Improved Mapping of Tropical Forests With Optical and SAR Imagery, Part I: Forest Cover and Accuracy Assessment Using Multi-Resolution Data

Häme, Tuomas; Kilpi, Jorma; Ahola, Heikki; Rauste, Yrjö; Antropov, Oleg; Rautiainen, Miina; Sirro, Laura; Bounpone, S.

doi:10.1109/jstars.2013.2241019

Cited by 25 publications

(31 citation statements)

References 47 publications

(44 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Six land use classes that are compatible with the good practice guidance [6] of IPCC: 'forest land', 'cropland', 'grassland', 'wetlands', 'settlements' and 'other land' were used in the accuracy assessment of the classifications. Additionally, a class 'shrubland' was used in the classification but in the accuracy assessment it was included in the 'grassland' class (class definitions from [34], Table 1). For forest definition, the FAO (Food and Agriculture Organization of the United Nations) thresholds, forest cover larger than 10% and a minimum height of 5 m [38], were used.…”

Section: Study Site and Class Definitionmentioning

confidence: 99%

“…Accuracy assessment approaches that are based on very high resolution (VHR) optical images with a spatial resolution less than one metre can be implemented at any global location. This kind of approach has been applied e.g., in [34][35][36]. If no VHR imagery exists from the mapping epoch, one applied solution has been to use the best available optical data with lower resolution [15,32].…”

Section: Introductionmentioning

confidence: 99%

“…These results were also considered applicable to this study. The procedure and the results are reported in detail in [34]. The 50 m by 50 m square was small enough to make objective visual interpretation of reference data possible.…”

mentioning

confidence: 99%

“…Large enough plot size was also needed to consider the residual geometric differences between image data sets in reference data collection for model training and for accuracy assessment. With 800 m distance, the spatial correlation between the plots was assumed to be low with a sufficient margin [34]. In comparison, the most common plot distance in the Finnish national forest inventory is 300 m [48].…”

mentioning

confidence: 99%

See 3 more Smart Citations

Potential of Different Optical and SAR Data in Forest and Land Cover Classification to Support REDD+ MRV

et al. 2018

Self Cite

View full text Add to dashboard Cite

The applicability of optical and synthetic aperture radar (SAR) data for land cover classification to support REDD+ (Reducing Emissions from Deforestation and Forest Degradation) MRV (measuring, reporting and verification) services was tested on a tropical to sub-tropical test site. The 100 km by 100 km test site was situated in the State of Chiapas in Mexico. Land cover classifications were computed using RapidEye and Landsat TM optical satellite images and ALOS PALSAR L-band and Envisat ASAR C-band images. Identical sample plot data from Kompsat-2 imagery of one-metre spatial resolution were used for the accuracy assessment. The overall accuracy for forest and non-forest classification varied between 95% for the RapidEye classification and 74% for the Envisat ASAR classification. For more detailed land cover classification, the accuracies varied between 89% and 70%, respectively. A combination of Landsat TM and ALOS PALSAR data sets provided only 1% improvement in the overall accuracy. The biases were small in most classifications, varying from practically zero for the Landsat TM based classification to a 7% overestimation of forest area in the Envisat ASAR classification. Considering the pros and cons of the data types, we recommend optical data of 10 m spatial resolution as the primary data source for REDD MRV purposes. The results with L-band SAR data were nearly as accurate as the optical data but considering the present maturity of the imaging systems and image analysis methods, the L-band SAR is recommended as a secondary data source. The C-band SAR clearly has poorer potential than the L-band but it is applicable in stratification for a statistical sampling when other image types are unavailable.

show abstract

Section: Study Site and Class Definitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Potential of Different Optical and SAR Data in Forest and Land Cover Classification to Support REDD+ MRV

et al. 2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…In addition, optical remotely sensed imagery and other spatial data can be used to aid stratification, to inform sampling, and also to enhance estimation (Wulder et al 2012; see GOFC GOLD 2016, section 2.10.2.2). In a further paper, several image sources were combined by Häme et al (2013a) in mapping tropical forest classes. LiDAR data can be used to provide conventional sampling-based estimates of biomass characteristics, and also to determine changes in the amount of biomass over time (GOFC-GOLD 2016; see also Asner 2009, Naesset et al 2013a.…”

Section: Mapping Forest Cover and Biomassmentioning

confidence: 99%

Optical data-driven multi-source forest inventory setups for boreal and tropical forests

Muinonen¹

2018

Diss. For.

