Assessing Agave sisalana biomass from leaf to plantation level using field measurements and multispectral satellite imagery

Biomassa, eli kasviaineksen määrä, on tärkeä muuttuja viljelykasvien kasvun seurannassa sekä arvioitaessa hiilen kiertoa. Kenttätöissä biomassaa voidaan arvioida kasveja vahingoittamatta hyödyntämällä allometrisia malleja. Suuremmassa mittakaavassa biomassaa voidaan kartoittaa kaukokartoitusmenetelmillä. Tässä tutkimuksessa arvioitiin Agave sisalanan eli sisalin lehtien kuivaa biomassaa. Sisal on trooppisilla ja subtrooppisilla alueilla viljeltävä monivuotinen kasvi, jonka lehdistä tuotetaan kuitua ja biopolttoainetta. Lehtibiomassan arvioimiseksi luotiin ensin allometrinen malli, minkä jälkeen biomassa mallinnettiin 8851 hehtaarin plantaasille Kaakkois-Keniassa käyttämällä Sentinel-2 multispektraalista satellittikuva-aineistoa. Allometrista mallia varten kerättiin 38:n lehden otos. Kasvin korkeuden ja lehden suurimman ympärysmitan avulla muodostettiin tilavuusarvio, jonka yhteyttä biomassaan mallinnettiin lineaarisella regressiolla. Muuttujien välille löytyi vahva log-log lineaarinen yhteys ja ristiinvalidointi osoitti, että mallin ennusteet ovat tarkkoja (R2 = 0.96, RMSE = 7.69g). Mallin avulla ennustettiin lehtibiomassa 58:lle koealalle, jotka muodostivat otoksen biomassan mallinnukseen Sentinel-2 kuvalla. Mallinnuksessa käytettiin yleistettyjä additiivisia malleja, joiden avulla tutkittiin lukuisten spektraalisten kasvillisuusindeksien yhteyttä biomassaan. Parhaaksi osoittautuivat indeksit, jotka laskettiin hyödyntämällä vihreää ja lähi-infrapunakanavaa, sekä ns. ”red-edge”-kanavia (D2 = 74%, RMSE = 4.96 Mg/ha). Keskeisin mallin selitysastetta heikentävä tekijä vaikutti olevan suuresti vaihteleva aluskasvillisuuden määrä. Hyödyntämällä parhaaksi todettua kasvillisuusindeksiä lehtibiomassa mallinnettiin koko plantaasin peltoalalle. Biomassa vaihteli 0 ja 45.1 Mg/ha välillä, keskiarvon ollessa 9.9 Mg/ha. Tämän tutkimuksen tuloksena syntyi allometrinen malli, jota voidaan käyttää sisalin lehtibiomassan arviointiin. Jatkotutkimuksissa tulisi ottaa huomioon myös kasvin muut osat, kuten varsi ja juuret. Biomassan mallinnus multispektraalisilla kasvillisuusindekseillä osoitti menetelmän toimivuuden sisalin biomassan kartoituksessa, mutta vaihtelevan aluskasvillisuuden todettiin heikentävän mallin suorituskykyä. Aluskasvillisuuden vaikutusta ja täydentäviä aineistolähteitä tulisi tutkia tulevaisuudessa. Plantaasin lehtibiomassan, ja näin ollen maanpäälle sitoutuneen hiilen määrä, on saman suuruinen, kuin alueen luonnollisella pensassavannilla. Sisal-plantaasin hiilen kierron kokonaisvaltainen ymmärtäminen vaatii kuitenkin lisätietoa kasvien ja maaperän hiilivuosta sekä maaperän hiilensitomisesta.Biomass is an important parameter for crop monitoring and management, as well as for assessing carbon cycle. In the field, allometric models can be used for non-destructive biomass assessment, whereas remote sensing is a convenient method for upscaling the biomass estimations over large areas. This study assessed the dry leaf biomass of Agave sisalana (sisal), a perennial crop whose leaves are grown for fibre and biofuel production in tropical and subtropical regions. First, an allometric model was developed for predicting the leaf biomass. Then, Sentinel-2 multispectral satellite imagery was used to model the leaf biomass at 8851 ha plantation in South-Eastern Kenya. For the allometric model 38 leaves were sampled and measured. Plant height and leaf maximum diameter were combined into a volume approximation and the relation to biomass was formalised with linear regression. A strong log-log linear relation was found and leave-one-out cross-validation for the model showed good prediction accuracy (R2 = 0.96, RMSE = 7.69g). The model was used to predict biomass for 58 field plots, which constituted a sample for modelling the biomass with Sentinel-2 data. Generalised additive models were then used to explore how well biomass was explained by various spectral vegetation indices (VIs). The highest performance (D2 = 74%, RMSE = 4.96 Mg/ha) was achieved with VIs based on the red-edge (R740 and R783), near-infrared (R865) and green (R560) spectral bands. Highly heterogeneous growing conditions, mainly variation in the understory vegetation seemed to be the main factor limiting the model performance. The best performing VI (R740/R783) was used to predict the biomass at plantation level. The leaf biomass ranged from 0 to 45.1 Mg/ha, with mean at 9.9 Mg/ha. This research resulted a newly established allometric equation that can be used as an accurate tool for predicting the leaf biomass of sisal. Further research is required to account for other parts of the plant, such as the stem and the roots. The biomass-VI modelling results showed that multispectral data is suitable for assessing sisal leaf biomass over large areas, but the heterogeneity of the understory vegetation limits the model performance. Future research should address this by investigating the background effects of understory and by looking into complementary data sources. The carbon stored in the leaf biomass at the plantation corresponds to that in the woody aboveground biomass of natural bushlands in the area. Future research is needed on soil carbon sequestration and soil and plant carbon fluxes, to fully understand the carbon cycle at sisal plantation

