Skip to content

module linearTrend

class linearTrend

Linear Trend in Remote Sensing

method __init__

1
__init__(image, nodata=-99999)

Parameters:

  • image: Optical images. It must be rasterio.io.DatasetReader with 3d.

  • nodata: The NoData value to replace with -99999.

method LN

1
LN(**kwargs)

Linear trend is useful for mapping forest degradation, land degradation, etc. This algorithm is capable of obtaining the slope of an ordinary least-squares linear regression and its reliability (p-value).

Parameters:

  • **kwargs: These will be passed to LN, please see full lists at:
  • https: //docs.scipy.org/doc/scipy/reference/generated/scipy.stats.linregress.html

Return: a dictionary with slope, intercept and p-value obtained. All of them in numpy.ndarray with 2d.

References:

Tarazona, Y., Maria, Miyasiro-Lopez. (2020). Monitoring tropical forest degradation using remote sensing. Challenges and opportunities in the Madre de Dios region, Peru. Remote - Sensing Applications: Society and Environment, 19, 100337.

Wilkinson, G.N., Rogers, C.E., 1973. Symbolic descriptions of factorial models for analysis of variance. Appl. Stat. 22, 392-399.

Chambers, J.M., 1992. Statistical Models in S. CRS Press.

Note:

Linear regression is widely used to analyze forest degradation or land degradation. Specifically, the slope and its reliability are used as main parameters and they can be obtained with this function. On the other hand, logistic regression allows obtaining a degradation risk map, in other words, it is a probability map.

method LR

1
LR(col_pos=0, **kwargs)

Logistic Regression is a statistical analysis technique that can measure statistically the relative influence of several factors and explain objectively how values depend on predictor variables. This method is applied to remotely sensed data.

Parameters:

  • **kwargs: These will be passed to MLN, please see full lists at:
  • https: //www.statsmodels.org/dev/generated/statsmodels.discrete.discrete_model.Logit.html

Return: a dictionary with the summary of logistic regression and an array of probability with 2d.

References: Tarazona, Y., Maria, Miyasiro-Lopez. (2020). Monitoring tropical forest degradation using remote sensing. Challenges and opportunities in the Madre de Dios region, Peru. Remote - Sensing Applications: Society and Environment, 19, 100337.

Chambers, J.M., 1992. Statistical Models in S. CRS Press.

Note:

Logistic regression allows obtaining a degradation risk map (for instance), in other words, it is a probability map.

This file was automatically generated via lazydocs.