Predicting Soil Organic Carbon and Nitrogen Content Using Airborne Laser Scanning in the Taita Hills, Kenya

Reducing greenhouse gas emissions and increasing carbon sequestration is critical for climate change mitigation. With the emergence of carbon markets and the development of compensatory mechanisms such as Reducing Emissions from Deforestation and Degradation in Developing Countries (REDD+), there is much interest in measurement and monitoring of soil organic carbon (SOC). Detailed information on the distribution of SOC and other soil attributes, such as nitrogen (N), across the landscape is necessary in order to locate areas where carbon stocks can be increased and loss of soil carbon slowed down. SOC has large spatial variability, which often demands intensive sampling in the field. Airborne laser scanning (ALS) provides very accurate information about the topography and vegetation of the measured area, and hence, possible means for improving soil properties maps. In this thesis, the aim was to study the feasibility of ALS and free of cost ancillary data for predicting SOC and N in a tropical study area. The study area is located in the Taita Hills, in South-Eastern Kenya, and has highly fluctuating topography ranging between 930–2187 m. Land cover in the Taita Hills is very heterogeneous and consists of forest, woodlands, agroforestry and croplands. The field data consisted of SOC and N measurements for 150 sample plots (0.1 ha). The soil samples along with several other soil and vegetation attributes were collected in 2013. ALS (Optech ALTM 3100, mean return density 11.4 m-1) data was acquired in February 2013. ALS data was pre-processed by classifying ground, low- and high vegetation, buildings and power wires. ALS point cloud was used to calculate two types of predictors for SOC and N: 1) topographical variables based on the high resolution digital terrain model (DTM) and 2) ALS metrics describing the vertical distribution and cover of vegetation. The ancillary datasets included spectral predictors based on Landsat 7 ETM+ time series and soil grids for Africa at 250 m resolution. In total, over 500 potential predictors were calculated for the modelling. Random Forest model was constructed from the selected variables and model performance was analysed by comparing the predicted values to the field measurements. The best model for SOC had pseudo R2 of 0.66 and relative root mean square error (RRMSE) of 30.98 %. Best model for N had pseudo R2 of 0.43 and RRMSE of 32.14 %. Usage of Landsat time series as ancillary dataset improved the modelling results slightly. For SOC, the most important variables were tangential curvature, maximum intensity and Landsat band 2 (green). Finally, the best model was applied for mapping SOC and N in the study area. The results of this study are in line with other remote sensing studies modelling soil properties in Africa. The soil properties in the study area do not correlate strongly with present vegetation and topography leading to intermediate modelling results.Kasvihuonepäästöjen vähentäminen ilmakehästä on kriittistä ilmastonmuutoksen hillitsemisen kannalta. Hiilimarkkinoiden ja erilaisten korvausmekanismien kehittyminen on lisännyt kiinnostusta maaperässä olevan orgaanisen hiilen mittaamiseen ja monitoroimiseen. Yksityiskohtainen tieto maaperän ominaisuuksista, kuten orgaanisen hiilen ja typen alueellisesta jakaumasta, voi auttaa löytämään alueita, joissa hiilen osuutta voidaan kasvattaa tai sen vähentymistä voidaan hidastaa. Maaperän hiilen vaihtelevasta spatiaalisesta jakaumasta johtuen kalliita kenttämittauksia tarvitaan runsaasti. Lentolaserkeilaus tarjoaa tarkkaa tietoa kuvatun alueen topografiasta ja kasvillisuudesta, mikä voisi olla hyödyllistä maaperän karttojen laadun parantamisessa. Tämän tutkimuksen tavoitteena oli selvittää, miten lentolaserkeilaus ja vapaasti saatavilla oleva lisäaineisto soveltuvat maaperän orgaanisen hiilen ja typen pitoisuuksien ennustamiseen. Tutkimusalue on Taitavuorilla Kaakkois-Keniassa, jossa topografia on hyvin vaihtelevaa, korkeuden vaihdellessa 930 ja 2187 metrin välillä. Taitavuorten maanpeite on hyvin heterogeenistä ja koostuu metsistä, metsämaasta, peltometsäviljelysmaista ja viljelysmaista. Tutkimuksessa käytetty kenttäaineisto koostuu 150:sta maaperän hiili- ja typpimittauksista 0.1 hehtaarin kokoisilta koealoilta. Maaperän mittaukset suoritettiin vuonna 2013. Lentolaserkeilausaineisto (Optech ALTM 3100) kuvattiin helmikuussa 2013. Kuvattu lentolaserkeilausaineisto esikäsiteltiin luokittelemalla maaperä, matala ja korkea kasvillisuus, rakennukset ja voimalinjat. Lentolaserkeilausaineistoa käytettiin kahden tyyppisten muuttujien laskennassa: 1) topografiamuuttujat, jotka laskettiin erittäin korkearesoluutioisesta korkeusmallista ja 2) kasvillisuuden vertikaalisesta rakenteesta ja peitosta kertoviin muuttujiin. Lisäaineistona analyysissä oli mukana spektraalista tietoa sisältävä Landsat ETM+ aikasarja, sekä maaperäruudukot Afrikasta 250 m:n spatiaalisella resoluutiolla. Yhteensä noin 500 muuttujaa laskettiin mallinnusta varten. Random Forest -malli rakennettiin valituista muuttujista ja mallien suorituskykyä arvioitiin vertaamalla ennustettuja arvoja havaittuihin arvoihin. Parhaan maaperän hiilimallin valeselitysaste oli 0.66 ja suhteellinen keskivirhe 30.98 %. Parhaan typpimallin valeselitysaste oli 0.43 ja suhteellinen keskivirhe 32.14 %. Tärkeimmät muuttujat maaperän hiilen ennustamiseen olivat tangentiaalinen kaarevuus (tangential curvature), maksimi-intensiteetti (maximum intensity) ja Landsatin kanava 2 (vihreä aallonpituus). Landsat aineiston käyttö avustavana aineistona johti pieniin parannuksiin mallinnuksessa. Lopulta maaperän hiili- ja typpikartat ennustettiin käyttämällä parhaita löydettyjä malleja. Tämän tutkimuksen tulokset ovat linjassa muiden kaukokartoitusta hyödyntävien maaperän ominaisuuksia tutkivien tutkimuksien kanssa. Maaperän ominaisuudet eivät korreloineet voimakkaasti kasvillisuuden ja topografian kanssa, mikä johti keskinkertaisiin tuloksiin

