The output of this task will be a summary of the recent advances in remote sensing considering aboveground biomass estimates (especially of HCS forests), the up-scaling of biomass estimates from forest inventory data by remote sensing to different spatial scales (local, regional and national) and an extensive set of references.
  • Develop an approach for up-scaling aboveground biomass with particular emphasis on accuracies and uncertainties in order to identify HCS forests.
    • Based on Task 1, a practical approach shall be developed for upscaling aboveground biomass data from local to regional level by remote sensing methods. A particular focus shall be on the assessment of the accuracies and uncertainties involved in the up-scaling process and the effect for the identification of HCS forests. The analysis should also include a cost-benefit analysis.
  • Prepare a high quality report covering Tasks 1 and 2 above and guideline for a best practice approach. This report will be made publicly available.
    • The report will summarise the current state-of-the-art aboveground biomass estimates of tropical forests (focus on Southeast Asia and Western/Central Africa) based on remote sensing data. A special focus will be on available approaches for up-scaling aboveground biomass estimates from forest inventory data by remote sensing data to different spatial scales (local, regional and global). Further the uncertainties in these different approaches will be summarised.
    • The synthesis of the summary will lead to a guideline for a best practice approach in assessing aboveground biomass of tropical forest from remote sensing data (focus on HCS forests) and up-scaling forest biomass estimates from forest inventories via remote sensing.
    • The consultant will be expected to contribute to teamwork involving the TC and other selected consultants, including attending a project ‘Synthesis’ meeting in April 2015.
  • Expected outputs
    • High quality report covering the outputs of Tasks 1 and 2.
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