Tropical monsoon forests, also called tropical seasonal forests or tropical deciduous forests in other literatures, extend much more tropical landmass than rainforests and savannas, occupying around 40% of tropical forests (Food and Agriculture Organization, 2001).
In terms of tropical monsoon forests, it's the most important characteristic that the leaves of most vegetation will sprout and defoliate phenologically. The deciduousness situation is mainly determined by the seasonality of local rainfall (Borchert, 1994), which means that the rainfall of monsoon forests reaches highest during the wet season (usually from May to October) and lowest during the dry season (usually from November to next year's April)(Tanaka et al, 2008). The leaf-out and leaf-fall process may affect water, energy and carbon balance of tropical zones, so the monsoon forests play a significant role in modeling tropical CO2-cycle and climate change (Ito, 2008), especially when the ecosystem is posed to a grave degraded threat(Janzen,1988), it may lead to deciduousness change followed by significant biophysical changes of tropical environment. Vice versa, the knowledge of the distribution of a land cover is undoubtedly required when we model the biophysical change of an ecosystem in response to global change (Defries et al, 1994). However, relatively less work has been documented on the spatial pattern of this large-area tropical monsoon forests (Murphy et al, 1986; Trejo et al, 2000; Yoshifuji et al, 2006).
Get your grade
or your money back
using our Essay Writing Service!
(2)é¥æ„Ÿ-æ³•çš„æˆåƒä¼˜åŠ¿ï¼Œçƒå¸¦å£é›¨-åœ¨ç›®å‰çš„åŸºäºŽé¥æ„ŸåœŸ°åˆ†ç±»ç³»ç»Ÿä¸°ä½,åŸºäºŽç»Ÿè®¡çš„åˆ†ç±»é¥æ„Ÿåˆ†ç±»-æ³•çš„ç¼ºç‚¹ã€‚å› æ¤æˆ‘ä»¬è¿™ç¯‡-ç« æå‡ºäº†ä¸€ç§å›žå½’°ç†å¦ç‰¹å¾ï¼Œåˆ©ç”¨ç®€å•°ç†å¦çŸ¥è¯†é‡-è½å¶ä¿¡æ¯å¯¹çƒå¸¦å£é›¨-æˆå›¾çš„-æ³•ã€‚
Remote sensing method provides spatially large-scale data and temporally frequent imaging of land covers information that can be used to derive the seasonality of spectral variation and map land covers by certain classification method(Bohlman, 2010).At present, although many land cover products have been brought forth with the support of more and more satellite data, none of them classifies monsoon forests as an individual ecosystem. For instance, monsoon forest is integrated in broadleaf deciduous forests and woodlands in UMD product and broadleaf deciduous closed forests in GLC2000 product (Hansen et al, 2000; Bartholomé et al, 2005). Due to monsoon forests can be seen as a transitional type between rainforests and savannas, the forest may comprise kinds of vegetation components. This feature determines the complexity of spectra and spatially heterogeneous so that it's not easy to distinguish monsoon forests from other land covers by traditional supervised or unsupervised classification methods that base on statistics (Jung et al, 2006).
In this paper, we develop a new method based on simple geographic knowledge for mapping tropical monsoon forests. Normalized difference vegetation index (NDVI), defined as (NIR-RED)/ (NIR+RED), where NIR and RED refer to near-infrared and red band reflectance respectively, is widely exploited to estimate regional or global vegetation status. To some extent, the scope of existed vegetation can be reflected by maximum value composite (MVC) and the scope of evergreen forests can be shown by NDVI minimum value composite. In this way, NDVI difference between maximum and minimum values will quantify the distribution of deciduous forests. With minimum Land surface Temperature (LST) limiting the tropical zone, the whole tropical monsoon forests will be derived.
1. Food and Agriculture Organization, 2001. Global Forest Resources Assessment 2000. FAO Forestry Paper 140, Rome.
R.Borchert. Soil and stem water storage determine phenology and distribution of tropical dry forest trees, 1994. Ecology 75, 1437-1449.
I.Trejo, R. Dirzo. Deforestation of seasonally dry tropical forests: a national and local
analysis in Mexico, 2000.Biological Conservation 94:133−142.[ Semi-deciduous to semi-evergreen forest are not considered as Seasonal Dry Tropical Forests.]
2. N.Tanaka, T.Kume, N.Yoshifuji, K.Tanaka, H.Takizawa, K.Shiraki et al. A review of evapotranspiration estimates from tropical forests in Thailand and adjacent regions, 2008, Agricultural and Forest Meteorology, 148, 807−819.
3. E.Ito, M.Araki, B.Tith, S.Pol, C.Trotter, M.Kanzaki, S.Ohta. Leaf-shedding phenology in lowland tropical seasonal forests of Cambodia as estimated from NOAA satellite images, 2008, IEEE Transactions on Geoscience and Remote Sensing, 46, 2867-2871. [Leaf regrowth begin in the middle of the dry season.]
4. D.Janzen. Tropical dry forests: The most endangered major tropical ecosystems , 1988 , Biodiversity. National Academy Press, pp. 130-137.
5. R.DeFries, J.Townshend. NDVI-derived land cover classifications at a global scale, 1994, International Journal of Remote Sensing, 15, 3567-3586.[The NDVI profiles may vary from continent to continent for a given cover type; southern savannas display a similar temporal profile to broadleaf deciduous forests]
Always on Time
Marked to Standard
6. P.Murphy, A. Lugo. Ecology of tropical dry forest, 1986, Annual Review of Ecology and Systematics, 17, 67-88.
7. N.Yoshifuji, T.Kumagai, K.Tanaka, N.Tanaka, H.Komatsu, M.Suzuki, C.Tantasirin, 2006. Inter-annual variation in growing season length of a tropical seasonal forest in northern Thailand. Forest Ecology and Management. 229, 333-339.
8. S.Bohlman. Landscape patterns and environmental controls of deciduousness in forests of central Panama, 2010, Global Ecology and Biogeography, 19,376-385.
9. M.Hansen, R.Defries, J.Townshend, R.Sohlberg. Global land cover classification at 1 km spatial resolution using a classification tree approach, 2000, International Journal of Remote Sensing, 21,1331- 1364.
10. E.Bartholomé, A.Belward. GLC2000: a new approach to global land cover mapping from Earth Observation data, 2005, International Journal of Remote Sensing, 26: 1959 - 1977.
11. M.Jung, K.Henkel, M.Herold, G.Churkina. Exploiting synergies of global land cover products for carbon cycle modeling, 2006, Remote Sensing of Environment, 101, pp. 534-553.