New computational methods for efficient utilisation of public data

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Significance of local topographic variables in commercial forest operations in KwaZulu-Natal, South Africa.

Masters Degree. School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Pietermaritzburg, 2019.The planning and management of forest operations is a complex task. This complexity is attributed to the variability of forest plantations’ site and topographic conditions. Therefore, there is a need for an integrated approach towards forest management and decision-making which offer a continuous review of forest operation systems used to increase site productivity. Therefore, the aim of this study was to determine the impact of local terrain on forest operations and forest productivity using GIS and statistical modelling in a commercial forest plantation in KwaZulu-Natal, South Africa. The first objective of the study focused on determining the influence of terrain variation on the productivity of commercial forestry using LiDAR-derived topographic factors. A 1m LiDAR-derived Canopy Height Model (CHM) and 30m digital elevation model (DEM) were used in the study. Four model scenarios were generated using LiDAR data in conjunction with a stepwise multiple linear regression. The results showed that elevation, aspect, clay content and slope were significant variables in influencing tree productivity (R2 > 0.9). Furthermore, a strong correlation was observed between observed tree heights and predicted tree height values (R2 = 0.88). The results of the study suggest that topographic variables strongly influence commercial tree species productivity. The second objective of the study determined optimal terrain classes based on a national terrain classification system developed by Erasmus (1994) for South African forestry regions. The integration of logistic regression in a GIS environment proved to accurately map suitable terrain classes (AUC = 0.93). This was achieved using a cost-effective 30 m DEM and SOTER-based soil parameter estimates (SOTWIS) data which was used to derive site topographic variables. The study demonstrates the use of integrated approaches for providing efficient and feasible means to apply terrain classification for current forestry practices. The study provides an effective framework for classifying ideal terrain conditions for forest management applications and forest operations. Overall, the study establishes the significance of local topography on commercial forest production and contributes towards enhancing management decision-making during spatial planning initiatives and operations

Spatial and temporal variations of carbon in global tropical forests using satellite and ground observations

