Remote Sensing in Mangroves

The book highlights recent advancements in the mapping and monitoring of mangrove forests using earth observation satellite data. New and historical satellite data and aerial photographs have been used to map the extent, change and bio-physical parameters, such as phenology and biomass. Research was conducted in different parts of the world. Knowledge and understanding gained from this book can be used for the sustainable management of mangrove forests of the worl

Biomass Density Based Adjustment of LiDAR-derived Digital Elevation Models: A Machine Learning Approach

Salt marshes are valued for providing protective and non-protective ecosystem services. Accurate digital elevation models (DEMs) in salt marshes are crucial for modeling storm surges and determining the initial DEM elevations for modelling marsh evolution. Due to high biomass density, lidar DEMs in coastal wetlands are seldom reliable. In an aim to reduce lidar-derived DEM error, several multilinear regression and random forest models were developed and tested to estimate biomass density in the salt marshes near Saint Marks Lighthouse in Crawfordville, Florida. Between summer of 2017 and spring of 2018, two field trips were conducted to acquire true elevation and biomass density measures. Lidar point cloud data were combined with vegetation monitoring imagery acquired from Sentinel-2 and Landsat Thematic Mapper (LTM) satellites, and 64 field biomass density samples were used as target variables for developing the models. Biomass density classes were assigned to each biomass sample using a quartile approach. Moreover, 346 in-situ elevation measures were used to calculate the lidar DEM errors. The best model was then used to estimate biomass densities at all 346 locations. Finally, an adjusted DEM was produced by deducting the quartile-based adjustment values from the original lidar DEM. A random forest regression model achieved the highest pseudo R2 value of 0.94 for predicting biomass density in g/m2. The adjusted DEM based on the estimated biomass densities reduced the root mean squared error of the original DEM from 0.38 m to 0.18 m while decreasing the raw mean error from 0.33 m to 0.14 m, improving both measures by 54% and 58%, respectively

Mapping grass nutrient phosphorus (P) and sodium (NA) across different grass communities using Sentinel-2 data

A research report submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in partial fulfillment of the requirement for the degree of Master of Science (Environmental Sciences) at the School of Geography, Archaeology & Environmental Studies March 2017Accurate estimates and mapping of grass quality is important for effective rangeland management. The purpose of this research was to map different grass species as well as nutrient Phosphorus (P) and Sodium (Na) concentration across grass communities using Sentinel-2 imagery in Telperion game reserve. The main objectives of the study were to: map the most common grass communities at the Telperion game reserve using Sentinel-2 imagery using artificial neural network (ANN) classifier and to evaluate the use of Sentinel-2 (MSI) in quantifying grass phosphorus and sodium concentration across different grass communities. Grass phosphorus and sodium concentrations were estimated using Random Forest (RF) regression algorithm, normalized difference vegetation index (NDVI) and the simple ratios (SR) which were calculated from all two possible band combination of Sentinel-2 data. Results obtained demonstrated woody vegetation as the dominant vegetation and Aristida congesta as the most common grass species. The overall classification accuracy = 81%; kappa =0.78 and error rate=0.18 was achieved using the ANN classifier. Regression model for leaf phosphorus concentration prediction both NDVI and SR data sets yielded similar results (R2 =0.363; RMSE=0.017%) and (R2 =0.36 2; RMSE=0.0174%). Regression model for leaf sodium using NDVI and SR data sets yielded dissimilar results (R2 =0.23; RMSE=16.74 mg/kg) and (R2 =0.15; RMSE =34.08 mg/kg). The overall outcomes of this study demonstrate the capability of Sentinel 2 imagery in mapping vegetation quality (phosphorus and sodium) and quantity. The study recommends the mapping of grass communities and both phosphorus and sodium concentrations across different seasons to fully understand the distribution of different species across the game reserve as well as variations in foliar concentration of the elements. Such information will guide the reserve managers on resource use and conservation strategies to implement within the reserve. Furthermore, the information will enable conservation managers to understand wildlife distribution and feeding patterns. This will allow integration of effective conservation strategies into decisions on stocking capacity.MT 201

