542
17.7.4 Field Observation and Validation of Results
Another method is field observation and validation of results. Field observations are
vital in remote sensing. In our research, the data collection stage prior to validation
of the results of supervised classifications includes two categories of surveying
methods, random (probabilistic) methods and empirical (non-random) methods.
The survey was based on the satellite imaging document. Point sampling was used
during this data processing stage. For each class, 1000 training points and 1000
control points (not the same points) were randomly chosen. This survey determines
whether an observation unit belongs to a sample by random draw. In this case, the
probability law is known. The random draw is stratified starting from all the homogeneous thematic areas. The stratification was initially performed prior to the field
investigation phase. This first stage stratification is a morphological stratification
that relies on textural homogeneity, backscattering and thematic homogenization
criteria. As concerns field observations, the ground surveys (20 floristic surveys per
thematic class) carried out in the restored delta areas allowed us to determine the
vegetation typology in the surveyed area. Vegetation description is physiognomic
and includes land cover rate estimation. Depending on the size of the homogeneous
area, the size of the observation unit is more or less significant. The vegetation
structure and type were measured at each point within a 100-m radius of the
observer. Some floristic information was also gathered, including a list of species
classified by physiognomic layers (trees, shrubs, and grasses).
The results of the evaluation are summarized in a confusion matrix. Based on the
confusion matrix, statistical accuracy parameters are calculated. One is the overall
accuracy, which counts pixels that are correctly classified in the reference divided
by all pixels that are taken for reference. This procedure is used for both optical and
microwave image classification.
17.7.5 Remote Sensing and Restoration Areas in the Danube
Delta
The results of this study relate to combinations of data from different satellite sensors (Sentinel-1 time series, Sentinel-2) that are used to improve the accuracy of
recognition and mapping of major vegetation classes in the restoring areas in the
Danube Delta. First, the data from each sensor are classified and analyzed. The
results show quite good classification performance (87.5% mean accuracy for
Sentinel-2; 95.7% for the Sentinel-1 time series) in this first step. The combination
of the Sentinel-2 time series and optical data from Sentinel-2 improved the performance of the classification (97.1%) (Fig. 17.16).
The vegetation types were labeled according to ten classes (figures classifications). These classifications allowed us to distinguish several classes of reeds in the
‘large marsh vegetation’ class (reed vegetation on salinized soils, pure reed
S. Niculescu et al.
17.7.4 Field Observation and Validation of Results
Another method is field observation and validation of results. Field observations are
vital in remote sensing. In our research, the data collection stage prior to validation
of the results of supervised classifications includes two categories of surveying
methods, random (probabilistic) methods and empirical (non-random) methods.
The survey was based on the satellite imaging document. Point sampling was used
during this data processing stage. For each class, 1000 training points and 1000
control points (not the same points) were randomly chosen. This survey determines
whether an observation unit belongs to a sample by random draw. In this case, the
probability law is known. The random draw is stratified starting from all the homogeneous thematic areas. The stratification was initially performed prior to the field
investigation phase. This first stage stratification is a morphological stratification
that relies on textural homogeneity, backscattering and thematic homogenization
criteria. As concerns field observations, the ground surveys (20 floristic surveys per
thematic class) carried out in the restored delta areas allowed us to determine the
vegetation typology in the surveyed area. Vegetation description is physiognomic
and includes land cover rate estimation. Depending on the size of the homogeneous
area, the size of the observation unit is more or less significant. The vegetation
structure and type were measured at each point within a 100-m radius of the
observer. Some floristic information was also gathered, including a list of species
classified by physiognomic layers (trees, shrubs, and grasses).
The results of the evaluation are summarized in a confusion matrix. Based on the
confusion matrix, statistical accuracy parameters are calculated. One is the overall
accuracy, which counts pixels that are correctly classified in the reference divided
by all pixels that are taken for reference. This procedure is used for both optical and
microwave image classification.
17.7.5 Remote Sensing and Restoration Areas in the Danube
Delta
The results of this study relate to combinations of data from different satellite sensors (Sentinel-1 time series, Sentinel-2) that are used to improve the accuracy of
recognition and mapping of major vegetation classes in the restoring areas in the
Danube Delta. First, the data from each sensor are classified and analyzed. The
results show quite good classification performance (87.5% mean accuracy for
Sentinel-2; 95.7% for the Sentinel-1 time series) in this first step. The combination
of the Sentinel-2 time series and optical data from Sentinel-2 improved the performance of the classification (97.1%) (Fig. 17.16).
The vegetation types were labeled according to ten classes (figures classifications). These classifications allowed us to distinguish several classes of reeds in the
‘large marsh vegetation’ class (reed vegetation on salinized soils, pure reed
S. Niculescu et al.