Influence of Seasonality on
Land Cover Classification
Forest Monitoring under REDD+Transparency under MRV

The implementation of international frameworks suchas REDD+ (Reduced Emissions from Deforestationand Forest Degradation) in a post-2012 climate
regime require transparent, reproducible andverifiable monitoring of national forest cover withineach country. The application of satellite remotesensing techniques for deforestation and forest
degradation monitoring is widely accepted andprovides the only means to forest monitoring in
Cambodia is based on visucover such large areas.

A change from manual to automatic classification isexpected to increase the transparency andreproducibility of the monitoring process, but mightalso lead to lower overall accuracies due toincreased misclassifications (Fig. 2 and 3). One
major cause for such misclassifications is theinfluence of the seasonality on the reflectance valuesof the various vegetation types, which strongly affectcomputer-based land cover classifications. Together
with the Cambodia Forestry Administration FFPRI isconducting a project to adjust for such seasonality
effects.

In this brochure we will deal with the forests ofCambodia as a typical example for the vast biome oftropical dry forests. Up to now al delineation of highresolution optical satellite data by human interpreters(Fig. 1). The manually derived land cover maps show
a very high accuracy but the quality strongly depends
on the performance of the human interpreters.
As optical sensors depend on cloud-free scenes,
only dry season data can be used. The leaf
phenology of the semi-evergreen and deciduous
forest types changes considerably between early tolate dry season due to leaf shedding. This results indifferent spectral properties of the corresponding
forests (seasonality effects). Using SPOT 4/5 satellite
data more than 80 scenes are needed to derive amosaic of whole Cambodia (Fig. 1) . And as opticalsatellite imagery acquired during the dry season is
not completely free of atmospheric disturbances suchas clouds and haze, it is unavoidable that single
scenes of this mosaic stem from different times of thedry season. Therefore the mosaic will combine
satellite scenes from the beginning of the dry season,
when deciduous forests still have leaves, with scenes
from the mid or end of the dry season, showing thesame forest type without leaves.

231For that sake, the influence of the seasonality on thespectral properties of all satellite bands per forest
type has to be analyzed, resulting in functions
describing the correlation between reflectance values
and acquisition date. These functions can be used tocorrect for the influence of seasonality.
Land cover map prepared by Cambodia FA

Influence of Seasonality on Land Cover Classification
Technical Approach
In a first step the influence of seasonality on thereflectance values of MODIS (Moderate Resolution
Imaging Spectroradiometer) data are analyzed. In
our study NASA LP DAAC (Land Processes
Distributed Active Archive Center) MODIS Surface
Reflectance 8-Day L3 Global 500m are used foranalysis. The medium spatial resolution of thissensor, situated on two satellite systems Terra andAqua, allows due to its wide swath of view andtherefore high overpass frequency a dense temporal
coverage of the whole study area. This is crucial,
as only this high temporal resolution data allows
deriving monthly composites of whole Cambodia,
which are completely free of clouds. Thesecomposites are derived using the arithmetic mean of
the 8 available monthly 8-day datasets (2 x 4 scenes
per month) for each month from October till April.
For forest cover monitoring under REDD+ and tounderstand the various drivers of deforestation it is
crucial to have detailed knowledge about thetemporal trend of forest cover changes within acountry. Therefore, forest cover maps have to be
derived at regular 2-year intervals. Due to interannual variability in precipitation, each year mightshow slightly different temporal reflectance patterns.
Thus, the analysis of the effects of seasonality using
MODIS data has to be undertaken separately foreach single year of investigation.

Reflectancee.g. Evergreen forest (SPOT band 1, 550-570 nm) 4

This finally facilitates an automatic land coverclassification for REDD+ forest monitoring on
country-level based on high resolution opticalsatellite data. In order to secure consistency andcomparability between the single investigation
periods, an object-based classification approach will
be applied on the season-adjusted SPOT mosaic
using the land cover classification of each particular
preceding investigation period as supplementary
information.
Bridging Project- and National-Level Activities
The derived land cover map will be validated and if
necessary corrected using information obtained byvarious local project-level REDD+ activities. This is a
crucial step, as only the integration of the existing
project-level activities can secure the success fornational-level REDD+ monitoring in the long-term.
The validated land cover map additionally serves asbasis to analyze the seasonality of the following
investigation period, which can then be used tocorrect a next SPOT mosaic as described above.
The iteration of these steps forward as well as
backward in time finally allows deriving historic landcover maps, providing information about the temporal
trend in forest cover change (Fig. 5).
Depending on the results in Cambodia, this REDD+
monitoring approach might be also applicable inother regions of the world with tropical dry forests.
5Project-level
REDD

LC / newbase map

SPOT
mosaicProject-level
REDD

LC / newbase map

SPOT
mosaicObtained
pixel value
March

Simulated pixelvalue December

Base map(FA Cambodia)

Time (future years)+4+2

Based on a manually derived high quality land coverclassification map obtained from the Cambodia
Forestry Administration of a base year (e.g. 2005/6),
the temporal change of the reflectance properties(mean reflectance value and standard deviation) per
each single MODIS band and land cover type are
analyzed. This is done for the investigation period
preceding the base-year. Polynomial models are
fitted in these analyses in order to derive thecharacteristic functions per land cover type (Fig. 4).

derived at different dates during the dry season, ispossible and allows the simulation of the reflectancevalues at similar acquisition dates.

Base year

AprilMarchFebruary

January

DecemberNovemberAs the final land cover map will be based on spatial
high resolution (20m/10m) SPOT 4/5 data, aregression analysis between the corresponding
MODIS and SPOT bands is undertaken in order toadjust above described functions. Using thesemodified functions, a temporal adjustment of thesingle SPOT scenes within the cloud-free mosaic,

Project-level
REDD

LC / newbase map

SPOTmosaicProject-level
REDD

LC / newbase map

SPOTmosaicTime (past years)-2-4

OctoberfSPOT (x)Contact
REDD Research and Development Center
Forestry and Forest Products Research Institute,
1 Matsunosato, Tsukuba, Ibaraki, 305-8687 Japan.
E-mail: redd-rd-center@ffpri.affrc.go.jp
Homepage: http://www.ffpri.affrc.go.jp/redd-rdc/en