Agroforestry pp 137-161 | Cite as

Monitoring and Assessment of Trees Outside Forests (TOF)

Chapter

Abstract

In the context of the international conventions on climate change (United Nations Framework Convention on Climate Change, UN-FCCC) and biological diversity (United Nations Convention on Biological Diversity, UN-CBD), the demand for up-to-date information on tree resources within and outside forests is higher than ever before, urging for approaches to reliably monitor tree resources across large areas. While for the assessment of tree resources within forests a variety of sophisticated forest inventory methods has been developed and tested, fewer efforts have been undertaken that focus on the assessment of trees outside forests (TOF). While the variables of interest are essentially the same, main differences in the assessment of trees within and outside forests arise from the distinctive characteristics of TOF, including uneven spatial distribution, specific geometric arrangements, specific functions, and the presence of other land uses. In this chapter, we give an overview of inventory approaches suitable for the science-based assessment of TOF, with a focus on agricultural lands, and highlight how inventory designs developed for forest inventories may be adapted to the assessment of trees on non-forest lands. The chapter covers considerations about operational definitions of TOF, describes implications resulting from modeling tree attributes using allometric models, and reviews the application of different response (plot) designs used in ground-based or remote sensing-based TOF assessments. Further, current approaches to monitor TOF resources with active and passive remote sensing sensors, such as LiDAR, RADAR, SPOT, RapidEye, Landsat, MODIS, etc., are presented, and besides, it is outlined how field inventory data and remote sensing data can potentially be integrated to increase the precision of parameter estimates.

Keywords

Allometric models Definitions Remote sensing Response designs Sampling strategies 

Notes

Acknowledgments

We are grateful to Dr. VP Tewari for inviting and encouraging us to write this chapter. Funding for this work was generously provided by the GTZ/BMZ Green Rubber Project (No. 13.1432.7-001.00).

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© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Forest Inventory and Remote Sensing, Faculty of Forest Sciences and Forest EcologyUniversity of GöttingenGöttingenGermany
  2. 2.Thünen Institute of Forest EcosystemsUniversity of GöttingenEberswaldeGermany

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