Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks

Abstract. Advances in forest carbon mapping have the potential to greatly reduce uncertainties in the global carbon budget and to facilitate effective emissions mitigation strategies such as REDD+ (Reducing Emissions from Deforestation and Forest Degradation). Though broad-scale mapping is based primarily on remote sensing data, the accuracy of resulting forest carbon stock estimates depends critically on the quality of field measurements and calibration procedures. The mismatch in spatial scales between field inventory plots and larger pixels of current and planned remote sensing products for forest biomass mapping is of particular concern, as it has the potential to introduce errors, especially if forest biomass shows strong local spatial variation. Here, we used 30 large (8–50 ha) globally distributed permanent forest plots to quantify the spatial variability in aboveground biomass density (AGBD in Mg ha–1) at spatial scales ranging from 5 to 250 m (0.025–6.25 ha), and to evaluate the implications of this variability for calibrating remote sensing products using simulated remote sensing footprints. We found that local spatial variability in AGBD is large for standard plot sizes, averaging 46.3% for replicate 0.1 ha subplots within a single large plot, and 16.6% for 1 ha subplots. AGBD showed weak spatial autocorrelation at distances of 20–400 m, with autocorrelation higher in sites with higher topographic variability and statistically significant in half of the sites. We further show that when field calibration plots are smaller than the remote sensing pixels, the high local spatial variability in AGBD leads to a substantial "dilution" bias in calibration parameters, a bias that cannot be removed with standard statistical methods. Our results suggest that topography should be explicitly accounted for in future sampling strategies and that much care must be taken in designing calibration schemes if remote sensing of forest carbon is to achieve its promise.

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Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks

Biogeosciences Discussions ◽

10.5194/bgd-11-5711-2014 ◽

2014 ◽

Vol 11 (4) ◽

pp. 5711-5742 ◽

Cited By ~ 25

Author(s):

M. Réjou-Méchain ◽

H. C. Muller-Landau ◽

M. Detto ◽

S. C. Thomas ◽

T. Le Toan ◽

...

Keyword(s):

Remote Sensing ◽

Spatial Variability ◽

Spatial Autocorrelation ◽

Sampling Error ◽

Spatial Scales ◽

Forest Biomass ◽

Remote Sensing Data ◽

Field Measurements ◽

Forest Carbon ◽

Mitigation Strategies

Abstract. Advances in forest carbon mapping have the potential to greatly reduce uncertainties in the global carbon budget and to facilitate effective emissions mitigation strategies such as REDD+. Though broad scale mapping is based primarily on remote sensing data, the accuracy of resulting forest carbon stock estimates depends critically on the quality of field measurements and calibration procedures. The mismatch in spatial scales between field inventory plots and larger pixels of current and planned remote sensing products for forest biomass mapping is of particular concern, as it has the potential to introduce errors, especially if forest biomass shows strong local spatial variation. Here, we used 30 large (8–50 ha) globally distributed permanent forest plots to quantify the spatial variability in aboveground biomass (AGB) at spatial grains ranging from 5 to 250 m (0.025–6.25 ha), and we evaluate the implications of this variability for calibrating remote sensing products using simulated remote sensing footprints. We found that the spatial sampling error in AGB is large for standard plot sizes, averaging 46.3% for 0.1 ha subplots and 16.6% for 1 ha subplots. Topographically heterogeneous sites showed positive spatial autocorrelation in AGB at scales of 100 m and above; at smaller scales, most study sites showed negative or nonexistent spatial autocorrelation in AGB. We further show that when field calibration plots are smaller than the remote sensing pixels, the high local spatial variability in AGB leads to a substantial "dilution" bias in calibration parameters, a bias that cannot be removed with current statistical methods. Overall, our results suggest that topography should be explicitly accounted for in future sampling strategies and that much care must be taken in designing calibration schemes if remote sensing of forest carbon is to achieve its promise.

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CHARACTERISTICS OF THE REMOTE SENSING DATA USED IN THE PROPOSED UNFCCC REDD+ FOREST REFERENCE EMISSION LEVELS (FRELS)

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b8-669-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 669-672

Author(s):

B. A. Johnson ◽

H. Scheyvens ◽

H. Samejima ◽

M. Onoda

Keyword(s):

Remote Sensing ◽

Land Use ◽

Remote Sensing Data ◽

Forest Degradation ◽

Median Number ◽

Forest Carbon ◽

Data Sets ◽

Median Percentage ◽

Forestry Sector

Developing countries must submit forest reference emission levels (FRELs) to the UNFCCC to receive incentives for REDD+ activities (e.g. reducing emissions from deforestation/forest degradation, sustainable management of forests, forest carbon stock conservation/enhancement). These FRELs are generated based on historical CO2 emissions in the land use, land use change, and forestry sector, and are derived using remote sensing (RS) data and in-situ forest carbon measurements. Since the quality of the historical emissions estimates is affected by the quality and quantity of the RS data used, in this study we calculated five metrics (i-v below) to assess the quality and quantity of the data that has been used thus far. Countries could focus on improving on one or more of these metrics for the submission of future FRELs. Some of our main findings were: (i) the median percentage of each country mapped was 100%, (ii) the median historical timeframe for which RS data was used was 11.5 years, (iii) the median interval of forest map updates was 4.5 years, (iv) the median spatial resolution of the RS data was 30m, and (v) the median number of REDD+ activities that RS data was used for operational monitoring of was 1 (typically deforestation). Many new sources of RS data have become available in recent years, so complementary or alternative RS data sets for generating future FRELs can potentially be identified based on our findings; e.g. alternative RS data sets could be considered if they have similar or higher quality/quantity than the currently-used data sets.