Assessing Leaf Biomass of Agave sisalana Using Sentinel-2 Vegetation Indices

Biomass is a principal variable in crop monitoring and management and in assessing carbon cycling. Remote sensing combined with field measurements can be used to estimate biomass over large areas. This study assessed leaf biomass of Agave sisalana (sisal), a perennial crop whose leaves are grown for fibre production in tropical and subtropical regions. Furthermore, the residue from fibre production can be used to produce bioenergy through anaerobic digestion. First, biomass was estimated for 58 field plots using an allometric approach. Then, Sentinel-2 multispectral satellite imagery was used to model biomass in an 8851-ha plantation in semi-arid south-eastern Kenya. Generalised Additive Models were employed to explore how well biomass was explained by various spectral vegetation indices (VIs). The highest performance (explained deviance = 76%, RMSE = 5.15 Mg ha−1) was achieved with ratio and normalised difference VIs based on the green (R560), red-edge (R740 and R783), and near-infrared (R865) spectral bands. Heterogeneity of ground vegetation and resulting background effects seemed to limit model performance. The best performing VI (R740/R783) was used to predict plantation biomass that ranged from 0 to 46.7 Mg ha−1 (mean biomass 10.6 Mg ha−1). The modelling showed that multispectral data are suitable for assessing sisal leaf biomass at the plantation level and in individual blocks. Although these results demonstrate the value of Sentinel-2 red-edge bands at 20-m resolution, the difference from the best model based on green and near-infrared bands at 10-m resolution was rather small

