Context:

• The state of Rajasthan has recorded a negligible increase of 25sqkm forest area, according to India State of Forest Report (ISFR), 2021.
• Out of the total geographical area of 3,42,239sqkm, the present forest cover is 16,655sqkm, which is 4.87%.
• The forest cover in the state two years ago was 16,630sqkm.

Key Points:

• According to the report, the state’s forest cover has seen a marginal increase of 0.15sqkm compared to 2019.
• While the moderate forest cover area has increased by 27sqkm, the open forest cover area has witnessed a slight reduction of 2sqkm in the last two years.
• In the previous report, the open forest was reduced from 12,210sqkm to 12,208sqkm.
• In 2019, the state’s forest cover had seen marginal increase of 57.51sqkm compared to 2017, when the moderate forest cover area and open forest cover area declined, though the former has started to show improvement.
• According to the report, while very dense forest (VDF) area remained unchanged from 2019 with 78sqkm, the moderately dense forest (MDF) witnessed a small increase from 4,342sqkm in 2019 to 4,369sqkm in 2021.
• Experts claimed that while the survey report is robust in terms of data, it does not take into consideration few other parameters including the quality of the vegetation and biodiversity which means that even though the forest cover might not have increased considerably, it has improved in terms of quality.
• IFSR said the reasons for increase were plantation and conservation activities as well as improvement in interpretation.
• The forest cover is broadly classified in four classes — Very dense forest, Moderately dense forest, Open forest and Mangrove.
• The classification of the cover into dense and open forests is based on internationally adopted norms of classification.
• It has not been possible to further segregate the dense forest into more classes owing to enormity of work of ground validation and limitations of methodology.
• However, there are certain limitations also to the report including non-availability of appropriate season data which sometimes leads to misinterpretation of the features owing to poor reflectance of data.