DATA COLLECTION FOR PRECISION FORESTRY: THE ROLE OF AN AUTOMATIC WEATHER STATION PROGRAMME

Transcription

1 DATA COLLECTION FOR PRECISION FORESTRY: THE ROLE OF AN AUTOMATIC WEATHER STATION PROGRAMME Mark Nrris-Rgers, RasterMatics Intl. Heyns Ktze, Mndi Ltd Phillemn Sithle, SASRI

2 Intrductin Precisin frest management requires: A base f reliable, relevant infrmatin being cllected An understanding f hw this data impacts tree grwth, frest planning, and frest peratins Fundamental driver f tree grwth and yield: availability f rain-fed misture and related temperature regimes Recrding f rainfall; submitting this data t SAWS was a daily rutine fr fresters Majr decline in the density and quality f this vital infrmatin surce Changing rle f Fresters Fewer Fresters n plantatin Exacerbated by difficulties in btaining verifiable, cmprehensive quality weather data frm the Suth African Weather Service (SAWS) Institute fr Cmmercial Frestry Research (ICFR) has t purchase, at great expense, annual data frm SAWS Cnsiderable data gaps need t be patched This takes time and effrt - adds t its already high cst Precisin Frestry Sympsium, Stellenbsch 2017 Autmatic Weather Statin Prgramme PAGE 1

3 Backgrund Drught: Highlighted the Frest Industry s expsure t this lack f weather data Very difficult t: Quantify the grwth impact Relate this t reliable weather data t mdel the impacts In stark cntrast, Sugar Industry: Has a dedicated Meterlgical team Have a cmprehensive weather mnitring prgramme acrss their sugar areas Als develped autmatic algrithms t validate and fill in missing data (such as where an instrument might malfunctin) Researched impact f weather data n the physilgy f cane grwth Utilise this infrmatin t infrm strategic decisin making (e.g. in the last seasn did nt pen a Sugar Mill in ne area because predicted insufficient sugarcane available t feed that mill) This infrmatin available via a web-service, which prvides relevant infrmatin t allw farmers t make gd management decisins. Precisin Frestry Sympsium, Stellenbsch 2017 Autmatic Weather Statin Prgramme PAGE 2

4 Backgrund cnt Drught: 2015 Mndi partnered with SASRI t cllabrate in the AWS Prgramme Deplyed 29 autmatic weather statins acrss the Mndi landing hldings SASRI manages the prgramme n Mndi s behalf Mndi gained access t the full SASRI weather database, expertise and equipment management prgramme Large gegraphic verlap between the frest and sugar industries, particularly in KwaZulu-Natal (KZN) - Sugar Industry benefits frm additinal weather statin cverage. Precisin Frestry Sympsium, Stellenbsch 2017 Autmatic Weather Statin Prgramme PAGE 3

5 Methdlgy Equipment Selectin: : Davis electrnic weather statins - fire prtectin purpses during the fire seasn (May t Octber). Data des nt have a validatin/patching system Des nt prvide a rbust data surce that is required fr grwth and yield study purpses SASRI system required use f same equipment - Campbell Scientific weather statin equipment Data Cllectin and Transfer: AWS cnsists f a data lgger cnnected t sensrs n tripd munting measuring: Temperature; Relative Humidity; Wind Speed/Directin; Rainfall; Slar radiatin Data is autmatically transmitted via a wireless mdem cnnected t the cell phne netwrk (using a standard SIM card n a data package) Data is sent t SASRI s centralised web-service, using LggerNet sftware package fr further validatin and prcessing Requires a cnnectin t a pwer surce (Eskm mains) in rder t prvide pwer t a battery charger (Slar panels are a security risk) Rain gauge munted n separate ple but cnnected t lgger by cable Precisin Frestry Sympsium, Stellenbsch 2017 Autmatic Weather Statin Prgramme PAGE 4

6 Campbell Scientific Autmatic Weather Statin vane and anemmeter wind sentry pyranmeter temperature and relative humidity prbes CR800 data lgger; Charger Unit; Wireless Mdem with SIM self-emptying tipping bucket rain gauge Precisin Frestry Sympsium, Stellenbsch 2017 Autmatic Weather Statin Prgramme PAGE 5

7 Methdlgy Site Selectin: Balance between btaining the maximum cverage but at an affrdable cst (equipment cst ~R80 000/site) Site criteria: Security; Mains Pwer Surce; Cell Phne Data Cverage Open areas away frm buildings; trees t prevent shadws (slar radiatin), interference with wind flw (speed and directin) interference with rainfall Grass grund cver t minimise false temperature readings lcal staff available (usually the lcal frester) t mnitr the equipment pwer failures/tripping, rain gauge being blcked by leaves, bird drppings etc. ther incidental events that might interfere with the cntinuus peratin f the weather statin Selectin dne in cnsultatin with SASRI - cmplement their netwrk Gaps in SASRI cverage Mndi site selected; vice versa Nn-sugar areas: Mndi sites t cver range f weather patterns Precisin Frestry Sympsium, Stellenbsch 2017 Autmatic Weather Statin Prgramme PAGE 6

