The effectiveness of lidar remote sensing for monitoring forest cover attributes and landscape restoration

Abstract Ambitious pledges to restore over 400 million hectares of degraded lands by 2030 have been made by several countries within the Global Partnership for Forest Landscape Restoration (FLR). Monitoring restoration outcomes at this scale requires cost-effective methods to quantify not only forest cover, but also forest structure and the diversity of useful species. Here we obtain and analyze structural attributes of forest canopies undergoing restoration in the Atlantic Forest of Brazil using a portable ground lidar remote sensing device as a proxy for airborne laser scanners. We assess the ability of these attributes to distinguish forest cover types, to estimate aboveground dry woody biomass (AGB) and to estimate tree species diversity (Shannon index and richness). A set of six canopy structure attributes were able to classify five cover types with an overall accuracy of 75%, increasing to 87% when combining two secondary forest classes. Canopy height and the unprecedented “leaf area height volume” (a cumulative product of canopy height and vegetation density) were good predictors of AGB. An index based on the height and evenness of the leaf area density profile was weakly related to the Shannon Index of tree species diversity and showed no relationship to species richness or to change in species composition. These findings illustrate the potential and limitations of lidar remote sensing for monitoring compliance of FLR goals of landscape multifunctionality, beyond a simple assessment of forest cover gain and loss.

[1]  Erle C. Ellis,et al.  Using lightweight unmanned aerial vehicles to monitor tropical forest recovery , 2015 .

[2]  Pete Smith,et al.  Natural climate solutions , 2017, Proceedings of the National Academy of Sciences.

[3]  Petteri Packalen,et al.  Gini coefficient predictions from airborne lidar remote sensing display the effect of management intensity on forest structure , 2016 .

[4]  M. Keller,et al.  Aboveground biomass variability across intact and degraded forests in the Brazilian Amazon , 2016 .

[5]  M. Keller,et al.  Landscape‐scale lidar analysis of aboveground biomass distribution in secondary Brazilian Atlantic Forest , 2018 .

[6]  L. Rodriguez,et al.  Characterization of Brazilian forest types utilizing canopy height profiles derived from airborne laser scanning , 2016 .

[7]  R. Valbuena,et al.  Diversity and equitability ordering profiles applied to study forest structure , 2012 .

[8]  Marcos Longo,et al.  Linking canopy leaf area and light environments with tree size distributions to explain Amazon forest demography. , 2015, Ecology letters.

[9]  M. Keller,et al.  Airborne lidar-based estimates of tropical forest structure in complex terrain: opportunities and trade-offs for REDD+ , 2015, Carbon Balance and Management.

[10]  G. Bohrer,et al.  Maintaining high rates of carbon storage in old forests: A mechanism linking canopy structure to forest function , 2013 .

[11]  Robin L. Chazdon,et al.  Beyond hectares: four principles to guide reforestation in the context of tropical forest and landscape restoration , 2017 .

[12]  G. Asner,et al.  Scale-dependence of aboveground carbon accumulation in secondary forests of Panama: A test of the intermediate peak hypothesis , 2012 .

[13]  G. Sánchez‐Azofeifa,et al.  Estimation of aboveground net primary productivity in secondary tropical dry forests using the Carnegie–Ames–Stanford approach (CASA) model , 2016 .

[14]  Christian Messier,et al.  Spatial complementarity in tree crowns explains overyielding in species mixtures , 2017, Nature Ecology &Evolution.

[15]  P. Brancalion,et al.  High diversity mixed plantations of Eucalyptus and native trees: An interface between production and restoration for the tropics , 2018 .

[16]  Karen D. Holl,et al.  A global review of past land use, climate, and active vs. passive restoration effects on forest recovery , 2017, PloS one.

[17]  Yosio Edemir Shimabukuro,et al.  Amazon forest carbon dynamics predicted by profiles of canopy leaf area and light environment. , 2012, Ecology letters.

[18]  James H. Brown,et al.  A General Model for the Origin of Allometric Scaling Laws in Biology , 1997, Science.

[19]  Lars Laestadius,et al.  When is a forest a forest? Forest concepts and definitions in the era of forest and landscape restoration , 2016, Ambio.

[20]  W. Cohen,et al.  Lidar Remote Sensing for Ecosystem Studies , 2002 .