Assessing Leaf Biomass of Agave sisalana Using Sentinel-2 Vegetation Indices

Biomass is a principal variable in crop monitoring and management and in assessing carbon cycling. Remote sensing combined with field measurements can be used to estimate biomass over large areas. This study assessed leaf biomass of Agave sisalana (sisal), a perennial crop whose leaves are grown for fibre production in tropical and subtropical regions. Furthermore, the residue from fibre production can be used to produce bioenergy through anaerobic digestion. First, biomass was estimated for 58 field plots using an allometric approach. Then, Sentinel-2 multispectral satellite imagery was used to model biomass in an 8851-ha plantation in semi-arid south-eastern Kenya. Generalised Additive Models were employed to explore how well biomass was explained by various spectral vegetation indices (VIs). The highest performance (explained deviance = 76%, RMSE = 5.15 Mg ha−1) was achieved with ratio and normalised difference VIs based on the green (R560), red-edge (R740 and R783), and near-infrared (R865) spectral bands. Heterogeneity of ground vegetation and resulting background effects seemed to limit model performance. The best performing VI (R740/R783) was used to predict plantation biomass that ranged from 0 to 46.7 Mg ha−1 (mean biomass 10.6 Mg ha−1). The modelling showed that multispectral data are suitable for assessing sisal leaf biomass at the plantation level and in individual blocks. Although these results demonstrate the value of Sentinel-2 red-edge bands at 20-m resolution, the difference from the best model based on green and near-infrared bands at 10-m resolution was rather small

Land Cover Map for Multifunctional Landscapes of Taita Taveta County, Kenya, Based on Sentinel-1 Radar, Sentinel-2 Optical, and Topoclimatic Data

Taita Taveta County (TTC) is one of the world’s biodiversity hotspots in the highlands with some of the world’s megafaunas in the lowlands. Detailed mapping of the terrestrial ecosystem of the whole county is of global significance for biodiversity conservation. Here, we present a land cover map for 2020 based on satellite observations, a machine learning algorithm, and a reference database for accuracy assessment. For the land cover map production processing chain, temporal metrics from Sentinel-1 and Sentinel-2 (such as median, quantiles, and interquartile range), vegetation indices from Sentinel-2 (normalized difference vegetation index, tasseled cap greenness, and tasseled cap wetness), topographic metrics (elevation, slope, and aspect), and mean annual rainfall were used as predictors in the gradient tree boost classification model. Reference sample points which were collected in the field were used to guide the collection of additional reference sample points based on high spatial resolution imagery for training and validation of the model. The accuracy of the land cover map and uncertainty of area estimates at 95% confidence interval were assessed using sample-based statistical inference. The land cover map has an overall accuracy of 81 ± 2.3% and it is freely accessible for land use planners, conservation managers, and researchers

Land Cover Map for Multifunctional Landscapes of Taita Taveta County, Kenya, Based on Sentinel-1 Radar, Sentinel-2 Optical, and Topoclimatic Data

Taita Taveta County (TTC) is one of the world’s biodiversity hotspots in the highlands with some of the world’s megafaunas in the lowlands. Detailed mapping of the terrestrial ecosystem of the whole county is of global significance for biodiversity conservation. Here, we present a land cover map for 2020 based on satellite observations, a machine learning algorithm, and a reference database for accuracy assessment. For the land cover map production processing chain, temporal metrics from Sentinel-1 and Sentinel-2 (such as median, quantiles, and interquartile range), vegetation indices from Sentinel-2 (normalized difference vegetation index, tasseled cap greenness, and tasseled cap wetness), topographic metrics (elevation, slope, and aspect), and mean annual rainfall were used as predictors in the gradient tree boost classification model. Reference sample points which were collected in the field were used to guide the collection of additional reference sample points based on high spatial resolution imagery for training and validation of the model. The accuracy of the land cover map and uncertainty of area estimates at 95% confidence interval were assessed using sample-based statistical inference. The land cover map has an overall accuracy of 81 ± 2.3% and it is freely accessible for land use planners, conservation managers, and researchers

