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Monitoring trees outside forests: a review

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Abstract

Trees outside forests (TOFs) are an important natural resource that contributes substantially to national biomass and carbon stocks and to the livelihood of people in many regions. Over the last decades, decision makers have become increasingly aware of the importance of TOF, and as a consequence, this tree resource is nowadays often considered in forest monitoring systems. Our review shows that in many cases, TOF are included in national forest inventories, applying traditional methodologies with relatively sparse networks of field sample plots. Only in some countries, such as India, the design of the inventories has considered the special features of how TOFs occur in the landscape. Several research studies utilising remote sensing for monitoring TOF have been conducted lately, but very few studies include comparative studies to optimise sampling strategies for TOF. Our review indicates that methods combining remote sensing and field surveys appear to be very promising, especially when remote sensing techniques that assess both the horizontal and vertical structures of tree resources are applied. For example, two-phase sampling strategies with laser scanning in the first phase and a field survey in the second phase appear to be effective for assessing TOF resources. However, TOFs often exhibit different characteristics than forest trees. Thus, to improve TOF monitoring, there is often a need to develop models, e.g. for biomass assessment, that are specifically adapted to this tree resource. Alternatively, field-based remote sensing methods that provide structural information about individual trees, notably terrestrial laser scanning, could be further developed for TOF monitoring applications. This also would have a potential to reduce the problem of accessing TOF during field surveys, which is a problem, for example, in countries where TOF are present on intensively utilised private grounds like gardens and agricultural fields.

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Notes

  1. European Cooperation in Science and Technology

References

  • Ahmed, P. (2008). Trees outside forests (TOF): a case study of wood production and consumption in Haryana. International Forestry Review, 10, 165–172.

    Article  Google Scholar 

  • Baffetta, F., Fattorini, L., & Corona, P. (2009). Estimation of small woodlot and tree row attributes in large-scale forest inventories. Environmental and Ecological Statistics, 18, 147–167. doi:10.1007/s10651-009-0125-0.

    Article  Google Scholar 

  • Baffetta, F., Corona, P., & Fattorini, L. (2011). Assessing the attributes of scattered trees outside the forest by a multi-phase sampling strategy. Forestry, 84, 315–325. doi:10.1093/forestry/cpr015.

    Article  Google Scholar 

  • Barnsley, M. J., & Barr, S. L. (1996). Inferring urban land use from satellite sensor images using kernel-based spatial reclassification. Photogrammetric Engineering and Remote Sensing, 62, 949–958.

  • Barr, C. J., & Gillespie, M. K. (2000). Estimating hedgerow length and pattern characteristics in Great Britain using Countryside Survey data. Journal of Environmental Management, 60, 23–32. doi:10.1006/jema.2000.0359.

    Article  Google Scholar 

  • Beer, C., Reichstein, M., Tomelleri, E., Ciais, P., Jung, M., Carvalhais, N., et al. (2010). Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science, 329(5993), 834–838. doi:10.1126/science.1184984.

    Article  CAS  Google Scholar 

  • Bellefontaine, R., Petit, S., Deleporte, P., & Bertault, J.-G. (2002). Trees outside forests. Towards better awareness., 218.

  • Boggs, G. S. (2010). Assessment of SPOT 5 and QuickBird remotely sensed imagery for mapping tree cover in savannas. International Journal of Applied Earth Observation and Geoinformation, 12, 217–224. doi:10.1016/j.jag.2009.11.001.

    Article  Google Scholar 

  • Bohlin, J., Wallerman, J., & Fransson, J. E. S. (2012). Forest variable estimation using photogrammetric matching of digital aerial images in combination with a high-resolution DEM. Scandinavian Journal of Forest Research, 27, 692–699. doi:10.1080/02827581.2012.686625.

    Article  Google Scholar 

  • Brändli, U.-B. (2010). Schweizerisches Landesforstinventar. Ergebnisse der dritten Erhebung 2004–2006. Birmensdorf: WSL, BAFU.

    Google Scholar 

  • Brown, S. (1997). Estimating biomass and biomass change of tropical forests: a primer (Vol. 137, FAO Forestry Paper). Rome: FAO.

