Arturo Sanchez-Azofeifa, PhD, PEng Cassidy Rankine, Gilberto Zonta-Pastorello Centre for Earth Observation Sciences (CEOS) Earth and Atmospheric

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1 Arturo Sanchez-Azofeifa, PhD, PEng Cassidy Rankine, Gilberto Zonta-Pastorello Centre for Earth Observation Sciences (CEOS) Earth and Atmospheric Sciences Department University of Alberta Microsoft WSN Workshop Sao Paulo, Brazil November 2010

2 What do we know? Well Very little to start with (although 47% of the tropics are tdfs). Dry forests lag on long term studies aimed to understand their response to climate change. In fact, dry forest lag on a ratio of 1:300 scientific papers when compared with tropical rainforests. Ecological studies in dry forestes are systematic in two regions: Chamela- Cuixmala biosphere reserve, Mexico; and Santa Rosa National Park; Costa Rica. Tropical Forests are not considered part of any global networks aimed to link climate change observations and models to phenological response; nor are part of long term monitoring efforts. Tropical dry forests present well define phenological signals allowing for unique opportunities to evaluate their response to climate change and specially drought effects.

3 Differential Response of Tropical Dry Forests to Climate Change

4 Enviro-Net Project Seven Sites Argentina, Brazil (3), Canada, Costa Rica, Mexico 36 Deployments 14 Wired Phenology Towers logger & 4-8 sensors 12 Wired Ground Deployments logger & 3-8 sensors 2 Wireless Phenology Towers logger, node & 6 sensors 8 Wireless Ground Deployments logger, 5-10 node, sensors

5 Wireless Optical Phenology Systems (WOPS ) 5-10m above canopy (5-20m total height) Physical Variables Measured: Air/soil temperature Air/soil humidity Solar Radiation Photosynthetically Active Radiation (PAR) Derived Variables: Vegetation Indexes APAR, fapar Vapour Pressure Deficit Carbon and water fluxes

6 Wireless Ground Deployments Nodes: Current deployments ( ): 5 20 nodes per deployment Planned deployments ( ): up to 100 nodes per deployment

7 WSN Understory PAR Understory light environment highly variable Networks of ~12 wireless PAR sensors capture this variable nature to better estimate FAPAR

8 Chamela, Mexico Sampling Design

9 Serra do Cipo, Minas Gerais, Brazil Soil temperature station WSN Collector: PAR, T/RH WSN Aggregator, Collector: PAR, T/RH Phenology tower

10 Mata Seca State Park, Minas Gerais, Brazil CC4 MC6 IC4 MC8 IC7 ETCP Phenology tower Wireless sensor network (PAR, T/RH) Understory meteorological station (rain, T/RH, PAR, Soil Moisture) Understory Wireless sensor network PAR experiment Phenology digital camera & logger Meteorological station

11 Data management within Wireless Sensor Networks (WSNs), e.g., algorithms for in-network query processing Exploiting the (potential) synergy between networking issues and data management within WSNs, e.g., logical topology and packet scheduling oriented by query semantics Indexing of spatio-temporal data and time-series with a focus on re-using sound and industrially tested technologies, e.g., B + -trees Data mining, web data and social network analysis

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14 Mid-Day Average EVI2 Daily Sum Precipitation (mm) Chamela: Phenology Cues Precipitation EVI2 Precipitation 0, , , , , , ,15 0 Day of Year

15 Mid-Day Average EVI2 Percent Soil Moisture Chamela: Phenology Cues Soil Moisture EVI2 Soil Moisture 0,75 0,3 0,65 0,25 0,55 0,2 0,45 0,15 0,35 0,1 0,25 0,05 0,15 0 Day of Year

16 Mid-Day Average EVI2 Vapour Pressure Deficit (hpa) Chamela: Phenology Cues Vapour Pressure Deficit EVI2 VPD 0, , , , ,35 6 0, ,15 0 Day of Year

17 Mid-Day Canopy EVI2 Canopy Albedo (PAR Out/PAR In) Chamela, Mexico Albedo Shifts EVI2 Albedo 0,75 0,095 0,65 0,085 0,55 0,075 0,45 0,065 0,35 0,055 0,25 0,045 0,15 0, Day of Year

18 Mid-Day Canopy EVI2 Chamela: Phenology Cues Phenology Shifts 0, ,65 0,55 0,45 0,35 0,25 0,15 Day of Year

19 Canopy EVI2 Daily Sum Precipitation (mm) Mata Seca, Minas Gerais, Brazil Precipitation 0,75 Precipitation Canopy EVI2 80 0, , , , , ,15 0 Day of Year (DDD YY)

20 Canopy EVI2 Soil Moisture (%) Mata Seca, Minas Gerais, Brazil Soil Moisture 0,75 Canopy EVI2 Soil Moisture 25,0% 0,65 20,0% 0,55 15,0% 0,45 0,35 10,0% 0,25 5,0% 0,15 0,0% Day of Year (DDD YY)

21 Canopy EVI2 Vapour Pressure Deficit (hpa) Mata Seca, Minas Gerais, Brazil Vapour Pressure Deficit Canopy EVI2 VPD 0, , , ,45 0,35 0, ,15 0 Day of Year (DDD YY) VPD has strong implications for plant physiology and is the earliest micro-climatic cue for leaf flush at this site

22 Canopy EVI2 Canopy Albedo (PAR Out/PAR In) Mata Seca, Minas Gerais, Brazil Alberdo Shifts 0,75 Canopy EVI2 Canopy Albedo 0,08 0,65 0,55 0,075 0,07 0,065 0,06 0,45 0,35 0,25 0,15 0,055 0,05 0,045 0,04 0,035 0,03 Day of Year (DDD YY) Important implications for phenological remote sensing

23 Chamela Tower NDVI WSN fapar This Year s Leaf Flush in Chamela 0,9 0,8 0,7 1 0,9 0,8 0,7 0,6 0,5 0,4 0,3 0,2 Tower NDVI (Wilson) 0,6 0,5 0,4 0,3 0,2 0,1 0 Day of Year

24 Tower NDVI Tower NDVI Chamela, Mexico Primary TDF 0,8 0,75 0,7 0,65 0,6 0,55 0,5 R² = ,45 0,4 WSN fapar and Tower NDVI have a strong linear relationship 0,35 0,3 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 0,8 0,75 WSN fapar 0,7 R² = ,65 Mata Seca, Brazil (Intermediate stage TDF) 0,6 0,55 0,5 0,45 0,4 0,7 0,75 0,8 0,85 0,9 0,95 WSN fapar

25 Conclusion WSN for environmental monitoring are providing important information on cues controlling phenological processes in tropical dry forests. Not all dry forests follow the common soil moisture model, other sites indicate less dependence on this variable. New data analysis and visualization tools are necessary to handle MASSIVE amounts of information specially on real time transmissions.

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