THAI NGUYEN UNIVERSITY UNIVERSITY OF AGRICULTURAL AND FORESTRY TRAN HOANG SON DIFFERENTIATING CRESTED WHEATGRASS (AGROPYRON CRISTATUM) IN SASKATCHEWAN LANDING PROVINCIAL PARK, CANADA WITH REMOTE SENSING BACHELOR THESIS Study Mode : Full-time Major : Environmental Science and Management Faculty : International Programs Office Batch : K47 Thai Nguyen, May 2020 DOCUMENTATION PAGE WITH ABSTRACT Thai Nguyen University of Agriculture and Forestry Degree Program Bachelor of Environmental Science and Management Student name Tran Hoang Son Student ID DTN1554290027 Thesis Title Differentiating Crested Wheatgrass (Agropyron Cristatum) in Saskatchewan Landing Provincial Park, Canada with Remote Sensing Supervisor (s) Assoc Prof Tran Van Dien Supervisor’s Signature Abstract: Invasive species are a topic of interest to researchers, soil resource managers and environmentalists Exotic grass species threaten the biodiversity and carrying capacity of an ecosystem, and cause negative impacts on the livelihoods of native plants To date, there has been no effective method for detecting exotic species in mixed prairie ecosystems This project focuses on the use of remote sensing data (biophysical, hyperspectral) to distinguish Crested Wheatgrass (CW) (Agropyron cristatum) in Saskatchewan Landing Provincial Park (SLPP), Saskatchewan, Canada The objectives are to: 1) Investigate the spectral characteristics of Crested Wheatgrass from ground hyperspectral data; 2) Find the best vegetation indices to differentiate Crested Wheatgrass; and 3) Investigate the behavior of Crested Wheatgrass in different Grazing regimes Hyperspectral data were used to indicate the spectral characteristics of Crested Wheatgrass Besides, reflectance at red and near infrared and green wavelengths were used to calculate Vegetation Indices: NDVI (Normalized Difference Vegetation Index), RDVI (Renormalized Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index), MSAVI (Modified Soil Adjusted Vegetation Index), PSRI (Plant Senescence Reflectance Index) The results of Tukey Post Hoc Test showed that NDVI is the best Vegetation Index to distinguish Crested Wheatgrass but using Vegetation Indices is not the optimal method to evaluate behavior of Crested Wheatgrass in different grazing regimes in this study i Invasive species, Native Plants, Crested Wheatgrass (Agropyron Keywords: cristatum), Saskatchewan Landing Provincial Park, Hyperspectral Data, Wavelength, Reflectance, Vegetation Indices, NDVI, RDVI, SAVI, MSAVI, PSRI Number of pages: 55 Date of Submission: May 15, 2020 ii ACKNOWLEDGEMENT First and foremost, I wish to express my endless thanks and gratefulness to my supervisor Dr Xulin Guo Her kind support and continuous advices went through the process of completion of my thesis at University of Saskatchewan I thank Saskatchewan Landing Provincial Park for their cooperation and support with this project in providing an ideal location for field data collection Moreover, I want to express my gratefulness to Ms Thuy Doan for her enthusiastic help in providing valuable documents Last but not least, I would like to give my special thanks to my parents for their endless love, care and having the mental assistance and motivation throughout my whole life Thai Nguyen, May 2020 TRAN HOANG SON iii TABLE OF CONTENT LIST OF FIGURES vi LIST OF ABBREVIATIONS .viii CHAPTER I INTRONDUCTION 1.1 Rationale 1.2 Objectives CHAPTER II LITERATURE REVIEW 2.1 Remote sensing technique 2.1.1 Remote Sensing 2.1.2 Hyperspectral data 2.1.3 Reflectance 2.1.4 Wavelength 2.1.5 Vegetation Indices 2.2 Crested Wheatgrass characteristics CHAPTER III METHODS 10 3.1 Study area 10 3.2 Data collection 11 3.2.1 Instruments 11 3.2.2 Sampling