  • Chave, J., Andalo, C., Brown, S., Cairns, M. A., Chambers, J. Q., Eamus, D., et al. (2005). Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia, 145(1), 87–99.

    Article  CAS  Google Scholar 

  • Chave, J., Rejou-Mechain, M., Burquez, A., Chidumayo, E., Colgan, M. S., Delitti, W. B., et al. (2014). Improved allometric models to estimate the aboveground biomass of tropical trees. Global Change Biology, 20(10), 3177–3190.

    Article  Google Scholar 

  • Corona, P., & Fattorini, L. (2006). The assessment of tree row attributes by stratified two-stage sampling. European Journal of Forest Research, 125, 57–66.

    Article  Google Scholar 

  • Corona, P., Fattorini, L., & Franceschi, S. (2011). Two-stage sector sampling for estimating small woodlot attributes. Canadian Journal of Forest Research, 41(9), 1819–1826.

    Article  Google Scholar 

  • Corona, P., Agrimi, M., Baffetta, F., Barbati, A., Chiriacò, M. V., Fattorini, L., Pompei, E., Valentini, R., & Mattioli, W. (2012). Extending large-scale forest inventories to assess urban forests. Environmental Monitoring and Assessment, 184(3), 1409–1422. doi:10.1007/s10661-011-2050-6.

    Article  Google Scholar 

  • COST (2014). Action FP1001: improving data and information on the potential supply of wood resources: a European approach from multisource national forest inventories (USEWOOD). http://www.cost.eu/COST_Actions/fps/Actions/FP1001. Accessed 15/10/2014.

  • Cumming, A. B., Nowak, D. J., Twardus, D., Hoehn, R., Mielke, M., & Rideout, R. (2007). National forest health monitoring program, urban forests of Wisconsin: pilot monitoring project 2002 (N. A. S. a. P. Forestry, Trans.). (pp. 40). Newton Square: USDA, Forest Service.

  • Cumming, A. B., Twardus, D. B., & Nowak, D. J. (2008). Urban forest health monitoring: large scale assessments in the United States. Arboriculture & Urban Forestry, 34, 341–346.

    Google Scholar 

  • David, C. A., & Rhyner, V. (1999). An assessment of windbreaks in central Wisconsin. Agroforestry Systems, 44, 313–331.

    Article  Google Scholar 

  • de Foresta, H., Somarriba, E., Temu, A., Boulanger, D., Feuilly, H., & Gaulthier, M. (2013). Towards the assessment of trees outside of forests. Rome.

  • Ene, L. T., Næsset, E., Gobakken, T., Gregoire, T. G., Ståhl, G., & Nelson, R. (2012). Assessing the accuracy of regional LiDAR-based biomass estimation using a simulation approach. Remote Sensing of Environment, 123, 579–592. doi:10.1016/j.rse.2012.04.017.

    Article  Google Scholar 

  • Eysn, L., Hollaus, M., Schadauer, K., & Pfeifer, N. (2012). Forest delineation based on airborne LIDAR data. Remote Sensing, 4, 762–783. doi:10.3390/rs4030762.

    Article  Google Scholar 

  • FAO (2001). Global forest resource assessment 2000 (F. Department, Trans.). FAO forestry paper (Vol. 140, pp. 511). Rome: FAO.

  • FAO (2006). Global forest resources assessment 2005. Progress towards sustainable forest management (FAO forestry paper, Vol. 147). Rome: FAO.

  • FAO (2010). Global forest resource assessment 2010. Main report. Rome: FAO.

  • FAO (2012). National forest monitoring and assessment—manual for integrated field data collection. Version 3.0 (F. Department, Trans.). NFMA working paper (Vol. 37/E, pp. 188). Rome: FAO.

  • Fattorini, L., Corona, P., Chirici, G., & Pagliarella, M. C. (2015). Design-based strategies for sampling spatial units from regular grids with applications to forest surveys, land use, and land cover estimation. Environmetrics, 26(3), 216–228. doi:10.1002/env.2332.

