Table 4 Comparative assessment (on 1–3 score value) of potential adaptability of Community based monitoring (CM) and Remote Sensing + Community based Monitoring (RS + CM) methods
No | Parameters | CM method ranking | RS + CM method ranking | Remarks |
|---|---|---|---|---|
A. Salience | ||||
1 | Contextualization | High (3) | High (3) | Both the methods relies on local context of forest characteristics, measurements and change. |
2 | Coupling to national systems | Medium (2) | High (3) | RS + CM methods facilitate the concept of Danielsen et al. [15] on integration of local community monitoring through multi-scale approach |
3 | Linkages to performance | Medium (2) | High (3) | Due to spatial explicit wall–wall information, linking to payments becomes more reliable using RS + CM, and also addressing leakage |
4 | Diagnostic/prescriptive support | Low (1) | Medium (2) | RS + CM due to spatial character and synergy with local ground data helps planning for local prescriptions for forest management |
B. Credibility | ||||
5 | Informative | Medium (2) | High (3) | RS + CM produces 70% of CM inputs with spatial explicitness to identify areas of positive, negative change, leakage over large area, CM limits to plot or limited traverses |
6 | Accuracy | High (3) | High (3) | Both produces > 80% accurate information |
7 | Cost effectiveness | Medium (2) | High (3) | RS + CM is estimated as less costly (Ref Table-4) |
8 | Repeatability | Medium (2) | Medium (2) | Risk of communities with drawing from measurements exists. RS + CM models need to be developed on region specific context, current approach given do not work for old growth forests |
C. Legitimacy | ||||
9 | Removal of bias | Low (1) | Medium (2) | Intrinsic and extrinsic factors of CM potentially can induce bias [15]. RS + CM introduces bias due to interpretation/model inaccuracies but can be improved |
10 | Transparency | Medium (2) | High (3) | Geospatial methods known as best visualization tools, open access data and platforms, hence RS + CM is more transparent |
11 | Participatory | High (3) | Medium (2) | RS + CM builds models on community data, hence relatively extrinsic and might suffers from non participation |
12 | Mutual trust | High (3) | Medium (2) | RS + CM involves professionals and community, hence potential risks exists for mistrust, can taper down over time |
REDD+ MSRL index:potential adoptability | 0.72 | 0.86 | ||