Exploring issues of balanced versus imbalanced samples in mapping grass community in the telperion reserve using high resolution images and selected machine learning algorithms

ABSTRACT Accurate vegetation mapping is essential for a number of reasons, one of which is for conservation purposes. The main objective of this research was to map different grass communities in the game reserve using RapidEye and Sentinel-2 MSI images and machine learning classifiers [support vector machine (SVM) and Random forest (RF)] to test the impacts of balanced and imbalance training data on the performance and the accuracy of Support Vector Machine and Random forest in mapping the grass communities and test the sensitivities of pixel resolution to balanced and imbalance training data in image classification. The imbalanced and balanced data sets were obtained through field data collection. The results show RF and SVM are producing a high overall accuracy for Sentinel-2 imagery for both the balanced and imbalanced data set. The RF classifier has yielded an overall accuracy of 79.45% and kappa of 74.38% and an overall accuracy of 76.19% and kappa of 73.21% using imbalanced and balanced training data respectively. The SVM classifier yielded an overall accuracy of 82.54% and kappa of 80.36% and an overall accuracy of 82.21% and a kappa of 78.33% using imbalanced and balanced training data respectively. For the RapidEye imagery, RF and SVM algorithm produced overall accuracy affected by a balanced data set leading to reduced accuracy. The RF algorithm had an overall accuracy that dropped by 6% (from 63.24% to 57.94%) while the SVM dropped by 7% (from 57.31% to 50.79%). The results thereby show that the imbalanced data set is a better option when looking at the image classification of vegetation species than the balanced data set. The study recommends the implementation of ways of handling misclassification among the different grass species to improve classification for future research. Further research can be carried out on other types of high resolution multispectral imagery using different advanced algorithms on different training size samples.EM201

The utility of new generation multispectral sensors in assessing aboveground biomass of Phragmites australis in wetlands areas in the City of Tshwane Metropolitan Municipality; South Africa.

Master of Science in Environmental Science. University of KwaZulu-Natal. Pietermaritzburg, 2017.Abstract available in PDF file

Mapping Prosopis glandulosa (mesquite) invasion in the arid environment of South African using remote sensing techniques

A dissertation submitted to the School of Geography, Archaeology and Environmental Studies, Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of requirements for the degree of Master of Science in Environmental Sciences. Johannesburg, March 2016.Mapping Prosopis glandulosa (mesquite) invasion in the arid environment of South Africa using remote sensing techniques Mureriwa, Nyasha Abstract Decades after the first introduction of the Prosopis spp. (mesquite) to South Africa in the late 1800s for its benefits, the invasive nature of the species became apparent as its spread in regions of South Africa resulting in devastating effects to biodiversity, ecosystems and the socio-economic wellbeing of affected regions. Various control and management practices that include biological, physical, chemical and integrated methods have been tested with minimal success as compared to the rapid spread of the species. From previous studies, it has been noted that one of the reasons for the low success rates in mesquite control and management is a lack of sufficient information on the species invasion dynamic in relation to its very similar co-existing species. In order to bridge this gap in knowledge, vegetation species mapping techniques that use remote sensing methods need to be tested for the monitoring, detection and mapping of the species spread. Unlike traditional field survey methods, remote sensing techniques are better at monitoring vegetation as they can cover very large areas and are time-effective and cost-effective. Thus, the aim of this research was to examine the possibility of mapping and spectrally discriminating Prosopis glandulosa from its native co-existing species in semi-arid parts of South Africa using remote sensing methods. The specific objectives of the study were to investigate the spectral separability between Prosopis glandulosa and its co-existing species using field spectral data as well as to upscale the results to different satellites resolutions. Two machine learning algorithms (Random Forest (RF) and Support Vector Machines (SVM)) were also tested in the mapping processes. The first chapter of the study evaluated the spectral discrimination of Prosopis glandulosa from three other species (Acacia karoo, Acacia mellifera and Ziziphus mucronata) in the study area using in-situ spectroscopy in conjunction with the newly developed guided regularized random forest (GRRF) algorithm in identifying key wavelengths for multiclass classification. The GRRF algorithm was used as a method of reducing the problem of high dimensionality associated with hyperspectral data. Results showed that there was an increase in the accuracy of discrimination between the four species when the full set of 1825 wavelengths was used in classification (79.19%) as compared to the classification used by the 11 key wavelengths identified by GRRF (88.59%). Results obtained from the second chapter showed that it is possible to spatially discriminate mesquite from its co-existing acacia species and other general land-cover types at a 2 m resolution with overall accuracies of 86.59% for RF classification and 85.98% for SVM classification. The last part of the study tested the use of the more cost effective SPOT-6 imagery and the RF and SVM algorithms in mapping Prosopis glandulosa invasion and its co-existing indigenous species. The 6 m resolution analysis obtained accuracies of 78.46% for RF and 77.62% for SVM. Overall it was concluded that spatial and spectral discrimination of Prosopis glandulosa from its native co-existing species in semi-arid South Africa was possible with high accuracies through the use of (i) two high resolution, new generation sensors namely, WorldView-2 and SPOT-6; (ii) two robust classification algorithms specifically, RF and SVM and (iii) the newly developed GRRF algorithm for variable selection and reducing the high dimensionality problem associated with hyperspectral data. Some recommendations for future studies include the replication of this study on a larger scale in different invaded areas across the country as well as testing the robustness of the RF and SVM classifiers by making use of other machine learning algorithms and classification methods in species discrimination. Keywords: Prosopis glandulosa, field spectroscopy, cost effectiveness, Guided Regularised Random Forest, Support Vector Machines, Worldview-2, Spot-