    Article  Google Scholar 

  • Fehrmann, L., Seidel, D., Krause, B., & Kleinn, C. (2014). Sampling for landscape elements—a case study from Lower Saxony, Germany. Environmental Monitoring and Assessment, 186(3), 1421–1430.

    Article  Google Scholar 

  • Fensham, R. J., & Fairfax, R. J. (2002). Aerial photography for assessing vegetation change: a review of applications and the relevance of findings for Australian vegetation history. Australian Journal of Botany, 50, 415–429.

    Article  Google Scholar 

  • Fensham, R. J., & Fairfax, R. J. (2003). Assessing woody vegetation cover change in north-west Australian savanna using aerial photography. International Journal of Wildland Fire, 12, 359–367.

    Article  Google Scholar 

  • Fensham, R. J., Choy, S. J. L., Fairfax, R. J., & Cavallaro, P. C. (2003). Modelling trends in woody vegetation structure in semi-arid Australia as determined from aerial photography. Journal of Environmental Management, 68, 421–436. doi:10.1016/S0301-4797(03)00111-7.

    Article  Google Scholar 

  • Foschi, P. G., & Smith, D. K. (1997). Detecting subpixel woody vegetation in digital imagery using two artificial intelligence approaches. Photogrammetric Engineering and Remote Sensing, 63, 493–499.

    Google Scholar 

  • Fransson, J. E. S., Wallerman, J., Gustavsson, A., & Ulander, L. M. H. (2013). Estimation of stem volume in hemi-boreal forests using airborne low-frequency synthetic aperture radar and Lidar data. IEEE International Geoscience and Remote Sensing Symposium (Igarss), 2013, 161–164.

    Google Scholar 

  • Fridman, J., Holm, S., Nilsson, M., Nilsson, P., Ringvall, A., & Ståhl, G. (2014). Adapting National Forest Inventories to changing requirements—the case of the Swedish National Forest Inventory at the turn of the 20th century. Silva Fennica, 48, 1–29. doi:10.14214/sf.1095.

    Article  Google Scholar 

  • GLC. The global land cover map for the year 2000, (2003). GLC 2000 database. In, 2003

  • Gove, J. H., Ringvall, A., Stahl, G., & Ducey, M. J. (1999). Point relascope sampling of downed coarse woody debris. Canadian Journal of Forest Research-Revue Canadienne De Recherche Forestiere, 29(11), 1718–1726.

    Article  Google Scholar 

  • Grafström, A. (2012). Spatially correlated Poisson sampling. Journal of Statistical Planning and Inference, 142, 139–147. doi:10.1016/j.jspi.2011.07.003.

    Article  Google Scholar 

  • Grafström, A., & Ringvall, A. H. (2013). Improving forest field inventories by using remote sensing data in novel sampling designs. Canadian Journal of Forest Research, 43, 1015–1022. doi:10.1139/cjfr-2013-0123.

    Article  Google Scholar 

  • Grafström, A., Lundström, N. L. P., & Schelin, L. (2012). Spatially balanced sampling through the pivotal method. Biometrics, 68(2), 514–520.

    Article  Google Scholar 

  • Grafström, A., Saarela, S., & Ene, L. T. (2014). Efficient sampling strategies for forest inventories by spreading the sample in auxiliary space. Canadian Journal of Forest Research, 44, 1156–1164. doi:10.1139/cjfr-2014-0202.

    Article  Google Scholar 

  • Gregoire, T. G., & Valentine, H. T. (2008). Sampling strategies for natural resources and the environment. In (pp. 474). Boca Raton: Chapman & Hall/CRC.

  • Gregoire, T. G., Ståhl, G., Næsset, E., Gobakken, T., Nelson, R., & Holm, S. (2011). Model-assisted estimation of biomass in a LiDAR sample survey in Hedmark County, Norway. This article is one of a selection of papers from Extending Forest Inventory and Monitoring over Space and Time. Canadian Journal of Forest Research, 41, 83–95. doi:10.1139/X10-195.

    Article  Google Scholar 

  • Grosenbaugh, L. (1952). Plotless timber estimates—new, fast, easy. Journal of Forestry, 50, 32–37.

