Geographic Information Systems (GIS) Analyses & Methods
All spatial analyses are performed within the context of a Geographic
Information System. GIS comprises the software, hardware, and methodologies
that allow spatial data to be displayed, modified and analyzed.
One of the powerful capabilities of GIS is the ability to combine
spatial data with non-spatial data, such as socio-economic and demographic
(SED) survey data.
The development, modification and analysis of geospatial data (including
remotely sensed imagery) occur within a GIS environment. Although
GIS and remote sensing are commonly separated thematically, the
processing and analysis of remotely sensed imagery falls within
the realm of GIS. Some remote sensing information, therefore, is
listed on this page as well as the Remote
Sensing Web Pages.
Below is a list of the GIS-based spatial analytical methods and
techniques that have been utilized up to this point in the CPC Ecuador
Projects. Each method has a brief description, which will be expanded
upon in the near future.
- Nearest Community, Euclidean – Calculation of
straight-line distance from each sampled household to the nearest
community.
- Nearest Community, Network – Calculation of
the distance along the road network from each sampled household
to the nearest community.
- Distance to Nearest Water Source, Euclidean –
Calculation of the straight-line distance from each sampled household
to the nearest water source.
- Land Use & Land Cover (LULC) Classification –
While this technically falls under remote sensing, it is a GIS
method nonetheless. Classification involves training an algorithm
to assign an LULC class to each pixel in an image based on statistics
generated from training data, which is itself generated from knowledge
of the true LULC on the ground. See the
Remote Sensing Web Pages for more detail.
- Neural Network-Based Cloud Masking – Neural
networks, a subset of artificial intelligence, are used to classify
and remove clouds and cloud shadows from the remotely sensed imagery.
- Change Detection – See the discussion of change
detections on the Remote
Sensing Web Pages.
- Pattern Metrics – Using the LULC classifications,
a variety of numeric indices were calculated that provide insight
into the structure of the landscape, including contiguity and
patchiness of land cover.
- Integrated Spatial/Non-spatial Generalized Linear Mixed
Modeling – Incorporation of SED data, spatial variables
and pattern metrics into a GLMM to analyze SED determinants of
LULC change.
- Analysis of Reliability in Reported Land Use –
The reliability of the amount of forest reported by each surveyed
farmer was assessed. Correlations were performed between amount
of forest on each farm from the LULC classification and the reported
amount for the same farm.
- Feature Extraction – Limited feature extraction
of roads from high-resolution IKONOS satellite imagery and medium-resolution
Landsat TM satellite imagery.
- Cellular Automata (CA) Modeling – See the
CA
Web Page for more details.