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The Forest Observation System, building a global reference dataset for remote sensing of forest biomass

Scientific Data ◽

10.1038/s41597-019-0196-1 ◽

2019 ◽

Vol 6 (1) ◽

Cited By ~ 13

Author(s):

Dmitry Schepaschenko ◽

Jérôme Chave ◽

Oliver L. Phillips ◽

Simon L. Lewis ◽

Stuart J. Davies ◽

...

Keyword(s):

Remote Sensing ◽

Forest Biomass ◽

Field Measurements ◽

Forest Degradation ◽

Observation System ◽

Energy Potential ◽

Ecosystem Research ◽

Carbon Losses ◽

Made In

Abstract Forest biomass is an essential indicator for monitoring the Earth’s ecosystems and climate. It is a critical input to greenhouse gas accounting, estimation of carbon losses and forest degradation, assessment of renewable energy potential, and for developing climate change mitigation policies such as REDD+, among others. Wall-to-wall mapping of aboveground biomass (AGB) is now possible with satellite remote sensing (RS). However, RS methods require extant, up-to-date, reliable, representative and comparable in situ data for calibration and validation. Here, we present the Forest Observation System (FOS) initiative, an international cooperation to establish and maintain a global in situ forest biomass database. AGB and canopy height estimates with their associated uncertainties are derived at a 0.25 ha scale from field measurements made in permanent research plots across the world’s forests. All plot estimates are geolocated and have a size that allows for direct comparison with many RS measurements. The FOS offers the potential to improve the accuracy of RS-based biomass products while developing new synergies between the RS and ground-based ecosystem research communities.

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CHARACTERISTICS OF THE REMOTE SENSING DATA USED IN THE PROPOSED UNFCCC REDD+ FOREST REFERENCE EMISSION LEVELS (FRELS)

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b8-669-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 669-672 ◽

Cited By ~ 1

Author(s):

B. A. Johnson ◽

H. Scheyvens ◽

H. Samejima ◽

M. Onoda

Keyword(s):

Remote Sensing ◽

Land Use ◽

Remote Sensing Data ◽

Forest Degradation ◽

Median Number ◽

Forest Carbon ◽

Data Sets ◽

Median Percentage ◽

Forestry Sector

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Effect of Slope Mesorelief on the Spatial Variability of Soil Properties and Vegetation Index Based on Remote Sensing Data

Izvestiya Atmospheric and Oceanic Physics ◽

10.1134/s0001433819090202 ◽

2019 ◽

Vol 55 (9) ◽

pp. 1329-1337

Author(s):

N. V. Gopp ◽

T. V. Nechaeva ◽

O. A. Savenkov ◽

N. V. Smirnova ◽

V. V. Smirnov

Keyword(s):

Remote Sensing ◽

Spatial Variability ◽

Soil Properties ◽

Vegetation Index ◽

Remote Sensing Data ◽

Sensing Data

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Imaging Spectroscopy for Conservation Applications

Remote Sensing ◽

10.3390/rs13020292 ◽

2021 ◽

Vol 13 (2) ◽

pp. 292

Author(s):

Megan Seeley ◽

Gregory P. Asner

Keyword(s):

Remote Sensing ◽

Decision Making ◽

Case Studies ◽

Spatial Scales ◽

Imaging Spectroscopy ◽

Remote Sensing Data ◽

Decision Makers ◽

High Spectral Resolution ◽

Conservation Areas ◽

Broad Array

As humans continue to alter Earth systems, conservationists look to remote sensing to monitor, inventory, and understand ecosystems and ecosystem processes at large spatial scales. Multispectral remote sensing data are commonly integrated into conservation decision-making frameworks, yet imaging spectroscopy, or hyperspectral remote sensing, is underutilized in conservation. The high spectral resolution of imaging spectrometers captures the chemistry of Earth surfaces, whereas multispectral satellites indirectly represent such surfaces through band ratios. Here, we present case studies wherein imaging spectroscopy was used to inform and improve conservation decision-making and discuss potential future applications. These case studies include a broad array of conservation areas, including forest, dryland, and marine ecosystems, as well as urban applications and methane monitoring. Imaging spectroscopy technology is rapidly developing, especially with regard to satellite-based spectrometers. Improving on and expanding existing applications of imaging spectroscopy to conservation, developing imaging spectroscopy data products for use by other researchers and decision-makers, and pioneering novel uses of imaging spectroscopy will greatly expand the toolset for conservation decision-makers.

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Detecting Forest Degradation in the Three-North Forest Shelterbelt in China from Multi-Scale Satellite Images

Remote Sensing ◽

10.3390/rs13061131 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1131

Author(s):

Tao Yu ◽

Pengju Liu ◽

Qiang Zhang ◽

Yi Ren ◽

Jingning Yao

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Net Primary Production ◽

Remote Sensing Data ◽

Forest Degradation ◽

Observation Data ◽

Area Index ◽

Sensing Data ◽

Multi Scale

Detecting forest degradation from satellite observation data is of great significance in revealing the process of decreasing forest quality and giving a better understanding of regional or global carbon emissions and their feedbacks with climate changes. In this paper, a quick and applicable approach was developed for monitoring forest degradation in the Three-North Forest Shelterbelt in China from multi-scale remote sensing data. Firstly, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR) and Net Primary Production (NPP) from remote sensing data were selected as the indicators to describe forest degradation. Then multi-scale forest degradation maps were obtained by adopting a new classification method using time series MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus (ETM+) images, and were validated with ground survey data. At last, the criteria and indicators for monitoring forest degradation from remote sensing data were discussed, and the uncertainly of the method was analyzed. Results of this paper indicated that multi-scale remote sensing data have great potential in detecting regional forest degradation.

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