    Google Scholar 

  • Gschwantner, T., Schadauer, K., Vidal, C., Lanz, A., Tomppo, E., di Cosmo, L., et al. (2009). Common tree definitions for national forest inventories in Europe. Silva Fennica, 43, 303–321.

    Article  Google Scholar 

  • Hansen, M. H. (1985). Notes: line intersect sampling of wooded strips. Forest Science, 31, 282–288.

    Google Scholar 

  • Hansen, M. C., DeFries, R. S., Townshend, J. R. G., Sohlberg, R., Dimiceli, C., & Carroll, M. (2002). Towards an operational MODIS continuous field of percent tree cover algorithm: examples using AVHRR and MODIS data. Remote Sensing of Environment, 83, 303–319. doi:10.1016/S0034-4257(02)00079-2.

    Article  Google Scholar 

  • Hansen, M. C., DeFries, R. S., Townshend, J. R. G., Carroll, M., Dimiceli, C., & Sohlberg, R. A. (2003). Global percent tree cover at a spatial resolution of 500 meters: first results of the MODIS vegetation continuous fields algorithm. Earth Interactions, 7, 1–15.

    Article  Google Scholar 

  • Herold, M., Scepan, J., Müller, A., & Günther, S. Object-oriented mapping and analysis of urban land use/cover using IKONOS data. In T. Benes (Ed.), 22nd EARSEL Symposium: geoinformation for European-wide Integration, Praque, 2003 (pp. 531–538): Millpress Rotterdam

  • Holmgren, P., Masakha, E. J., & Sjöholm, H. (1994). Not all African land is being degraded: a recent survey of trees on farms in Kenya reveals rapidly increasing forest resources. Ambio, 23, 390–395.

    Google Scholar 

  • Iles, K., & Smith, N. J. (2006). A new type of sample plot that is particularly useful for sampling small clusters of objects. Forest Science, 52, 148–154.

    Google Scholar 

  • Jenkins, J. C., Chojnacky, D. C., Heath, L. S., & Birdsey, R. A. (2003). National-scale biomass estimators for United States tree species. Forest Science, 49, 12–35.

    Google Scholar 

  • Kleinn, C. (2000). On large-area inventory and assessment of trees outside forests. Unasylva 200, 51, 3–10.

  • Krishnankutty, C. N., Thampi, K. B., & Chundamannil, M. (2008). Trees outside forests (TOF): a case study of the wood production-consumption situation in Kerala. International Forestry Review, 10, 156–164.

    Article  Google Scholar 

  • Kumar, B. M., Suman, J. G., Jamaludheen, V., & Suresh, T. K. (1998). Comparison of biomass production, tree allometry and nutrient use efficiency of multipurpose trees grown in woodlot and silvopastoral experiments in Kerala, India. Forest Ecology and Management, 112, 145–163. doi:10.1016/S0378-1127(98)00325-9.

    Article  Google Scholar 

  • Kumar, A., Singh, K., Lal, B., & Singh, R. (2008). Mapping of apple orchards using remote sensing techniques in cold desert of Himachal Pradesh, India. Journal of the Indian Society of Remote Sensing, 36, 387–392.

    Article  Google Scholar 

  • Kuyah, S., Dietz, J., Muthuri, C., Jamnadass, R., Mwangi, P., Coe, R., et al. (2012). Allometric equations for estimating biomass in agricultural landscapes: I. Aboveground biomass. Agriculture, Ecosystems & Environment, 158, 216–224. doi:10.1016/j.agee.2012.05.011.

    Article  Google Scholar 

  • Lam, T. Y., Kleinn, C., & Coenradie, B. (2011). Double sampling for stratification for the monitoring of sparse tree populations: the example of Populus euphratica Oliv. forests at the lower reaches of Tarim River, Southern Xinjiang, China. Environmental Monitoring and Assessment, 175, 45–61. doi:10.1007/s10661-010-1492-6.

    Article  Google Scholar 

  • Lee, S., & Lathrop, R. G. (2005). Sub-pixel estimation of urban land cover components with linear mixture model analysis and Landsat Thematic Mapper imagery. International Journal of Remote Sensing, 26, 4885–4905.

