Forest cover dynamics (1998 to 2019) and prediction of deforestation probability using binary logistic regression (BLR) model of Silabati watershed, India

References 34

1. 1.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library

2. 2.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library

3. 3.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library

4. 4.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library

5. 5.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library

6. 6.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library

7. 7.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library

8. 8.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library

9. 9.

   This reference’s wording can’t be shown under the paper’s licence.

   Not in our library

10. 10.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

11. 11.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

12. 12.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

13. 13.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

14. 14.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

15. 15.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

16. 16.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

17. 17.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

18. 18.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

19. 19.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

20. 20.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

21. 21.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

22. 22.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

23. 23.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

24. 24.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

25. 25.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

26. 26.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

27. 27.
    Confounding and collinearity in regression analysis: a cautionary tale and an alternative procedure, illustrated by studies of British voting behaviour · 2017

    No notice
28. 28.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

29. 29.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

30. 30.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

31. 31.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

32. 32.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

33. 33.

    This reference’s wording can’t be shown under the paper’s licence.

    Not in our library

34. 34.

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    Not in our library

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