Kliimamuutuse mõju hindamine rannaniidu taimekooslusele mesokosmi katse ja mehitamata õhusõidukiga kogutud andmete põhjal

A Thesis for applying for the degree of Doctor of Philosophy in Environmental Protection.Väitekiri filosoofiadoktori kraadi taotlemiseks keskkonnakaitse erialal.Semi-natural grasslands are an essential part of the cultural landscape of Europe. Semi-natural grasslands are commonly characterised by a very high biodiversity, including rare species. Beyond the high biodiversity value, semi-natural grasslands worldwide provide many ecosystem services, including: carbon sequestration and storage, nutrient cycling, regulation of soil quality, habitats for migrating birds, erosion control, and flood regulation. Within the realm of semi-natural grasslands, coastal meadows are particularly important. However, coastal grasslands are threatened by a range of factors such as coastal squeeze, transformation into monoculture ponds, pollution, and climate change. Coastal areas are threatened at a range of spatial scales as a result of sea-level rise, and can include higher flooding frequency in coastal areas, salt water intrusion in aquifers, and potential declines in the extent of coastal wetlands. A warmer climate also implies a modification in precipitation patterns affecting runoff into the sea. In coastal areas, both water levels and salinity have a strong impact on species distribution and therefore on the structure and composition of aquatic and coastal floral and faunal communities. Consequently, plant communities in coastal meadows are expected to undergo changes in their composition and structure. The current thesis explores different methodologies to assess plant community distribution, above-ground biomass, and the effects of management type, duration, and intensity on sward structure using UAV-derived multispectral data and aerial photogrammetry. In addition, the keystone of this thesis is a mesocosm experiment that was used to assess shifts in species richness and abundance in plant community types in Estonian coastal meadows related to future change scenarios of water level and salinity for the Baltic Sea. a. Unmanned Aerial Vehicle (UAV) The use of UAV demonstrated to be able to identify plant community extent and distribution in high biodiversity value coastal meadows in West Estonia. Species diversity and biomass significantly influence the quality of data and this should be accounted for when planning the sample collection to achieve better results. This study has shown that UAVs are useful tools of mapping grasslands at a plant community level. Also, UAV showed to be possible to reveal the structure of the grassland and how it is affected by the management history. For example, the grassland turns more homogeneous under long-term monospecific grazing, b. Mesoscosm Experiment The mesocosm experiment in the present study revealed different temporal changes of wetland communities to altered salinity and water conditions, highlighting the response of plant species to environmental variables. These changes were not significant according to alteration of water level and salinity in the Open Pioneer community, but they were over time. On the other hand, Lower Shore and Upper Shore had significant changes according to time and treatments. These could be explained by dynamic differences in the communities, since Open Pioneer was more variable. c. Conclusions Both methodologies, remote sensing and the mesocosm experiment, are evidently important to evaluate the structure and function of Estonian coastal meadows. The mapping of the extent and structure of coastal plant communities allows an evaluation of the current state of the ecosystem. The mesocosm experiment helps to understand changes in plant community composition under altered conditions of water level and salinity in Estonian coastal meadows and consequently, understand how species richness, abundance, and biomass will respond to those changes. This information is important when considering the protection and potential management of these areas taking into account the species diversity of fauna and flora as well as that of livestock.Uuring viidi läbi kahel tasandil: uuringukohtades Lääne-Eestis ja katsekeskkonnas. Esimesel juhul valiti Silma looduskaitsealal, Matsalu rahvuspargis ja Vormsi