Aboveground Forest Biomass Estimation with Landsat and LiDAR Data and Uncertainty Analysis of the Estimates

Landsat Thematic mapper (TM) image has long been the dominate data source, and recently LiDAR has offered an important new structural data stream for forest biomass estimations. On the other hand, forest biomass uncertainty analysis research has only recently obtained sufficient attention due to the difficulty in collecting reference data. This paper provides a brief overview of current forest biomass estimation methods using both TM and LiDAR data. A case study is then presented that demonstrates the forest biomass estimation methods and uncertainty analysis. Results indicate that Landsat TM data can provide adequate biomass estimates for secondary succession but are not suitable for mature forest biomass estimates due to data saturation problems. LiDAR can overcome TM’s shortcoming providing better biomass estimation performance but has not been extensively applied in practice due to data availability constraints. The uncertainty analysis indicates that various sources affect the performance of forest biomass/carbon estimation. With that said, the clear dominate sources of uncertainty are the variation of input sample plot data and data saturation problem related to optical sensors. A possible solution to increasing the confidence in forest biomass estimates is to integrate the strengths of multisensor data

Historical forest biomass dynamics modelled with Landsat spectral trajectories

Acknowledgements National Forest Inventory data are available online, provided by Ministerio de Agricultura, Alimentación y Medio Ambiente (España). Landsat images are available online, provided by the USGS.Peer reviewedPostprin

Aboveground forest biomass estimation with Landsat and LiDAR data and uncertainty analysis of the estimates.

Landsat Thematic mapper (TM) image has long been the dominate data source, and recently LiDAR has offered an important new structural data stream for forest biomass estimations. On the other hand, forest biomass uncertainty analysis research has only recently obtained sufficient attention due to the difficulty in collecting reference data. This paper provides a brief overview of current forest biomass estimation methods using both TM and LiDAR data. A case study is then presented that demonstrates the forest biomass estimation methods and uncertainty analysis. Results indicate that Landsat TM data can provide adequate biomass estimates for secondary succession but are not suitable for mature forest biomass estimates due to data saturation problems. LiDAR can overcome TM?s shortcoming providing better biomass estimation performance but has not been extensively applied in practice due to data availability constraints. The uncertainty analysis indicates that various sources affect the performance of forest biomass/carbon estimation. With that said, the clear dominate sources of uncertainty are the variation of input sample plot data and data saturation problem related to optical sensors. A possible solution to increasing the confidence in forest biomass estimates is to integrate the strengths of multisensor data

Characterizing forest biomass and the impacts of bark beetles and forest management in the southern Rocky Mountains, USA

Includes bibliographical references.2020 Summer.To view the abstract, please see the full text of the document

Optical remote sensing for biomass estimation in the tropics: the case study of Uganda

This study investigates the capabilities and limitations of freely available optical satellite data at medium resolution to estimate aboveground biomass density of vegetation at national scales in the tropics, and compares this approach with existing methodologies to understand and quantify the sources of variability in the estimations. Uganda was chosen as a case-study because it presents a reliable national biomass reference dataset. As a result of this thesis, aboveground woody biomass for the year circa-2000 was mapped at national scale in Uganda at 30-m spatial resolution on the basis of Landsat ETM+ images, a national land cover dataset and field data using an object-oriented approach. A regression tree-based model (Random Forest) produced good results (cross-validated R² 0.81, RMSE 13 Mg/ha) when trained with a sufficient number of field plots representative of the vegetation variability. This study demonstrated that in certain contexts Landsat data can effectively spatialize field biomass measurements and produce accurate and detailed estimates of biomass distribution at national scale. This approach tended to provide conservative biomass estimates and its limitations were mainly related to the saturation of the optical signal at high biomass density and to the cloud cover. When compared with the Uganda national biomass dataset, the map produced in this study presented higher agreement than other five regional/global biomass maps. The comparative analysis showed strong disagreement between the products, with estimates of total biomass of Uganda ranging from 343 to 2201 Tg and different spatial distribution patterns. Maps based on biome-average biomass values, such as the Intergovernmental Panel on Climate Change default values, and global land cover datasets strongly overestimated biomass stocks, while maps based on satellite data provided conservative estimates. The comparison of the maps predictions with field data confirmed the above findings

FOREST CARBON MAPPING AND SPATIAL UNCERTAINTY ANALYSIS: COMBINING NATIONAL FOREST INVENTORY DATA AND LANDSAT TM IMAGES

Being able to accurately map forest carbon is a critical step in the global carbon cycle modeling and management process. This project is aimed at enhancing the current methodologies used for forest carbon mapping, and applying a method to account for any errors produced. By doing so, more accurate decisions can be made based on the knowledge gained from forest carbon maps; such as policy decisions on how to manage forests, or how to mitigate climate change. The use of remotely sensed images, in combination with Forest Inventory and Analysis (FIA) data, is one such way of doing this. This study compared three different methods; including linear regression, cosimulation, and up-scaled cosimulation to interpolate forest carbon based on a defined relationship between sample plots of national FIA data and satellite images. An uncertainty analysis was completed in an effort to quantify, and separate the different sources of error produced within a cosimulation mapping effort. The results indicated that the band ratio of TM4 / TM5 + TM4 / TM7 had the highest correlation coefficient, around 0.56, with the FIA forest carbon values. At a resolution of 90 m ×by 90 m, co-simulation predicted carbon values from about 14 Mg/ha, to 135 Mg/ha. The regression model, at the same resolution, estimated carbon values from about -17 Mg/ha, to 2,400 Mg/ha. Up-scaled cosimulation at a resolution of 990 m x× 990 m, predicted carbon values of ranging from 16 Mg/ha, to 133 Mg/ha. The uncertainty analysis was unable to produce any statistically significant results, with all R2 values below 0.1. These results showed that using a linear regression produced some impossible estimates, while cosimulation led to more realistic values. However, no conclusion can be made when comparing the methods based on the map validation techniques used. Although limited validation of the results was conducted, using both the FIA data and some independent sampling data; further work that focuses on validation is recommended