    Article  Google Scholar 

  • Lefsky, M., & McHale, M. R. (2008). Volume estimates of trees with complex architecture from terrestrial laser scanning. Journal of Applied Remote Sensing, 2, 023521. doi:10.1117/1.2939008.

    Article  Google Scholar 

  • Levin, N., McAlpine, C., Phinn, S., Price, B., Pullar, D., Kavanagh, R. P., et al. (2009). Mapping forest patches and scattered trees from SPOT images and testing their ecological importance for woodland birds in a fragmented agricultural landscape. International Journal of Remote Sensing, 30, 3147–3169. doi:10.1080/01431160802558782.

    Article  Google Scholar 

  • Liknes, G. C., Perry, C. H., & Meneguzzo, D. M. (2010). Assessing tree cover in agricultural landscapes using high-resolution aerial imagery. Journal of Terrestrial Observation, 2, 38–55.

    Google Scholar 

  • Lister, A. J., Scott, C. T., & Rasmussen, S. (2012). Inventory methods for trees in nonforest areas in the Great Plains States. Environmental Monitoring and Assessment, 184, 2465–2474. doi:10.1007/s10661-011-2131-6.

    Article  Google Scholar 

  • Magdon, P., Fischer, C., Fuchs, H., & Kleinn, C. (2014). Translating criteria of international forest definitions into remote sensing image analysis. Remote Sensing of Environment, 149, 252–262. doi:10.1016/j.rse.2014.03.033.

    Article  Google Scholar 

  • McHale, M. R., Burke, I. C., Lefsky, M. A., Peper, P. J., & McPherson, E. G. (2009). Urban forest biomass estimates: is it important to use allometric relationships developed specifically for urban trees? Urban Ecosystems, 12, 95–113. doi:10.1007/s11252-009-0081-3.

    Article  Google Scholar 

  • Meneguzzo, D. C., Liknes, G. C., & Nelson, M. D. (2013). Mapping trees outside forests using high-resolution aerial imagery: a comparison of pixel- and object-based classification approaches. Environmental Monitoring and Assessment, 185, 6261–6275. doi:10.1007/s10661-012-3022-1.

    Article  Google Scholar 

  • Myeong, S., Nowak, D. J., & Duggin, M. J. (2006). A temporal analysis of urban forest carbon storage using remote sensing. Remote Sensing of Environment, 101, 277–282. doi:10.1016/j.rse.2005.12.001.

    Article  Google Scholar 

  • Naesset, E., & Gobakken, T. (2008). Estimation of above- and below-ground biomass across regions of the boreal forest zone using airborne laser. Remote Sensing of Environment, 112(6), 3079–3090.

    Article  Google Scholar 

  • Nair, P. K. R. (2012). Carbon sequestration studies in agroforestry systems: a reality-check. Agroforestry Systems, 86(2), 243–253.

    Article  Google Scholar 

  • Nilsson, S. (2008). The Indian forestry system at a crossroads: an outsider’s view. International Forestry Review, 10, 414–421.

    Article  Google Scholar 

  • Nowak, D. J. (1994). Atmospheric carbon dioxide reduction by Chicago’s urban forest. In E. G. McPherson, D. J. Nowak, & R. A. Rowntree (Eds.), Chicago’s urban forest ecosystem: results of the Chicago urban forest climate project (pp. 83–94). Radnor: U.S. Department of Agriculture, Forest Service, Northeastern Forest Experiment Station.

    Google Scholar 

  • Nowak, D. J., Noble, M. H., Sisinni, S. M., & Dwyer, J. F. (2001). People and trees: assessing the US urban forest resource. Journal of Forestry, 99(3), 37–42.

    Google Scholar 

  • Nowak, D. J., Hoehn, R., Walton, J. T., Crane, D. E., Stevens, J. C., Twardus, D., et al. (2006). Urban forest health monitoring in the United States. Paper presented at the Monitoring Science and Technology Symposium: unifying knowledge for sustainability in the Western Hemisphere Proceedings, Fort Collins.