saarel ranniku taimekoosluste ja maapealse biomassi kaardistamiseks kokku üheksa rannaniiduala (I, II). Teine osa hõlmab mesokosmi katset (III), mille käigus kasutati katse seadmiseks ja eksperimenteerimiseks Silma looduskaitsealalt kogutud proove. Vaatamata oma suhteliselt väikesele pindalale (45 228 km2) iseloomustab Eestit mitmekesine geoloogia, pinnamood ja kliima. Läänemere rannaniidud on tekkinud ja need säilivad maa isostaatilise tõusu, setete kogunemise ja alade vähese intensiivsusega majandamise – karjatamise või niitmise – tõttu. Eesti rannikumärgaladel on ebatavaline hüdroloogiline režiim. Kuna loodete ulatus on väga väike (~0,02 m), põhjustab rannaniitude üleujutusi valdavalt tsüklonaalne aktiivsus Põhja-Atlandil ja Fennoskandias. Üleujutuste sagedus ja ulatus on ebaregulaarne ning varieerub kogu rannikumaastikul, sõltudes tuule kiirusest ja suunast. Hiljutised hinnangud suhtelise meretaseme tõusu kohta kolmelt mõõnamõõturilt piki Eesti rannikut on järgmised: Tallinnas 1,5–1,7 mm a-1, Narva-Jõesuus 1,7–2,1 mm a-1 ja Pärnus 2,3–2,7 mm a-1 (Ward et al., 2014). Taimekoosluse klassifitseerimiseks ja biomassi prognoosimiseks analüüsiti üheksat rannikumärgala kolmes kohas Silma looduskaitsealal, kahes kohas Matsalu rahvuspargis ja neljas kohas Vormsi saarel. Neis kohtades esinevad kõik väitekirjas käsitletud taimekooslused. Uurimiskohtade taimekooslused liigitati vastavalt Burnside´i jt fütosotsioloogilisele klassifikatsioonile (2007): pilliroostik, võsasoo, madal rannik, kõrgrannik, pioneerliikidega paljakud, kõrgrohustu, võsa ja metsamaa. Võsasoo ning võsa ja metsamaa jäeti nende marginaalse esinemise tõttu uurimusest välja. Uurimistöö käigus tehti kaks erinevat analüüsi, kasutades UAV-ga kogutud multispektraal- ja rgb-fotosid. UAV multispektraalseid pilte kasutati taimekoosluste kaardistamiseks Silma looduskaitsealal Põhja-Tahu, Lõuna-Tahu ja Kudani rannaniidul (I). Järgnevalt kasutati multispektraalseid ja rgb-pilte kõrge ruumilise eraldusvõimega kaartide koostamiseks maapealse biomassi tuvastamiseks kõigis üheksas uuringukohas (II). Taimekoosluste kaardistamiseks (I) ja maapealse biomassi prognoosimiseks (II) kasutati otsustusmetsa klassifikatsiooni. Seejärel analüüsiti maapealse biomassi kaartide abil majandamisviisi ja intensiivsuse mõju rannaniitude heinamaade struktuurile (II). Teavet rannaniitude kasutusviisi kohta saadi maaomanikega isiklikult suheldes. Uurimistöö teises osas valiti mesokosmi katse jaoks kolm taimekooslust: pioneerliikidega paljakud, madal rannik ja kõrgrannik. Need kooslused valiti sealsete võtmeliikide spetsiifilise autökoloogilise kasvukohaeelistuse tõttu (nt soolsus ja mulla veesisaldus). Katsest välja jäetud pilliroostikus ja võsasoos domineerivad üleujutust taluvad liigid; kõrgrohustu kujutab endast maismaa ja märgalade ökosüsteemi vahelist kooslust, ning võsa on täielikult maismaa. Silma looduskaitsealal varuti Põhja-Tahu alalt 2018. aasta juunis kolmest valitud taimekooslusest 15 mätast (suurus 50 x 70 cm, paksus 30 cm). Mesokosmi katse varustus koosnes mahutitest (90L, mõõtmed 56 x 79 x 32 cm), mis olid täidetud 2:1:1 mullaseguga, mis koosneb pestud sõmera struktuuriga liivast, savist ja kompostist, mis on väga sarnane märgala põhjasubstraadiga. Mahutid numereeriti ja varustati vastava tähisega. Mahutid asusid kogu katse jooksul samal kohal. Katse käiku hinnati alalise gradueeritud 50 cm2 kvadraadi abil, mis jaotati 25 kvadraadiks (10 x 10 cm), ja määrati kindlaks muutused esinevate taimeliikide arvukuses pinnakatte pindala järgi (katteprotsent). Katse kestis kolme aastat veetaseme ja soolsuse tingimustes, mis tuletati kliimamuutuste prognoosidest 2100. aastaks. Liikide arvukus ja liigirikkus arvutati 2018., 2019. ja 2020. aastaks iga taimekoosluse kohta eraldi. Liigirikkuse erinevusi aastati ja kasvutingimuste suhtes hinnati Kruskal Wallise testiga, mis põhineb Bonferroni kohandustega Dunni testil, et tuvastada liigirikkuse erinevusi igal aastal. Liigilise arvukuse esitamiseks kasutati arvukuse kõveraid. Taimekoosluse koostise erinevuste uurimiseks kasutati permutatsioonilist mitmemõõtmelist analüüsi Bray-Curtise erinevusega. Aasta ristmõju analüüsis käsitleti töötlemisviisi fikseeritud mõju ja valimeid juhusliku mõjuna. Tulemused ja järeldused Rannaniitudel hinnati taimekoosluste levikut, maismaa biomassi ja taimestiku