8 Methdlgy Site Establishment: Field visits t cnfirm the selectin criteria were met fr each site Each site Fenced ff Cncrete ft pads laid fr the weather statin tripd munting Rain gauge munting ple with cnduit fr lgger cable Pwer cables and islatr switches installed Dne by prfessinal fencing cmpanies prperly cnstructed infrastructure t prvide sufficient prtectin Once the sites were erected SASRI Meterlgical Staff undertk the actual weather statin installatin Critical parameters required t ensure accurate data cllectin Precisin Frestry Sympsium, Stellenbsch 2017 Autmatic Weather Statin Prgramme PAGE 7

9 Methdlgy Data Prcessing: Weather data based predictins, infrmatin r decisins significantly influenced by the quality f the input data Tw cmplimentary peratins t ensure data integrity: On-site sensr maintenance and calibratins by trained SASRI technicians Bi-annual inspectins; Ad-hc fr equipment failure Rigrus cmputer-based autmated data quality check and validatin system Identify missing, errneus and suspect data Autmatically replaced by data derived frm related climatic variables, neighburing sites r frm histrical recrds. Check system ccurs daily with each data uplading prcedure Tw check levels: Acceptable range limits Upper and lwer limits are set fr all variables based n ne criteria r a cmbinatin f thery, histrical recrds and specificatins f the recrding instrument. Limits all values t within an acceptable range e.g. relative humidity has t be within the range f zer t 100 % Precisin Frestry Sympsium, Stellenbsch 2017 Autmatic Weather Statin Prgramme PAGE 8

10 Methdlgy Data Prcessing cnt.: Internal and spatial cnsistency levels cnsistency at a given site t ensure that there is agreement between related/assciated variables e.g. minimum temp. has t be always less than maximum temp. The rate f change f values between successive days is als checked t make sure it s plausible Trends between neighburing sites are als validated fr cnsistency (may be dne fr daily, weekly r mnthly values depending n the variable in questin) Applicatin t Grwth and Yield Data Once sufficient weather data has been cllected - applied t research n impact f weather cycles n tree grwth. Link this data t dendrmeter trials that are currently being initiated in cnjunctin with the University f Stellenbsch Mdelling apprach similar t that prpsed by Sclfr et al (2016) is planned Precisin Frestry Sympsium, Stellenbsch 2017 Autmatic Weather Statin Prgramme PAGE 9

11 Cnclusin Cnclusin Lng-term prject Significant steps have been achieved in a relatively shrt perid f time Extensive infrastructure has been established Linked int a prven data capture/validatin/archiving prgramme in terms f SASRI s weather data cllectin facility. Allw Grwth and Yield Researchers t fast-track their investigatins int the impacts f weather cycles n frest grwth and yield A fundamental input int a Precisin Frestry Apprach! Precisin Frestry Sympsium, Stellenbsch 2017 Autmatic Weather Statin Prgramme PAGE 10

12 Acknwledgements: Keith Cllings; Jarmain Chetty SASRI Russell Mrkel - Mndi Thank yu fr attending Questins? FORWARD - LOOKING STATEMENTS It shuld be nted that certain statements herein which are nt histrical facts, including, withut limitatin thse regarding expectatins f market grwth and develpments; expectatins f grwth and prfitability; and statements preceded by believes, expects, anticipates, fresees, may r similar expressins, are frward-lking statements. Since these statements are based n current knwledge, plans, estimates and prjectins, they invlve risks and uncertainties which may cause actual results t materially differ frm thse expressed in such frward-lking statements. Varius factrs culd cause actual future results, perfrmance r events t differ materially frm thse described in these statements. Such factrs include in particular but withut any limitatin: (1) perating factrs such as cntinued success f manufacturing activities and the achievement f efficiencies therein, cntinued success f prduct develpment plans and targets, changes in the degree f prtectin created by Grup s patents and ther intellectual prperty rights, the availability f capital n acceptable terms; (2) industry cnditins, such as strength f prduct demand, intensity f cmpetitin, prevailing and future glbal market prices fr the Grup s prducts and raw materials and the pricing pressures theret, financial cnditin f the custmers, suppliers and the cmpetitrs f the Grup, ptential intrductin f cmpeting prducts and technlgies by cmpetitrs; and (3) general ecnmic cnditins, such as rates f ecnmic grwth in the Grup s principal gegraphical markets r fluctuatins f exchange rates and interest rates. Mndi des nt a) assume any warranty r liability as t accuracy r cmpleteness f the infrmatin prvided herein b) undertake t review r cnfirm analysts expectatins r estimates r t update any frward-lking statements t reflect events that ccur r circumstances that arise after the date f making any frward-lking statements. Precisin Frestry Sympsium, Stellenbsch 2017 Autmatic Weather Statin Prgramme PAGE 11