Soil greenhouse gas emissions from a sisal chronosequence in Kenya

Sisal (Agave sisalana) is a climate-resilient crop grown on large-scale farms in semi-arid areas. However, no studies have investigated soil greenhouse gas (GHGs: CO2, N2O and CH4) fluxes from these plantations and how they relate to other land cover types. We examined GHG fluxes (Fs) in a sisal chronosequence at Teita Sisal Estate in southern Kenya. The effects of stand age on Fs were examined using static GHG chambers and gas chromatography for a period of one year in seven stands: young stands aged 1-3 years, mature stands aged 7-8 years, and old stands aged 13-14 years. Adjacent bushland served as a control site representing the surrounding land use type. Mean CO2 fluxes were highest in the oldest stand (56 +/- 3 mg C m(-2) h(-1)) and lowest in the 8-year old stand (38 +/- 3 mg C m(-2) h(-1)), which we attribute to difference in root respiration between the stand. All stands had 13-28% higher CO2 fluxes than bushland (32 +/- 3 mg C m(-2) h(-1)). CO2 fluxes in the wet season were about 70% higher than dry season across all sites. They were influenced by soil water content (W-S) and vegetation phenology. Mean N2O fluxes were very low (Peer reviewe

Assessing Leaf Biomass of Agave sisalana Using Sentinel-2 Vegetation Indices

Biomass is a principal variable in crop monitoring and management and in assessing carbon cycling. Remote sensing combined with field measurements can be used to estimate biomass over large areas. This study assessed leaf biomass of Agave sisalana (sisal), a perennial crop whose leaves are grown for fibre production in tropical and subtropical regions. Furthermore, the residue from fibre production can be used to produce bioenergy through anaerobic digestion. First, biomass was estimated for 58 field plots using an allometric approach. Then, Sentinel-2 multispectral satellite imagery was used to model biomass in an 8851-ha plantation in semi-arid south-eastern Kenya. Generalised Additive Models were employed to explore how well biomass was explained by various spectral vegetation indices (VIs). The highest performance (explained deviance = 76%, RMSE = 5.15 Mg ha−1) was achieved with ratio and normalised difference VIs based on the green (R560), red-edge (R740 and R783), and near-infrared (R865) spectral bands. Heterogeneity of ground vegetation and resulting background effects seemed to limit model performance. The best performing VI (R740/R783) was used to predict plantation biomass that ranged from 0 to 46.7 Mg ha−1 (mean biomass 10.6 Mg ha−1). The modelling showed that multispectral data are suitable for assessing sisal leaf biomass at the plantation level and in individual blocks. Although these results demonstrate the value of Sentinel-2 red-edge bands at 20-m resolution, the difference from the best model based on green and near-infrared bands at 10-m resolution was rather small

Open source modelling of rock aggregate resources in the Pirkanmaa Region

Non peer reviewe

Allometric models for estimating leaf biomass of sisal in a semi-arid environment in Kenya

Publisher Copyright: © 2021 The AuthorsBiomass is a key variable for crop monitoring and for assessing carbon stocks and bioenergy potential. This study aimed to develop an allometric model for predicting the dry leaf biomass of sisal, an agave plant with crassulacean acid metabolism grown for fibre production in the tropics and subtropics and whose biomass can be utilised as a feedstock to produce biogas through anaerobic digestion. The allometric model was used to estimate leaf biomass and productivity across different stand ages in a sisal plantation in semi-arid region in south-east Kenya (annual rainfall 611 mm and temperature 24.9 °C). Based on a sample of 38 leaves, the best predictor for biomass was leaf maximum width and plant height used as a combined variable in a log-log regression model (cross-validated R2 = 0.96 and root-mean-square error = 7.69 g). The mean productivity in nine 26- to 36-month-old plots was 11.1 Mg ha−1 yr−1, which could potentially yield approximately 3000 m3 CH4 ha−1 yr−1. The leaf biomass in 55 field plots (400 m2 in area) ranged from 2.7 to 42.7 Mg ha−1, with mean at 13.5 Mg ha−1, which equals to 6.3 Mg C ha−1. The yielded allometric equations can be utilised for predicting the leaf biomass of sisal in similar agro-ecological zones. The estimates on plantation biomass can be used in assessing the role of sisal plantations as a regional carbon storage. In addition, the results provide reference on the productivity of agave and crassulacean acid metabolism in semi-arid regions of East Africa, where such reports are few.Peer reviewe