vertikaalset struktuuri. Fleissi kapa kordaja 0,89 põhjal kaardistati põhjalikult taimekooslused (I). Otsustusmetsa klassijärgsed vead näitavad, et homogeensema struktuuriga piirkondi on kergem klassifitseerida kui keerulise struktuuriga koosluseid. Otsustusmetsa algoritmi jõudlusanalüüs näitas, et biomassi hindamisel saadi parim tulemus, kui multispektraalne info kombineeriti fotogramm-meetriliselt loodud digitaalse maastikumudeliga (DTM, ingl digital terrain model) (II). Tulemused viitavad sellele, et mitme anduri kombinatsiooni saab kasutada ökosüsteemi omaduste mõõtmiseks, mida ainult spektraalinformatsiooni analüüsides ei pruugi tuvastada. Siinse uuringu maapealse biomassi prognooside suur täpsus näitab, et rannaniitude jälgimisel on kaugseire UAV-ga sobiv meetod. Struktuurianalüüsi tulemused näitasid, mil määral mõjutab biomassi jaotust karjatamise kestus ja heterogeensus. Pidevalt majandatavatele rohumaadele on iseloomulikud suuremad ja homogeensemad alad (II). Üldine lineaarne mudelianalüüs ja Mann-Whitney u-testid näitasid, kuidas taimtoidulised liigid mõjutavad rohumaa struktuuri. Rohumaad, millel karjatatakse erinevaid taimtoidulisi, on mitmekesisema struktuuriga kui veiste karjamaa (II). Mesokosmi katse tulemused näitasid, et kõigis kolmes Läänemere ranniku märgalade koosluses ilmnesid aja jooksul vee- ja soolsusrežiimis märkimisväärsed muutused, mis tõi esile taimeliikide reaktsiooni keskkonnamuutuste suhtes (III). Pioneerliikidega paljakutel suurenes liigirikkus ja taimkate kõigi keskkonnamuutuste korral, sellega võrreldes esines madalal rannikul ja kõrgrannikul nii veetaseme kui ka soolsusega seotud muutusi vähemal määral. Pioneerliikidega paljakuid mõjutab enamasti soolsus, seda isegi peamiselt sõmerast, keskmise fraktsiooniga ja peenest liivast koosnevas pinnases, mis säilitab vähem toitaineid kui peenema fraktsiooniga muld. Spergularia marina ja Glaux maritima aitasid kaasa liigirikkuse suurenemisele mulla suurenenud ja vähenenud soolsuse tingimustes. Üldiselt ei ilmnenud madala ranniku ja kõrgranniku taimekooslustes soolsuse muutumise korral olulisi muutusi võrreldes kontrollkatsega. Nendes kooslustes on liike, mis kasvavad nii soolases kui ka mittesoolases keskkonnas. Veetaseme muutus mõjutas pioneerliikidega paljakute taimekooslust sarnaselt soolsuse muutmisega. Selle koosluse liigirikkus suurenes kõrgema veetaseme korral, võrreldes kontrollkatsega. Kõrgema veetasemega kohanenud liike nagu Eleocharis palustris esines kõrgenenud veetaseme korral kolmandal aastal rohkem; alanenud vees leidus katse lõpus rohkem väiksema veevajadusega liike, nagu Glaux maritima ja Centaurium littorale. Madalal rannikul registreeriti madalama veetaseme korral liigirikkuse muutus, võrreldes kontrollkatsega. Aja jooksul toimuv liikide varieeruvus ilmnes vähese pinnakatvusega liikide puhul, nt ahenesid Carex flacca ja Triglochin palustris´e kasvukohad. Madal rannik asub veetasemelt pioneerliikidega paljaku ja kõrgranniku vahel ning see võib seletada, miks sealsed liigid taluvad mulla mitmesuguseid niiskustingimusi. Kõrgranniku koosluses vähenes kõrgenenud veetaseme korral liikide arv ja sellest tulenevalt ka liigirikkus; sealjuures laienesid vähese pinnakatvuse ja madala veetasemega kohastunud liikide Stellaria graminea ja Viola canina kasvukohad. See uurimus näitas, et ökoloogilistes uuringutes võib erinevate metoodikate kombinatsioon osutuda tõhusaks. Vaid vähestes uuringutes kombineeritakse ökosüsteemiprotsesside mõistmiseks erinevaid lähenemisviise, nt kaugseiret ja katseplatvorme, antud töös mesokosmi katset. Uued tehnoloogilised edusammud kaugseire vallas võivad lahendada küsimusi, millele vastuse leidmine traditsiooniliste ökoloogiliste meetodite abil oleks keeruline või ebapraktiline. Samas on traditsioonilise lähenemisviisiga, nt mesokosmi katsega saadud teadmised uue tehnoloogilise potentsiaali rakendamiseks väga vajalikud. Uurimus näitas, et UAV on sobiv vahend rannikurohumaade struktuuri ja taimekoosluste leviku täpse eraldusvõimega kaartide koostamiseks. Teisest küljest aitab mesokosmi katse mõista taimekoosluse koostise muutusi eri veetaseme ja soolsuse tingimustes.Publication of this thesis is supported by the Estonian University of Life Sciences and by the Doctoral School of Earth Sciences and Ecology created under the auspices of the European Social Fund

