Data for: Tree-planting programs in Himachal Pradesh India 2019

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Persistent link to this item

https://doi.org/10.13020/8x0d-gb23
https://hdl.handle.net/11299/212261

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Keywords

India

Himalayas

tree plantations

afforestation

Collection period

2020-01-02
2020-03-15

Date completed

2020-03-15

Date updated

Time period coverage

Geographic coverage

Source information

Journal Title

Journal ISSN

Volume Title

Title

Data for: Tree-planting programs in Himachal Pradesh India 2019

Published Date

2020-03-26

Authors

Rana, Pushpendra

Fleischman, Forrest

Ramprasad, Vijay

Lee, Kangjae

Group

Author Contact

Rana, Pushpendra
pranaifs27@gmail.com

Type

Dataset
Field Study Data
Spatial Data
Statistical Computing Software Code

Abstract

This dataset provides information on the tree plantation programs of the government of the state of Himachal Pradesh, India. Data were released publicly by the government in response to questions posed by Members of the State Legislative Assembly in 2019. We combined this data with other publicly available datasets to provide a more complete picture of plantation programs for the purpose of evaluating their effectiveness.

Description

Data are derived from publicly released data produced by the forest department of the Government of Himachal Pradesh, India. Dataset-A comprising of 16,674 forest polygon is the result of a 4 year effort on the part of the Himachal Pradesh forest department, and covers 33 forest divisions of Himachal Pradesh. We could not use 1998 polygons in this dataset due to missing data. The digitization of polygons for the rest of the 10 forest divisions in the state of Himachal Pradesh is in process. The data has 31 predictor variables used in training a predictive model to estimate tree cover loss probabilities in these compartments based on ensemble-modeling. Outcome variable included in the dataset is tree cover loss/mortality (dummy; mortality =1, No mortality =0). Predictor variables include variables related to forest dependence, soil and biophysical characteristic, canopy cover and other management practices. For details about prediction algorithm model construction and model predictors, please refer to the readme section. Dataset-B comprises of 2147 forest polygons where plantations happened from 1 st Jan 2016 to 31 st July, 2019. Total 2809 tree plantations were carried out during this period. But, due to missing data, we could only use 2147 for construction of data. Finally, for our paper (under review), we used 2024 tree plantations for which we had budgetary data available. The dataset also has same predictor variables as in dataset-A. We used our selected ensemble model to predict tree cover loss probabilities for 2024 tree plantations and then compared budgetary allocations against these probabilities for our analysis.

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License

CC0 1.0 Universal
http://creativecommons.org/publicdomain/zero/1.0/

Publisher

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Data Repository for U of M (DRUM)

Funding information

“Impacts of Afforestation on the Provision of Ecosystem Services to Rural Communities in India (ROSES 15).” National Aeronautics and Space Administration (NASA) Award #NNX17AK14G.

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Previously Published Citation

Suggested citation

Rana, Pushpendra; Fleischman, Forrest; Ramprasad, Vijay; Lee, Kangjae. (2020). Data for: Tree-planting programs in Himachal Pradesh India 2019. Retrieved from the Data Repository for the University of Minnesota (DRUM), https://doi.org/10.13020/8x0d-gb23.

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