Remote Sensing Technology Applications in Forestry and REDD+

Advances in close-range and remote sensing technologies drive innovations in forest resource assessments and monitoring at varying scales. Data acquired with airborne and spaceborne platforms provide us with higher spatial resolution, more frequent coverage and increased spectral information. Recent developments in ground-based sensors have advanced three dimensional (3D) measurements, low-cost permanent systems and community-based monitoring of forests. The REDD+ mechanism has moved the remote sensing community in advancing and developing forest geospatial products which can be used by countries for the international reporting and national forest monitoring. However, there still is an urgent need to better understand the options and limitations of remote and close-range sensing techniques in the field of degradation and forest change assessment. This Special Issue contains 12 studies that provided insight into new advances in the field of remote sensing for forest management and REDD+. This includes developments into algorithm development using satellite data; synthetic aperture radar (SAR); airborne and terrestrial LiDAR; as well as forest reference emissions level (FREL) frameworks.

[1]  D. Spracklen,et al.  Identifying European Old-Growth Forests using Remote Sensing: A Study in the Ukrainian Carpathians , 2019, Forests.

[2]  Mingyang Li,et al.  Improving Forest Aboveground Biomass (AGB) Estimation by Incorporating Crown Density and Using Landsat 8 OLI Images of a Subtropical Forest in Western Hunan in Central China , 2019, Forests.

[3]  F. Kraxner,et al.  Determining a Carbon Reference Level for a High-Forest-Low-Deforestation Country , 2019 .

[4]  Corinne Le Quéré,et al.  Trends in the sources and sinks of carbon dioxide , 2009 .

[5]  Haibo Zhang,et al.  Forest Growing Stock Volume Estimation in Subtropical Mountain Areas Using PALSAR-2 L-Band PolSAR Data , 2019, Forests.

[6]  R. B. Jackson,et al.  A Large and Persistent Carbon Sink in the World’s Forests , 2011, Science.

[7]  H. Pourghasemi,et al.  Testing a New Ensemble Model Based on SVM and Random Forest in Forest Fire Susceptibility Assessment and Its Mapping in Serbia’s Tara National Park , 2019, Forests.

[8]  Matthew N. House,et al.  Landsat 8 Based Leaf Area Index Estimation in Loblolly Pine Plantations , 2019, Forests.

[9]  Jun Zhao,et al.  Estimating Forest Canopy Cover in Black Locust (Robinia pseudoacacia L.) Plantations on the Loess Plateau Using Random Forest , 2018, Forests.

[10]  Jian Liu,et al.  Estimation of Pinus massoniana Leaf Area USING Terrestrial Laser Scanning , 2019, Forests.

[11]  M. Herold,et al.  Tree Biomass Equations from Terrestrial LiDAR: A Case Study in Guyana , 2019, Forests.

[12]  Richard H. Waring,et al.  Forest Ecosystem Analysis at Multiple Time and Space Scales , 2007 .

[13]  Lammert Kooistra,et al.  Assessing capacities of non-Annex I countries for national forest monitoring in the context of REDD+ , 2012 .

[14]  S. Goetz,et al.  Importance of biomass in the global carbon cycle , 2009 .

[15]  J. Dymond,et al.  LiDAR-Based Regional Inventory of Tall Trees—Wellington, New Zealand , 2018, Forests.

[16]  R. B. Jackson,et al.  CO 2 emissions from forest loss , 2009 .

[17]  M. Herold,et al.  The Importance of Consistent Global Forest Aboveground Biomass Product Validation , 2019, Surveys in Geophysics.

[18]  Chaofan Wu,et al.  Spatiotemporal Variations of Aboveground Biomass under Different Terrain Conditions , 2018, Forests.

[19]  Chunying Ren,et al.  Estimation of Forest Above-Ground Biomass by Geographically Weighted Regression and Machine Learning with Sentinel Imagery , 2018, Forests.