Forest Aboveground Biomass Estimation Using Multi-Source Remote Sensing Data in Temperate Forests

Forests are a crucial part of global ecosystems. Accurately estimating aboveground biomass (AGB) is important in many applications including monitoring carbon stocks, investigating forest degradation, and designing sustainable forest management strategies. Remote sensing techniques have proved to be a cost-effective way to estimate forest AGB with timely and repeated observations. This dissertation investigated the use of multiple remotely sensed datasets for forest AGB estimation in temperate forests. We compared the performance of Landsat and lidar data—individually and fused—for estimating AGB using multiple regression models (MLR), Random Forest (RF) and Geographically Weight Regression (GWR). Our approach showed MLR performed similarly to GWR and both were better than RF. Integration of lidar and Landsat inputs outperformed either data source alone. However, although lidar provides valuable three-dimensional forest structure information, acquiring comprehensive lidar coverage is often cost prohibitive. Thus we developed a lidar sampling framework to support AGB estimation from Landsat images. We compared two sampling strategies—systematic and classification-based—and found that the systematic sampling selection method was highly dependent on site conditions and had higher model variability. The classification-based lidar sampling strategy was easy to apply and provides a framework that is readily transferable to new study sites. The performance of Sentinel-2 and Landsat 8 data for quantifying AGB in a temperate forest using RF regression was also tested. We modeled AGB using three datasets: Sentinel-2, Landsat 8, and a pseudo dataset that retained the spatial resolution of Sentinel-2 but only the spectral bands that matched those on Landsat 8. We found that while RF model parameters impact model outcomes, it is more important to focus attention on variable selection. Our results showed that the incorporation of red-edge information increased AGB estimation accuracy by approximately 6%. The additional spatial resolution improved accuracy by approximately 3%. The variable importance ranks in the RF regression model showed that in addition to the red- edge bands, the shortwave infrared bands were important either individually (in the Sentinel-2 model) or in band indices. With the growing availability of remote sensing datasets, developing tools to appropriately and efficiently apply remote sensing data is increasingly important