Design 12 3.2.3 Ground hyperspectral data 15 3.3 Pre – processing 15 3.4 Data processing 16 3.4.1 Hyperspectral data analysis 16 iv 3.4.2 Vegetation Indices calculation 17 3.4.3 Hypothesis testing 19 CHAPTER IV: RESULTS AND DISCUSSION 20 4.1 Spectral characteristics of Crested Wheatgrass 20 4.2 The best vegetation indices to differentiate Crested Wheatgrass 21 4.3 The behavior of Crested Wheatgrass in different Grazing regimes 26 CHAPTER V CONCLUSION 32 5.1 Limitation and futher study 32 5.2 Conclusion 33 REFERENCES 34 APPENDIX A 41 APPENDIX B 44 v LIST OF FIGURES Figure Remote sensing process (researchgate.net) Figure Location of Saskatchewan Landing Provincial Park 10 Figure Saskatchewan Landing Provincial Park Grassland 10 Figure Sampling design in each site 12 Figure Designing the sampling area 13 Figure Juniper in SLPP 14 Figure Alfafa in Alberta 14 Figure Sage in SLPP 14 Figure Shrub in SLPP 14 Figure 10 Crested Wheatgrass and grazing activity in SLPP 14 Figure 11 Spectral characteristic of CW and non-grass types in SLPP 20 Figure 12 Five Vegetation Indices of CW & non-grass types showed on graph 22 Figure 13 Vegetation Indices of 10 sites study indicated on graph 27 vi LIST OF TABLES Table List of Hyperspectral Vegetation Indices have been used 17 Table Average reflectances at wavelength 800, 670 and 500 of grass types 18 Table Vegetation Indices of CW & non-grass types 21 Table Comparisons of NDVI, RDVI, SAVI and MSAVI (CW and non-grass types) 24 Table Boxplots for Vegetation Indices of CW and non-grass types 25 Table Vegetation Indices for 10 sites study 26 Table Grazing & Dominant species information of 10 sites study in SLPP 29 Table Boxplots for Vegetation Indices of 10 sites study 30 vii LIST OF ABBREVIATIONS SLPP: Saskatchewan Landing Provincial Park CW: Crested Wheatgrass Vis: Vegetation Indices NDVI: Normalized Difference Vegetation Index RDVI: Renormalized Difference Vegetation Index SAVI: Soil Adjusted Vegetation Index MSAVI: Modified Soil Adjusted Vegetation Index PSRI: Plant Senescence Reflectance Index viii CHAPTER I INTRODUCTION 1.1 Rationale Non-native species are a threat to agricultural and native prairie communities around the world The existence of invasive species affected negatively on the native species population, crowding out the existence of native plants Invasive species are any species that not originate from that ecosystem and are capable of self-propagation, have introduced or likely caused harm to the environment and other factors (Pejchar and Mooney, 2009) Non-native species are also affected by climate change Predicted environmental changes, such as changes in rainfall and temperature, nutrient content and soil disturbance, may increase habitat sensitivity to non-native plants (Hufnagel and Garamvölgyi, 2014) When non-native plants invade the environment, they are able to overcome native plants through direct or indirect competition (Robert et al., 2013) A study by Driscoll et al (2014) shows that environmental weeds are nonnative plants that establish in natural areas, often harming to the native plants, damage to ecosystem function and cost billions of dollars to manage each year Crested Wheatgrass (CW) (Agropyron cristatum) is known as a species that occupied – 11 million hectares of grassland in the North American Great Plains (DeLuca and Lesica, 1996) Crested Wheatgrass threatens native ecosystems, which are important reservoirs of biodiversity (Mooney and Drake, 1989; D’Antonio and Vitousek, 1992) This species impacts both fauna and flora (Sutter and Brigham, 1998; Heidinga and Wilson, 2002) They also affect SAVI MSAVI 018324140918237 018586531076837 861 -.034034324090946 070682605927419 -.368552649272954* 