[21]  David J. Harding,et al.  A portable LIDAR system for rapid determination of forest canopy structure , 2004 .

[22]  Carlos Alberto Silva,et al.  Optimizing the Remote Detection of Tropical Rainforest Structure with Airborne Lidar: Leaf Area Profile Sensitivity to Pulse Density and Spatial Sampling , 2019, Remote. Sens..

[23]  J. Gamon,et al.  Integrating proximal broad-band vegetation indices and carbon fluxes to model gross primary productivity in a tropical dry forest , 2018, Environmental Research Letters.

[24]  M. Fladeland,et al.  Remote sensing for biodiversity science and conservation , 2003 .

[25]  B. Nelson,et al.  Improved allometric models to estimate the aboveground biomass of tropical trees , 2014, Global change biology.

[26]  Inderjit,et al.  Inhibitory effects of Eucalyptus globulus on understorey plant growth and species richness are greater in non‐native regions , 2017, Global Ecology and Biogeography.

[27]  P. Brancalion,et al.  Participatory monitoring to connect local and global priorities for forest restoration , 2018, Conservation biology : the journal of the Society for Conservation Biology.

[28]  R. Macarthur,et al.  Foliage Profile by Vertical Measurements , 1969 .

[29]  P. Brancalion,et al.  Early ecological outcomes of natural regeneration and tree plantations for restoring agricultural landscapes. , 2018, Ecological applications : a publication of the Ecological Society of America.

[30]  R. Valbuena,et al.  Characterizing forest structural types and shelterwood dynamics from Lorenz-based indicators predicted by airborne laser scanning , 2013 .

[31]  M. Loreau,et al.  Tropical tree diversity enhances light capture through crown plasticity and spatial and temporal niche differences , 2014 .

[32]  G. Bohrer,et al.  The role of canopy structural complexity in wood net primary production of a maturing northern deciduous forest. , 2011, Ecology.

[33]  Karen D. Holl,et al.  Restoring tropical forests from the bottom up , 2017, Science.

[34]  David A. Coomes,et al.  Applications of airborne lidar for the assessment of animal species diversity , 2014 .

[35]  J. Stape,et al.  Atlantic forest tree species responses to silvicultural practices in a degraded pasture restoration plantation: From leaf physiology to survival and initial growth , 2014 .

[36]  R. Macarthur,et al.  On Bird Species Diversity , 1961 .

[37]  K. Holl,et al.  Handbook of Ecological Restoration: Monitoring and appraisal , 2002 .

[38]  R. Kasten Dumroese,et al.  Contemporary forest restoration: A review emphasizing function , 2014 .

[39]  J. Stape,et al.  Silvicultural opportunities for increasing carbon stock in restoration of Atlantic forests in Brazil , 2015 .

[40]  J. Stape,et al.  Köppen's climate classification map for Brazil , 2013 .

[41]  B. Schröder,et al.  Identifying suitable multifunctional restoration areas for Forest Landscape Restoration in Central Chile , 2017 .

[42]  Michel G.J. den Elzen,et al.  The key role of forests in meeting climate targets requires science for credible mitigation , 2017 .

[43]  G. Asner,et al.  Mapping tropical forest carbon: Calibrating plot estimates to a simple LiDAR metric , 2014 .

[44]  R. Chazdon.  Second growth : the promise of tropical forest regeneration in an age of deforestation , 2014 .

[45]  James Aronson,et al.  On the need of legal frameworks for assessing restoration projects success: new perspectives from São Paulo state (Brazil) , 2015 .

[46]  D. R. Almeida,et al.  Contrasting fire damage and fire susceptibility between seasonally flooded forest and upland forest in the Central Amazon using portable profiling LiDAR , 2016 .

[47]  R. Valbuena,et al.  Classification of multilayered forest development classes from low-density national airborne lidar datasets , 2016 .

[48]  M. Mcdonnell.  Old field vegetation height and the dispersal pattern of bird-disseminated woody plants , 1986 .

[49]  J. B. Ruhl,et al.  Committing to ecological restoration , 2015, Science.

[50]  P. Brancalion,et al.  Protocol for Monitoring Tropical Forest Restoration , 2017 .

[51]  G. Henebry,et al.  Remote sensing of vegetation 3-D structure for biodiversity and habitat: Review and implications for lidar and radar spaceborne missions , 2009 .