  • Nowak, D. J., Crane, D. E., Stevens, J. C., Hoehn, R. E., Walton, J. T., & Bond, J. (2008). A ground-based method of assessing urban forest structure and ecosystem services. Arboriculture & Urban Forestry, 34, 347–358.

    Google Scholar 

  • Ouma, Y. O., & Tateishi, R. (2008). Urban-trees extraction from Quickbird imagery using multiscale spectex-filtering and non-parametric classification. ISPRS Journal of Photogrammetry and Remote Sensing, 63(3), 333–351.

    Article  Google Scholar 

  • Pan, Y. D., Birdsey, R. A., Fang, J. Y., Houghton, R., Kauppi, P. E., Kurz, W. A., et al. (2011). A large and persistent carbon sink in the world’s forests. Science, 333(6045), 988–993.

    Article  CAS  Google Scholar 

  • Pan, Y. D., Birdsey, R. A., Phillips, O. L., & Jackson, R. B. (2013). The structure, distribution, and biomass of the world’s forests. Annual Review of Ecology, Evolution, and Systematics, 44(1), 593–622. doi:10.1146/annurev-ecolsys-110512-135914.

    Article  Google Scholar 

  • Perry, C. H., Woodall, C. W., Liknes, G. C., & Schoeneberger, M. M. (2009). Filling the gap: improving estimates of working tree resources in agricultural landscapes. Agroforestry Systems, 75, 91–101. doi:10.1007/s10457-008-9125-6.

    Article  Google Scholar 

  • Riemann, R. (2003). Pilot inventory of FIA plots traditionally called “nonforest” (p. 50). Newton Square: USDA, Forest Service.

    Google Scholar 

  • Rodriguez, F., Lizarralde, I., Fernandez-Landa, A., & Condes, S. (2014). Non-destructive measurement techniques for taper equation development: a study case in the Spanish Northern Iberian Range. European Journal of Forest Research, 133(2), 213–223.

    Article  Google Scholar 

  • Rutzinger, M., Höfle, B., Hollaus, M., & Pfeifer, N. (2008). Object-based point cloud analysis of full-waveform airborne laser scanning data for urban vegetation classification. Sensors, 8, 4505–4528. doi:10.3390/s8084505.

    Article  Google Scholar 

  • Särndal, C. E., Swensson, B., & Wretman, J. (1992). Model assisted survey sampling. New York: Springer.

    Book  Google Scholar 

  • Schnell, S., Altrell, D., Ståhl, G., & Kleinn, C. (2015a). The contribution of trees outside forests to national tree biomass and carbon stocks—a comparative study across three continents. Environmental Monitoring and Assessment, 187, 4197. doi:10.1007/s10661-014-4197-4.

    Article  Google Scholar 

  • Schnell, S., Ene, L. T., Grafström, A., Nord-Larsen, T., & Ståhl, G. (2015b). Sampling strategies for large area inventories of trees outside forests—a simulation study. Canadian Journal of Forest Research.

  • Schumacher, J., & Nord-Larsen, T. (2014). Wall-to-wall tree type classification using airborne lidar data and CIR images. International Journal of Remote Sensing, 35(9), 3057–3073.

    Article  Google Scholar 

  • Sheeren, D., Bastin, N., Ouin, A., Ladet, S., Balent, G., & Lacombe, J.-P. (2009). Discriminating small wooded elements in rural landscape from aerial photography: a hybrid pixel/object-based analysis approach. International Journal of Remote Sensing, 30, 4979–4990. doi:10.1080/01431160903022928.

    Article  Google Scholar 

  • Shvidenko, A., Barber, C. V., & Persson, R. (2005). Forest and woodland systems. In H. Rashid, R. Scholes, & N. Ash (Eds.), Ecosystems and human well-being: current state and trends (pp. 587–621). Washington: Island Press.

    Google Scholar 

  • Small, C., & Lu, J. W. T. (2006). Estimation and vicarious validation of urban vegetation abundance by spectral mixture analysis. Remote Sensing of Environment, 100(4), 441–456.