018823311719037 000 -.421578127987826 -.315527170558082 -.022856969498434 018823311719037 743 -.075882448213306 030168509216438 -.168961632349977* 019742055882791 000 -.224575223604593 -.113348041095361 * -.150637491431740 017607595834271 000 -.200238284961921 -.101036697901559 -.217915157841214* 017607595834271 000 -.267515951371395 -.168314364311033 127780521933306* 017607595834271 000 078179728403125 177381315463487 -.018324140918237 018586531076837 861 -.070682605927419 034034324090946 * 150637491431740 017607595834271 000 101036697901559 200238284961921 375912037607204* 021887051161133 000 314255970768435 437568104445974 214125226857136* 022955332918149 000 149459798413264 278790655301008 392941596780080* 021887051161133 000 331285529941310 454597663618849 * 241643578951971 020473461662938 000 183969609470293 299317548433648 -.375912037607204* 021887051161133 000 -.437568104445974 -.314255970768435 -.161786810750068* 022955332918149 000 -.226452239193940 -.097121382306196 017029559172875 021887051161133 936 -.044626507665894 078685626011645 * -.134268458655234 020473461662938 000 -.191942428136911 -.076594489173556 -.214125226857136* 022955332918149 000 -.278790655301008 -.149459798413264 161786810750068* 022955332918149 000 097121382306196 226452239193940 178816369922944* 022955332918149 000 114150941479072 243481798366815 027518352094835 021611731381747 708 -.033362135859650 088398840049319 -.392941596780080* 021887051161133 000 -.454597663618849 -.331285529941310 -.017029559172875 021887051161133 936 -.078685626011645 044626507665894 -.178816369922944* 022955332918149 000 -.243481798366815 -.114150941479072 * -.151298017828109 020473461662938 000 -.208971987309787 -.093624048346431 -.241643578951971* 020473461662938 000 -.299317548433648 -.183969609470293 134268458655234* 020473461662938 000 076594489173556 191942428136911 -.027518352094835 021611731381747 708 -.088398840049319 033362135859650 * 151298017828109 020473461662938 000 093624048346431 208971987309787 470206780162716* 027783039570290 000 391941648322027 548471912003405 291037835751856* 029139097730381 000 208952672974138 373122998529575 481881115146533* 027783039570290 000 403615983305844 560146246987223 324231392601176* 025988653808802 000 251021065431538 397441719770814 -.470206780162716* 027783039570290 000 -.548471912003405 -.391941648322027 -.179168944410860* 029139097730381 000 -.261254107188578 -.097083781633141 011674334983817 027783039570290 993 -.066590796856872 089939466824506 -.145975387561540* 025988653808802 000 -.219185714731178 -.072765060391902 -.291037835751856* 029139097730381 000 -.373122998529575 -.208952672974138 179168944410860* 029139097730381 000 097083781633141 261254107188578 190843279394677* 029139097730381 000 108758116616959 272928442172395 42 5 033193556849320 027433553462324 746 -.044087068733613 110474182432253 -.481881115146533* 027783039570290 000 -.560146246987223 -.403615983305844 -.011674334983817 027783039570290 993 -.089939466824506 066590796856872 -.190843279394677* 029139097730381 000 -.272928442172395 -.108758116616959 -.157649722545357* 025988653808802 000 -.230860049714995 -.084439395375720 -.324231392601176* 025988653808802 000 -.397441719770814 -.251021065431538 145975387561540* 025988653808802 000 072765060391902 219185714731178 -.033193556849320 027433553462324 746 -.110474182432253 044087068733613 157649722545357* 025988653808802 000 084439395375720 230860049714995 * The mean difference is significant at the 0.05 level 43 APPENDIX B Multiple Comparisons Table (10 sites study) Multiple Comparisons Tukey HSD 95% Confidence Interval Dependent Variable (I) Code (J) Code NDVI -.017573179829342 019858501673780 997 -.081154481117515 