Tropical Forest Canopy Height and Aboveground Biomass Estimation Using Airborne Lidar and Landsat-8 Data, a Sensitivity Study with Respect to Landsat-8 Data Temporal Availability, in Mai Ndombe Province, Democratic Republic of Congo

Tropical forests’ structure information, such as forest canopy height, is a key component in any estimate of carbon stock. Tropical rainforests constitute the most forested ecosystems that harbor the largest biodiversity on Earth and store more carbon (above and belowground biomass) than any other ecosystem in the world. However, estimates of forest canopy structure is lacking over most of the regions that host this ecosystem because of both the structure’s complexity of this ecosystems and the incomplete or lack of up-to-date national forest inventory data necessary to derive forest canopy height and aboveground biomass. This study explores the capability of Landsat-8 imagery to predict dominant forest canopy height and aboveground biomass in Mai Ndombe province, Democratic Republic of Congo – a country that host half of the Congo Basin forests – within the context of the temporal availability of Landsat-8 imagery. A random forest regression model was used to predict dominant forest canopy height at 30 m spatial resolution from (a) only the July 14th 2013 (dry season) Landsat-8 image, (b) only the December 8th 2014 (wet season) Landsat-8 image, and (c) both images. The accuracy of the random forest regression model was performed on test data (n=2639) resulting in a, for the best prediction when using both dates together, RMSE = 3.84 m, R2 = 0.47. The model was then applied to the study area to derive forest canopy height using predictor variables from (a) only the dry season, (b) only the wet season, and (c) both images. The allometry equation defined by Xu et al. (2017) was used to generate aboveground biomass maps from (a) only the July 14th 2013 (dry season) Landsat-8 image, (b) only the December 8th 2014 (wet season) Landsat-8 image, and (c) both images using the study area forest canopy height maps. Field plots of aboveground biomass measurements were compared to predicted aboveground biomass maps for validation purpose. Validation process revealed a better prediction of aboveground biomass (RMSE= 83.77 Mg.ha-1) when the forest canopy height maps derived with both images was used to estimate aboveground biomass

Remote Sensing of Aboveground Biomass in Tropical Secondary Forests: A Review

Tropical landscapes are, in general, a mosaic of pasture, agriculture, and forest undergoing various stages of succession. Forest succession is comprised of continuous structural changes over time and results in increases in aboveground biomass (AGB). New remote sensing methods, including sensors, image processing, statistical methods, and uncertainty evaluations, are constantly being developed to estimate biophysical forest changes. We review 318 peer-reviewed studies related to the use of remotely sensed AGB estimations in tropical forest succession studies and summarize their geographic distribution, sensors and methods used, and their most frequent ecological inferences. Remotely sensed AGB is broadly used in forest management studies, conservation status evaluations, carbon source and sink investigations, and for studies of the relationships between environmental conditions and forest structure. Uncertainties in AGB estimations were found to be heterogeneous with biases related to sensor type, processing methodology, ground truthing availability, and forest characteristics. Remotely sensed AGB of successional forests is more reliable for the study of spatial patterns of forest succession and over large time scales than that of individual stands. Remote sensing of temporal patterns in biomass requires further study, in particular, as it is critical for understanding forest regrowth at scales useful for regional or global analyses

Analysis of Land Use/Land Cover Change Impacts Upon Ecosystem Services in Montane Tropical Forest of Rwanda: Forest Carbon Assessment and REDD+ Preparedness

Changes in forest cover especially changes within tropical forests, affect global climate change, together with ecosystems and forest carbon. Forests play a key role in both carbon emission and carbon sequestration. Efforts to reduce emissions through reduced deforestation and degradation of forests have become a common discussion among scientists and politicians under the auspices of the United Nations Programme on Reducing Emissions from Deforestation and Forest Degradation (UN-REDD Programme). This dissertation research assessed the impacts of land use land cover change upon ecosystem services from a protected area focusing on forest carbon distribution and vegetation mapping using remote sensing and geographical information systems (GIS). I also assessed Rwanda’s preparedness in the United Nations global program, Reducing Emissions from Deforestation and forest Degradation, Measuring, Monitoring, Reporting, and Verifying (REDD+MMRV). I carried out research in Nyungwe National Park (NNP), one of four National Parks of Rwanda. NNP is a montane tropical forest located in the Albertine Rift, one of the most biodiverse places in central and east Africa. I used remote sensing and field data collection from December 2011 and July 2012 in the western part of the Park to assess distribution and quantities of aboveground (ABG) forest carbon using generalized allometric functions. Using Landsat data together with 2009 high resolution color orthophotos and groundtruthing, I analyzed land cover changes between 1986 and 2011 for NNP. The land-use land cover change analysis showed that between 1986 and 1995 there was a minor increase in forest cover from 53% to 58% while from 1995-2003 a substantial decrease in forest cover occurred. Between 2003 and 2011 was a period of recovery with forest cover increasing by 59%. Vegetation analysis based on a 2009 Park biodiversity survey yielded 13 vegetation communities based on dominant and co-dominant species. Macaranga kilimandscharica was found to be dominant in three communities, representing 42% of the Park, and co-dominant in one community, representing 7% of the Park. While ~50% of the Park is secondary forest, the change in protection status has had a positive impact upon forest cover change within the Park. . Assessment of REDD+-MMRV readiness revealed that Rwanda has higher capacity and readiness in remote sensing and GIS than in forest inventory and carbon pools inventory. Lack of data to support development of emission models is a major problem at the national level which needs to be addressed