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Quantification of live aboveground forest biomass dynamics with Landsat time-series and field inventory data: A comparison of empirical modeling approaches

Remote Sensing of Environment, 2010.

Powell, S., Cohen, W., Healey, S., Kennedy, R., Moisen, G., Pierce, K., Ohmann, J.

The authors modeled tree biomass in a 20 year time series of Landsat images and tested three statistical techniques to derive trajectories of evolution.

Reduced Major Axis regression …

Gradient Nearest Neighbor imputation …

Random Forests regression trees: RF is a non- parametric ensemble modeling approach that constructs numerous small regression trees that vote on predictions, and is considered to be robust to over-fitting.

The following variables were employed to model live aboveground tree biomass:

Raw Landsat bands (B1–B5, B7, as surface reflectance)
Tasseled Cap indices (brightness (TCB), greenness (TCG), and wetness (TCW))
NDVI, ( ( B4 − B3 ) / ( B4 + B3 ) )

Characteristic of using time-series: One approach that leverages temporal information involves smoothing a time-series of predictions at the pixel level. Such temporal smoothing may be advantageous in two important ways. First, doing somayimprove estimates of biomass change, compared to the more commonly applied two-date change- detection method. Second, improved predictions of biomass at any given point along the time-series may be possible …

Feature-selection ability of the decision-tree algorithm and the impact of feature-selection/extraction on decision-tree results based on hyperspectral data

International Journal of Remote Sensing, 2008.

Wang, Y., Li, J.

Decision Tree (DT) was tested as a feature selection algorithm, using hyperspectral data. Feature selection is defined by the authors as imperative for massive amounts of data. According to the authors, “feature selection results of DT are those features that are used to form splitting rules at internal tree nodes”, i.e. the attributes of the splitting nodes are the most relevant ones. C4.5 algorithm were employed in this article.

DT as a feature selector is proportional to the amount of samples (“tends to select too many features when the sample size is large”), and they suggested to use another algorithm to select features (mainly with small sample sizes), and to allow repeated use of features to form split nodes.

Another suggestion is to use “feature extraction”, which means creating new features by applying combinations on already existent features. However, “although extracted features have higher discrimination power, their physical meanings were hard to explain, which lowered the interpretability of classification trees”.

Applying case-based reasoning in the evolution of deforestation patterns in the Brazilian Amazonia

ACM symposium on Applied computing, 2008.

Mota, J., Câmara, G., Fonseca, L., Escada, M., Bittencourt, O.

Starting from one initial pattern of deforestation, they aim to recover the object's history, based on a set of similar cases of deforestation progress. For this purpose they use a Case-Based system, which stores previous cases, and “continually increases allowing adapting cases with similar characteristics that can be useful to solve a new problem”.

The database of cases were generated in the Agrarian Settlements Project called Vale do Anari, in Rondônia. The main spatial patterns are called Linear, Irregular, and Geometric. The stages of deforestation were defined as Road, Small Lots, and Large Concentration. Such typology were applied to describe the deforestation evolution in another area.

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