046008121458831 001712123687693 019858501673780 1.000 -.061869177600481 065293424975866 -.058292991041983 019858501673780 103 -.121874292330156 005288310246190 -.097299097459764* 019858501673780 000 -.160880398747937 -.033717796171591 -.102768540715587* 019858501673780 000 -.166349842003760 -.039187239427414 -.099903677938985* 019858501673780 000 -.163484979227158 -.036322376650812 -.163469260334672* 019858501673780 000 -.227050561622845 -.099887959046498 -.094081254708281* 019858501673780 000 -.157662555996454 -.030499953420108 10 -.036640942254210 019858501673780 706 -.100222243542383 026940359033963 017573179829342 019858501673780 997 -.046008121458831 081154481117515 019285303517035 019858501673780 994 -.044295997771139 082866604805208 -.040719811212641 019858501673780 565 -.104301112500814 022861490075532 -.079725917630422* 019858501673780 003 -.143307218918595 -.016144616342249 -.085195360886245* 019858501673780 001 -.148776662174418 -.021614059598072 -.082330498109643* 019858501673780 002 -.145911799397816 -.018749196821470 -.145896080505329* 019858501673780 000 -.209477381793503 -.082314779217156 -.076508074878939* 019858501673780 006 -.140089376167112 -.012926773590766 10 -.019067762424868 019858501673780 994 -.082649063713041 044513538863305 -.001712123687693 019858501673780 1.000 -.065293424975866 061869177600481 -.019285303517035 019858501673780 994 -.082866604805208 044295997771139 -.060005114729675 019858501673780 082 -.123586416017848 003576186558498 -.099011221147456* 019858501673780 000 -.162592522435630 -.035429919859283 -.104480664403280* 019858501673780 000 -.168061965691453 -.040899363115107 -.101615801626677* 019858501673780 000 -.165197102914850 -.038034500338504 -.165181384022364* 019858501673780 000 -.228762685310537 -.101600082734191 -.095793378395974* 019858501673780 000 -.159374679684147 -.032212077107801 10 -.038353065941903 019858501673780 648 -.101934367230076 025228235346271 058292991041983 019858501673780 103 -.005288310246190 121874292330156 040719811212641 019858501673780 565 -.022861490075532 104301112500814 060005114729675 019858501673780 082 -.003576186558498 123586416017848 -.039006106417781 019858501673780 625 -.102587407705954 024575194870392 -.044475549673604 019858501673780 434 -.108056850961778 019105751614569 Mean Difference (I-J) Std Error Sig Lower Bound Upper Bound 44 -.041610686897002 019858501673780 533 -.105191988185175 021970614391171 -.105176269292689* 019858501673780 000 -.168757570580862 -.041594968004516 -.035788263666298 019858501673780 733 -.099369564954472 027793037621875 10 021652048787773 019858501673780 985 -.041929252500400 085233350075946 097299097459764* 019858501673780 000 033717796171591 160880398747937 079725917630422* 019858501673780 003 016144616342249 143307218918595 099011221147456* 019858501673780 000 035429919859283 162592522435630 039006106417781 019858501673780 625 -.024575194870392 102587407705954 -.005469443255823 019858501673780 1.000 -.069050744543996 058111858032350 -.002604580479221 019858501673780 1.000 -.066185881767394 060976720808952 -.066170162874908* 019858501673780 034 -.129751464163081 -.002588861586735 003217842751483 019858501673780 1.000 -.060363458536690 066799144039656 10 060658155205554 019858501673780 075 -.002923146082619 124239456493727 102768540715587* 019858501673780 000 039187239427414 166349842003760 085195360886245* 019858501673780 001 021614059598072 148776662174418 104480664403280* 019858501673780 000 040899363115107 168061965691453 044475549673604 019858501673780 434 -.019105751614569 108056850961778 005469443255823 019858501673780 1.000 -.058111858032350 069050744543996 002864862776602 019858501673780 1.000 -.060716438511571 066446164064776 -.060700719619084 019858501673780 075 -.124282020907257 002880581669089 008687286007306 019858501673780 1.000 -.054894015280867 072268587295479 10 066127598461377* 019858501673780 034 002546297173204 129708899749550 099903677938985* 019858501673780 000 036322376650812 163484979227158 082330498109643* 019858501673780 002 018749196821470 145911799397816 101615801626677* 019858501673780 000 038034500338504 165197102914850 041610686897002 019858501673780 533 -.021970614391171 105191988185175 002604580479221 019858501673780 1.000 -.060976720808952 066185881767394 -.002864862776602 019858501673780 1.000 -.066446164064776 060716438511571 -.063565582395687 019858501673780 050 -.127146883683860 000015718892486 005822423230704 019858501673780 1.000 -.057758878057470 069403724518877 10 063262735684775 019858501673780 052 -.000318565603398 126844036972948 163469260334672* 019858501673780 000 099887959046498 227050561622845 145896080505329* 019858501673780 000 082314779217156 209477381793503 165181384022364* 019858501673780 000 101600082734191 228762685310537 105176269292689* 019858501673780 000 041594968004516 168757570580862 066170162874908* 019858501673780 034 002588861586735 129751464163081 060700719619084 019858501673780 075 -.002880581669089 124282020907257 063565582395687 019858501673780 050 -.000015718892486 127146883683860 45 10 RDVI 069388005626390* 019858501673780 020 005806704338217 132969306914564 10 126828318080462* 019858501673780 000 063247016792288 190409619368635 094081254708281* 019858501673780 000 030499953420108 157662555996454 076508074878939* 019858501673780 006 012926773590766 140089376167112 095793378395974* 019858501673780 000 032212077107801 159374679684147 035788263666298 019858501673780 733 -.027793037621875 099369564954472 -.003217842751483 019858501673780 1.000 -.066799144039656 060363458536690 -.008687286007306 019858501673780 1.000 -.072268587295479 054894015280867 -.005822423230704 019858501673780 1.000 -.069403724518877 057758878057470 -.069388005626390* 019858501673780 020 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1.000 -.043534264658799 031853539144309 -.022038921268212 011773027585746 688 -.059732823169766 015654980633342 -.006284211700977 011773027585746 1.000 -.043978113602531 031409690200577 -.044815684591967* 011773027585746 007 -.082509586493521 -.007121782690413 -.005201151388280 011773027585746 1.000 -.042895053289834 032492750513274 10 025924876176671 011773027585746 459 -.011769025724882 063618778078225 024681401145542 011773027585746 532 -.013012500756012 062375303047096 042043833582384* 011773027585746 016 004349931680830 079737735483938 031170241562197 011773027585746 203 -.006523660339357 068864143463750 005840362757245 011773027585746 1.000 -.031853539144309 043534264658799 -.016198558510967 011773027585746 933 -.053892460412521 021495343390587 -.000443848943732 011773027585746 1.000 -.038137750845286 037250052957822 -.038975321834722* 011773027585746 036 -.076669223736276 -.001281419933168 000639211368965 011773027585746 1.000 -.037054690532589 038333113270519 10 031765238933916 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Student ID DTN1554290027 Thesis Title Differentiating Crested Wheatgrass (Agropyron Cristatum) in Saskatchewan Landing Provincial Park, Canada with Remote Sensing Supervisor (s) Assoc Prof Tran... Figure Remote sensing process (researchgate.net) Figure Location of Saskatchewan Landing Provincial Park 10 Figure Saskatchewan Landing Provincial Park Grassland 10 Figure Sampling... Wheatgrass; c Investigate the behavior of Crested Wheatgrass in different Grazing regimes CHAPTER II LITERATURE REVIEW 2.1 Remote sensing technique 2.1.1 Remote Sensing Remote sensing is the collection