    Article  Google Scholar 

  • Smeets, E. M. W., & Faaij, A. P. C. (2007). Bioenergy potentials from forestry in 2050—an assessment of the drivers that determine the potentials. Climatic Change, 81(3–4), 353–390.

    Article  CAS  Google Scholar 

  • Ståhl, G. (1998). Transect relascope sampling—a method for the quantification of coarse woody debris. Forest Science, 44, 58–63.

    Google Scholar 

  • Ståhl, G., Allard, A., Esseen, P. A., Glimskar, A., Ringvall, A., Svensson, J., et al. (2011a). National Inventory of Landscapes in Sweden (NILS)—scope, design, and experiences from establishing a multiscale biodiversity monitoring system. Environmental Monitoring and Assessment, 173(1–4), 579–595. doi:10.1007/s10661-010-1406-7.

    Article  Google Scholar 

  • Ståhl, G., Holm, S., Gregoire, T. G., Gobakken, T., Næsset, E., & Nelson, R. (2011b). Model-based inference for biomass estimation in a LiDAR sample survey in Hedmark County, Norway. Canadian Journal of Forest Research, 41, 96–107. doi:10.1139/X10-161.

    Article  Google Scholar 

  • Stenberg, P., Korhonen, L., & Rautiainen, M. (2008). A relascope for measuring canopy cover. Canadian Journal of Forest Research, 38, 2545–2550. doi:10.1139/X08-081.

    Article  Google Scholar 

  • Strahler, A. H., Woodcock, C. E., & Smith, J. A. (1986). On the nature of models in remote-sensing. Remote Sensing of Environment, 20(2), 121–139.

    Article  Google Scholar 

  • Strand, L. (1957). “Relaskopisk” høde- og kubikmassebestemmelse. [Determination of height and volume by relascope]. Norsk Skogbruk, 3, 535–538.

    Google Scholar 

  • Straub, C., Weinacker, H., & Koch, B. (2008). A fully automated procedure for delineation and classification of forest and non-forest vegetation based on full waveform laser scanner data. Paper presented at the ISPRS Archives–Volume XXXVII Part B8, Beijing,

  • Tansey, K., Chambers, I., Anstee, A., Denniss, A., & Lamb, A. (2009). Object-oriented classification of very high resolution airborne imagery for the extraction of hedgerows and field margin cover in agricultural areas. Applied Geography, 29, 145–157. doi:10.1016/j.apgeog.2008.08.004.

    Article  Google Scholar 

  • Taubenbock, H., Esch, T., Wurm, M., Roth, A., & Dech, S. (2010). Object-based feature extraction using high spatial resolution satellite data of urban areas. Journal of Spatial Science, 55(1), 117–132.

    Article  Google Scholar 

  • Tewari, V. P., Sukumar, R., Kumar, R., & Gadow, K. (2014). Forest observational studies in India: past developments and considerations for the future. Forest Ecology and Management, 316, 32–46. doi:10.1016/j.foreco.2013.06.050.

    Article  Google Scholar 

  • Thornton, M. W., Atkinson, P. M., & Holland, D. A. (2006). Sub‐pixel mapping of rural land cover objects from fine spatial resolution satellite sensor imagery using super‐resolution pixel‐swapping. International Journal of Remote Sensing, 27, 473–491. doi:10.1080/01431160500207088.

    Article  Google Scholar 

  • Thornton, M. W., Atkinson, P. M., & Holland, D. A. (2007). A linearised pixel-swapping method for mapping rural linear land cover features from fine spatial resolution remotely sensed imagery. Computers & Geosciences, 33(10), 1261–1272.

    Article  Google Scholar 

  • Tomppo, E., Heikkinen, J., Henttonen, H. M., Ihalainen, A., Katila, M., Mäkelä, H., et al. (2011). Designing and conducting a forest inventory—case: 9th National Forest Inventory of Finland (Managing forest ecosystems, Vol. 22). Dordrecht: Springer.

    Book  Google Scholar 

  • Tomppo, E., Malimbwi, R., Katila, M., Makisara, K., Henttonen, H. M., Chamuya, N., et al. (2014). A sampling design for a large area forest inventory: case Tanzania. Canadian Journal of Forest Research-Revue Canadienne De Recherche Forestiere, 44(8), 931–948.