View full text Add to dashboard Cite

Muinonen, E. (2018). Optical data-driven multi-source forest inventory setups for boreal and tropical forests. Dissertationes Forestales 256. 53 p. ABSTRACTThe aim of the studies in this thesis was to apply and further develop methods in multi-source forest inventory tasks in boreal and tropical forests. The applications presented in this dissertation are based on optical remote sensing data and k-nearest neighbours techniques, both of which are common components in multi-source forest inventory.The use of variograms as a texture information source in standwise volume estimation was tested using image data from a digitized aerial photograph taken in Hyytiälä, Finland. According to the leave one out cross-validation, the accuracy of volume estimation at stand level improved when empirical values of semivariance were included in the set of feature variables.Landsat 5 Thematic Mapper (TM) satellite data was utilized in forest cover and volume mapping in Terai, Nepal. A corresponding multi-source forest inventory-oriented processing chain was also tested and demonstrated in forest volume mapping in the region of Kon Tum province in Vietnam. In these two studies, coarse scale MODIS reflectance products were used as a reference in a local correction approach conducted for the relative calibration of Landsat TM images.Multi-source forest inventory techniques for obtaining biomass maps have facilitated the development of a spatially explicit methodology to estimate the bioenergy potentials of forest chips. The technical bioenergy potential of forest chips was calculated in a case study in Central Finland, based on the logging residues and stumps from final fellings.An adaptation of the abovementioned methods and techniques in studies with target areas of forests in sub-tropical and tropical zones in Nepal and Vietnam was carried out using open source software tools. These studies serve the purpose of capacity building in utilizing remote sensing data in forest inventory activities related to the REDD+ mechanism, and estimating bioenergy potentials provides quantitative decision making support in the field of forest bioenergy production.

show abstract

Remote Sensing Concepts and Their Applicability in REDD+ Monitoring

Tokola

2015

Curr Forestry Rep

View full text Add to dashboard Cite

Th e Tier app ro ach introd uced by the ntergovernmental Panel on Climate Change (IPCC) has been used to describe levels of methodological quality and complexity in the measurement reporting and verification (MRV) assessment system. Although guidelines do not explicitly state accuracy requirements, it has been asserted in previous studies that remote sensing missions should entail biomass errors that remain within 20 % of stand level estimates. The biomass of the target forest can be almost directly measured using LiDAR techniques when other sensors have rather low capacity to penetrate through canopy and response signals are not too noisy to detect changes in closed forests. Optical or radar satellite data, in relative terms, are only a tenth of the price of LiDAR. Taking into account cost-effectiveness within a tropical context, it is proposed that Landsat-like data can be useful for obtaining land cover and economical estimates for large areas. Typically, the role of airborne LiDAR is to provide ground truth type of information covering large areas. The reducing emissions from deforestation and forest degradation measurement, reporting and verification (REDD MRV) will need good ground sample for basal area and canopy cover change detection. LiDAR will be also feasible for forest management planning maps and planning operational REDD activities, but cost-effective mapping of carbon stocks for regional-and national-scale assessments will rely on a combination of satellite imagery and ground-based inventory samples. The distribution of tree species and different forest types need to be measured from the field and information can be generalised using satellite data with 1-30-m resolution. The global scale maps with the ground resolution 100 m-1 km are mainly for visualisation purposes and can be used to extrapolate distribution of information collected by 1-30-m remote sensing sensors and field plots.

show abstract

Improved Mapping of Tropical Forests With Optical and SAR Imagery, Part I: Forest Cover and Accuracy Assessment Using Multi-Resolution Data

Cited by 25 publications

References 47 publications

Potential of Different Optical and SAR Data in Forest and Land Cover Classification to Support REDD+ MRV

Potential of Different Optical and SAR Data in Forest and Land Cover Classification to Support REDD+ MRV

Optical data-driven multi-source forest inventory setups for boreal and tropical forests

Remote Sensing Concepts and Their Applicability in REDD+ Monitoring

Contact Info

Product

Resources

About