Soil properties and neighbouring forest cover affect above-ground biomass and functional composition during tropical forest restoration

2018 ◽  
Vol 21 (2) ◽  
pp. 179-189 ◽  
Author(s):  
Renato Miazaki Toledo ◽  
Rozely Ferreira Santos ◽  
Lander Baeten ◽  
Michael P. Perring ◽  
Kris Verheyen
Keyword(s):  
Tropical Forest ◽  
Soil Properties ◽  
Forest Restoration ◽  
Forest Cover ◽  
Functional Composition ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Tropical Forest Restoration

scholarly journals Approaches of Satellite Remote Sensing for the Assessment of Above-Ground Biomass across Tropical Forests: Pan-tropical to National Scales

2020 ◽  
Vol 12 (20) ◽  
pp. 3351
Author(s):  
Sawaid Abbas ◽  
Man Sing Wong ◽  
Jin Wu ◽  
Naeem Shahzad ◽  
Syed Muhammad Irteza
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Carbon Sequestration ◽  
Tropical Forest ◽  
Tropical Forests ◽  
Satellite Remote Sensing ◽  
Forest Cover ◽  
Above Ground Biomass ◽  
Forest Cover Change ◽  
Ground Biomass

Tropical forests are acknowledged for providing important ecosystem services and are renowned as “the lungs of the planet Earth” due to their role in the exchange of gasses—particularly inhaling CO2 and breathing out O2—within the atmosphere. Overall, the forests provide 50% of the total plant biomass of the Earth, which accounts for 450–650 PgC globally. Understanding and accurate estimates of tropical forest biomass stocks are imperative in ascertaining the contribution of the tropical forests in global carbon dynamics. This article provides a review of remote-sensing-based approaches for the assessment of above-ground biomass (AGB) across the tropical forests (global to national scales), summarizes the current estimate of pan-tropical AGB, and discusses major advancements in remote-sensing-based approaches for AGB mapping. The review is based on the journal papers, books and internet resources during the 1980s to 2020. Over the past 10 years, a myriad of research has been carried out to develop methods of estimating AGB by integrating different remote sensing datasets at varying spatial scales. Relationships of biomass with canopy height and other structural attributes have developed a new paradigm of pan-tropical or global AGB estimation from space-borne satellite remote sensing. Uncertainties in mapping tropical forest cover and/or forest cover change are related to spatial resolution; definition adapted for ‘forest’ classification; the frequency of available images; cloud covers; time steps used to map forest cover change and post-deforestation land cover land use (LCLU)-type mapping. The integration of products derived from recent Synthetic Aperture Radar (SAR) and Light Detection and Ranging (LiDAR) satellite missions with conventional optical satellite images has strong potential to overcome most of these uncertainties for recent or future biomass estimates. However, it will remain a challenging task to map reference biomass stock in the 1980s and 1990s and consequently to accurately quantify the loss or gain in forest cover over the periods. Aside from these limitations, the estimation of biomass and carbon balance can be enhanced by taking account of post-deforestation forest recovery and LCLU type; land-use history; diversity of forest being recovered; variations in physical attributes of plants (e.g., tree height; diameter; and canopy spread); environmental constraints; abundance and mortalities of trees; and the age of secondary forests. New methods should consider peak carbon sink time while developing carbon sequestration models for intact or old-growth tropical forests as well as the carbon sequestration capacity of recovering forest with varying levels of floristic diversity.

scholarly journals Local tropical forest restoration strategies affect tree recruitment more strongly than does landscape forest cover

2016 ◽  
Vol 54 (4) ◽  
pp. 1091-1099 ◽  
Author(s):  
Karen D. Holl ◽  
John Leighton Reid ◽  
José Miguel Chaves-Fallas ◽  
Federico Oviedo-Brenes ◽  
Rakan A. Zahawi
Keyword(s):  
Tropical Forest ◽  
Forest Restoration ◽  
Forest Cover ◽  
Tree Recruitment ◽  
Restoration Strategies ◽  
Tropical Forest Restoration

scholarly journals Degree of intervention affects interannual and within‐plot heterogeneity of seed arrival in tropical forest restoration

2021 ◽  
Author(s):  
Leland K. Werden ◽  
Karen D. Holl ◽  
Jose Miguel Chaves‐Fallas ◽  
Federico Oviedo‐Brenes ◽  
Juan Abel Rosales ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Forest Restoration ◽  
Tropical Forest Restoration

Accelerating tropical forest restoration through the selective removal of pioneer species

2016 ◽  
Vol 381 ◽  
pp. 209-216 ◽  
Author(s):  
Tom Swinfield ◽  
Roki Afriandi ◽  
Ferry Antoni ◽  
Rhett D. Harrison
Keyword(s):  
Tropical Forest ◽  
Forest Restoration ◽  
Selective Removal ◽  
Pioneer Species ◽  
Tropical Forest Restoration

scholarly journals Practitioner views on the determinants of tropical forest restoration longevity

2021 ◽  
Author(s):  
Lauren Nerfa ◽  
Sarah Jane Wilson ◽  
J. Leighton Reid ◽  
Jeanine M. Rhemtulla
Keyword(s):  
Tropical Forest ◽  
Forest Restoration ◽  
Tropical Forest Restoration

scholarly journals Vegetation cover and carbon pool loss assessment due to extreme weather induced disaster in Mandakini valley, Western Himalaya

2020 ◽  
Vol 21 (1&2) ◽  
pp. 49-62
Author(s):  
Yogesh Kumar ◽  
Sanjay Babu ◽  
Sarnam Singh
Keyword(s):  
Forest Cover ◽  
Western Himalaya ◽  
Flash Floods ◽  
Extreme Weather ◽  
Carbon Pool ◽  
Coefficient Of Determination ◽  
Above Ground Biomass ◽  
Loss Assessment ◽  
Subtropical Forests ◽  
Ground Biomass

Sendai Framework for 2015-2030 emphasises on the damage and loss assessment needs and its ecosystem level impacts. We have assessed the loss of forest cover and phytomass/carbon pool in the natural forest ecosystems lost due to extreme weather conditions leading flash floods and landslides during Kedarnath tragedy on June 17, 2013 in Mandakini Valley, Uttarakhand in Western Himalaya. We used high resolution satellite IRS LISS IV (5.8 m spatial resolution) of pre-disaster (2012) & post-disaster (2013). Since lost vegetation cannot be ground inventoried, a new approach was developed wherein we used pre-disaster spectral characteristics to identify  sample locations in nearby and adjacent to affected areas. We laid 45 geotagged sample plots in May 2014 on both side of the 37 landslide affected areas within a distance of 2 km from river-bed for primary data collection. Above ground biomass and Carbon was estimated using standard protocols and used species-specific volumetric equations and wood density. Above ground biomass varied from 18.05t/ha in Alpine Scrub to 252.95 t/ha in Subtropical forests. Assuming that the biomass increment and spectral properties would not change significantly, we applied several vegetation indices to get best regression model with biomass.  We found NDVI (2014) with coefficient of determination (R2) of 0.893, SE± 0.038 with linear function as the best for geospatial modelling of the biomass for pre-flood 2013 and post-flood 2014 situations. Coefficient of determination (R²) between estimated vis-à-vis modelled biomass was 0.8643. It is found that there is a net loss of 52,055.80 tonnes of forest biomass and 24,466.14 tonnes of carbon due to landslides and flash floods. The maximum biomass/carbon was lost in the sub-tropical forests. The loss of forest cover was maximum in subtropical forests.

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