    Article  Google Scholar 

  • Turner, W. R., Nakamura, T., & Dinetti, M. (2004). Global urbanization and the separation of humans from nature. Bioscience, 54(6), 585–590. doi:10.1641/0006-3568(2004)054[0585:GUATSO]2.0.CO;2.

    Article  Google Scholar 

  • UN. (2012). World urbanization prospects: the 2011 revision (p. 318). New York: United Nations.

    Google Scholar 

  • Vesa, L., Malimbwi, R. E., Tomppo, E., Zahabu, E., Maliondo, S., Chamuya, N., et al. (2010). National forestry resources monitoring and assessment of Tanzania. Field Manual. Biophysical survey (F. a. B. Division, Trans.). NFORMA Document (pp. 108). Dar es Salaam, Tanzania.

  • Walker, J. S., & Briggs, J. M. (2007). An object-oriented approach to urban forest mapping in Phoenix. Photogrammetric Engineering and Remote Sensing, 73(5), 577–583.

    Article  Google Scholar 

  • Walton, J. T. (2008). Difficulties with estimating city-wide urban forest cover change from national, remotely-sensed tree canopy maps. Urban Ecosystems, 11, 81–90.

    Article  Google Scholar 

  • Walton, J. T., Nowak, D. J., & Greenfield, E. J. (2008). Assessing urban forest canopy cover using airborne or satellite imagery. Arboriculture & Urban Forestry, 34, 334–340.

    Google Scholar 

  • Wiseman, G., Kort, J., & Walker, D. (2009). Quantification of shelterbelt characteristics using high-resolution imagery. Agriculture, Ecosystems & Environment, 131, 111–117. doi:10.1016/j.agee.2008.10.018.

    Article  Google Scholar 

  • Yoon, T. K., Park, C. W., Lee, S. J., Ko, S., Kim, K. N., Son, Y., et al. (2013). Allometric equations for estimating the aboveground volume of five common urban street tree species in Daegu, Korea. Urban Forestry & Urban Greening, 12(3), 344–349.

    Article  Google Scholar 

  • Zanne, A. E., Lopez-Gonzalez, G., Coomes, D. A., Ilic, J., Jansen, S., Lewis, S. L., et al. (2009). Data from: towards a worldwide wood economics spectrum.

  • Zhang, Y. (2001). Texture-integrated classification of urban treed areas in high-resolution colour-infrared imagery. Photogrammetric Engineering and Remote Sensing, 67, 1359–1365.

  • Zhou, W., & Troy, A. (2008). An object-oriented approach for analysing and characterizing urban landscape at the parcel level. International Journal of Remote Sensing, 29, 3119–3135. doi:10.1080/01431160701469065.

    Article  Google Scholar 

  • Zhou, X. H., Brandle, J. R., Schoeneberger, M. M., & Awada, T. (2007). Developing above-ground woody biomass equations for open-grown, multiple-stemmed tree species: shelterbelt-grown Russian-olive. Ecological Modelling, 202(3–4), 311–323.

    Article  Google Scholar 

  • Zomer, R. J., Coe, R., Place, F., van Noordwijk, M., & Xu, J. C. (2014). Trees on farms: an update and reanalysis of agroforestry’s global extent and socio-ecological characteristics. Bogor: World Agroforestry Centre (ICRAF) Southeast Asian Regional Program.

    Google Scholar 

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Acknowledgments

We are grateful to two anonymous reviewers for the valuable suggestions helping to improve an earlier version of the paper.

Compliance with ethical standards

The authors declare that they have followed the rules of good scientific practice. All authors have contributed sufficiently to the scientific work and have followed the ethical standards of Environmental Monitoring and Assessment.

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The authors further declare that they have no conflict of interest.

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Schnell, S., Kleinn, C. & Ståhl, G. Monitoring trees outside forests: a review. Environ Monit Assess 187, 600 (2015). https://doi.org/10.1007/s10661-015-4817-7

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