Characterizing leaf nutrients of wetland plants and agricultural crops with nonparametric approach using Sentinel-2 imagery data

In arid environments of the world, particularly in sub-Saharan Africa and Asia, floodplain wetlands are a valuable agricultural resource. However, the water reticulation role by wetlands and crop production can negatively impact wetland plants. Knowledge on the foliar biochemical elements of wetland plants enhances understanding of the impacts of agricultural practices in wetlands. This study thus used Sentinel-2 multispectral data to predict seasonal variations in the concentrations of nine foliar biochemical elements in plant leaves of key floodplain wetland vegetation types and crops in the uMfolozi floodplain system (UFS). Nutrient concentrations in different floodplain plant species were estimated using Sentinel-2 multispectral data derived vegetation indices in concert with the random forest regression. The results showed a mean R2 of 0.87 and 0.86 for the dry winter and wet summer seasons, respectively. However, copper, sulphur, and magnesium were poorly correlated (R2 ≤ 0.5) with vegetation indices during the summer season. The average % relative root mean square errors (RMSE’s) for seasonal nutrient estimation accuracies for crops and wetland vegetation were 15.2 % and 26.8%, respectively. There was a significant difference in nutrient concentrations between the two plant types, (R2 = 0.94 (crops), R2 = 0.84 (vegetation). The red-edge position 1 (REP1) and the normalised difference vegetation index (NDVI) were the best nutrient predictors. These results demonstrate the usefulness of Sentinel-2 imagery and random forests regression in predicting seasonal, nutrient concentrations as well as the accumulation of chemicals in wetland vegetation and crops.Department of Higher Education, Science and Technology and Agricultural Research Council.http://www.mdpi.com/journal/remotesensingpm2022Geography, Geoinformatics and Meteorolog