Geo-ForPy - Forest Cover Changes in the Paraguayan Chaco - Landsat-5/7/8,1987-2020

The dataset is based on the analysis of forest cover dynamics in the Paraguayan Chaco (northeastern part of Paraguay) between 1987 and 2020. The underlying forest masks were derived through annual forst classifications with a Random-Forest-Classifier trained on Landsat data from 1987 until 2020. The map shows the year in which the forest area was lost.

Citation proposal
German Aerospace Center (DLR). Geo-ForPy - Forest Cover Changes in the Paraguayan Chaco - Landsat-5/7/8,1987-2020. https://gdk.gdi-de.org/geonetwork/srv/api/records/dbc3ec56-a9d7-4050-ad14-1ff2e72a753

Simple

Date ( Creation ): 2022-09-01T00:00:00

Edition

Identifier: https://geoservice.dlr.de/catalogue/srv/metadata/dbc3ec56-a9d7-4050-ad14-1ff2e72a753

Presentation form: Digital map

Other citation details

Purpose: This data set provides an overview over the dynamic development of the forest cover in the Paraguayan Chaco between 1987 and 2020.

Status: Completed

Author

German Aerospace Center (DLR) -

Point of contact

German Aerospace Center (DLR)

Maintenance and update frequency: As needed

GEMET - INSPIRE themes, version 1.0 ( Theme )

Land cover

Spatial scope

National

Keywords

opendata
DLR
EOC
GeoForPy
Forest
Forest Cover
Landsat-5
Landsat-7
Landsat-8
Paraguay
Time Series

Use limitation: Nutzungseinschränkungen: Das DLR ist nicht haftbar für Schäden, die sich aus der Nutzung ergeben. / Use Limitations: DLR not liable for damage resulting from use.

Access constraints: Other restrictions

Use constraints: Other restrictions

Other constraints: Nutzungsbedingungen: Lizenz, https://creativecommons.org/licenses/by/4.0/ Terms of use: License, https://creativecommons.org/licenses/by/4.0/

Other constraints

{"id": "cc-by-4.0",

"name": "Creative-Commons - Attribution 4.0 International (CC BY 4.0)",

"url": "http://dcat-ap.de/def/licenses/cc-by/4.0",

"quelle": "Copyright DLR (2022)"}

Spatial representation type: Grid

Denominator: 60000

Metadata language: English

Character set: UTF8

Topic category

Biota

Number of dimensions: 2

Dimension name: Column

Dimension size: 18909

Resolution: 30 m

Dimension name: Row

Dimension size: 22341

Resolution: 30 m

Cell geometry: Area

Transformation parameter availability: false

Distribution format

Cloud Optimized GeoTIFF ()

OnLine resource: FOREST_COVER_PRY_1986_2020_P1Y
WMS Access: Forest Cover Changes in the Paraguayan Chaco 1987-2020

OnLine resource: HTTP download Geo-ForPy - Paraguayan Chaco, 1987-2020
HTTP download Geo-ForPy - Paraguayan Chaco, 1987-2020

OnLine resource: Geo-ForPy - Deforestration in Latin America and the Paraguayan Chaco
Geo-ForPy - project Webpage

OnLine resource: Understanding 34 Years of Forest Cover Dynamics across the Paraguayan Chaco: Characterizing Annual Changes and Forest Fragmentation Levels between 1987 and 2020
Article

Hierarchy level: Dataset

Domain consistency

Measure identification: INSPIRE / Conformity_001

Conformance result

Date ( Publication ): 2010-12-08

Explanation: See the referenced specification.

Pass: true

Statement: This data set provides an overview of 34 years of forest cover losses in the Paraguayan Chaco. The year in which there was the last change of an area from forest to non-forest is represented by its colour. Afforestaton cannot be represented in this dataset.

Description

Geo-ForPy - Forest cover changes in the Paraguayan Chaco 1987-2020 - Processing

Data:

The used forest masks (1987 – 2020) are a product oft he GeoFor-Py project. More information on this data set can be found under the following link.

Processing:

For this overview, the forest masks from 1986 to 2020 were compared with each other. The year in which a pixel changed its value from forest to non-forest was stored in this data set. If there were several changes between forest and non-forest, the last change was saved.

Quality Assurance:

The underlying forest masks have overall accuracies between 85% and 97%. Missclassifications directly affect the derived year of deforestation and can cause salt and pepper effects within continuously deforested areas.