Tropical forests play an important role in the global carbon cycle. Covering 7-10% of the Earth land surface, they contribute to more than half of carbon stock in the world’s forests. Spatial and temporal variations of canopy structure and carbon stock are thus key indicators of ecological processes associated with the changing climate. At macroscales, we evaluated the contributions of climate, soil and topography to the structural variations of pan-tropical forests. Using LiDAR observations from satellite, we built spatial regression models between the LiDAR-derived canopy height and abiotic variables. Results show these factors and spatial contextual information can explain more than 60% of the variations in the heights of these forests. Within the tropics, Amazonian forests contain nearly half of the tropical carbon stocks and thus a vital part to the global carbon budget. The impacts of droughts in Amazonia have been recorded as short-term tree mortality and biomass loss from inventory plots. Using interannual satellite LiDAR measurements from 2003 to 2008, we quantitatively assessed carbon lost after the 2005 Amazon drought. Through careful signal filtering and sampling strategies, we found a significant loss of carbon over the Amazon basin, turning the ecosystem to a net source of carbon at 0.63 PgC/yr (0.16-1.10 PgC). And there was no sign of complete recovery 3 years after the drought. Besides natural disturbances such as droughts, human activities vastly alter the carbon footprint in the tropics. Tropical secondary forests (SF), mainly restored from deforestation, are often identified as a major terrestrial carbon sink. We analyzed changes in SF from 2004 to 2014 in the Brazilian Amazon and found SF contribution to regional carbon sink was negligible, due to significant turnover and frequent clearing activities. But it has the capacity of more than 0.2 PgC/yr net sink to compensate for total emissions from deforestation, if policies to restore secondary forests are implemented and enforced. My dissertation studies provide a clearer picture of abiotic controls over the pan-tropical forests and a better understanding of the carbon dynamics in regions of post-drought Amazonia and secondary forests in the Brazilian Amazon

GIS-based decision support systems to minimise soil impacts in logging operations

Mechanised logging operations can leave negative impacts, like ruts, on forest soils. To avoid this, forestry planners and machine operators need decision support systems that can estimate soil trafficability and help to minimise soil impacts. The main objective of this thesis was to evaluate whether or how different data, stored in a geographic information system (GIS), can contribute to improved estimation of soil trafficability. Requirements for implementation of soil trafficability maps in forestry GIS applications were also described. A soil trafficability map, based on several GIS data using multi-criteria decision analysis (MCDA), was proposed in Paper I. Availability and implementation of soil trafficability maps, mainly depth-to-water (DTW) maps, in some European countries, was reviewed in Paper II. Effect of DTW map resolutions to predict soil moisture was evaluated in Paper IV, and the study showed that a spatial resolution of 1–2 m was sufficient. Risk for rutting was analysed in relation to field-measured and GIS data in Papers III, V and VI. GIS data included digital elevation models, DTW maps, hydrological data, soil type, and clay content maps. The results showed that planning forwarder trails and evaluating different alternatives can be improved by using a soil trafficability map. GIS data of high quality is required to achieve acceptable results. Easy or free access to soil trafficability maps facilitate their application in forestry operations. DTW maps, together with other data, can be used to estimate risk for rutting. Clay content maps and hydrological data, at current resolution, need further development but showed potential to predict risk for rutting. More studies are required to estimate temporal and spatial variability of soil trafficability maps. In conclusion, GIS-based decision support systems should be used for planning of logging operations to minimise risk for rutting

Classical gully spatial identification and slope stability modeling using high-resolution elevation and data mining technique

It is widely known that soil erosion is an issue of concern in soil and water quality, affecting agriculture and natural resources. Thus, scientific efforts must take into consideration the high-resolution elevation dataset in order to implement a precision conservation approach effectively. New advances such as LiDAR products have provided a basic source of information to enable researchers to identify small erosional landscape features. To fill this gap, this study developed a methodology based on data mining of hydrologic and topographic attributes associated with concentrated flow path identification to distinguish classic gully side walls and bed areas. At 0.91 Km2 region of the Keigley Branch-South Skunk River watershed, an area with gullies, we computed profile curvature, mean slope deviation, stream power index, and aspect gridded in 1-m pixel from Iowa LiDAR project. CLARA (CLustering LARge Applications) algorithm. An unsupervised clustering approach was employed on 913,495 points splitting the dataset in six groups, the number in agreement with within-group sum of squared error (WSS) statistical technique. In addition, a new threshold criteria termed gully concentrated flow (GCF) based upon data distribution of flow accumulation and mean overall slope were introduced to produce polylines that identified the main hydrographic flow paths, corresponding to the gully beds. Cluster #6 was classified as gully side walls. After distinguishing gullies and cliffs areas among points belonging to cluster 6, all six gullies were satisfactorily identified. The proposed methodology improves on existent techniques because identifies distinct parts of gullies which include side walls and bed zone. Another important concept is assessing gully slope stability in order to generate useful information for precision conservation planning. Although limit-equilibrium concept has been used widely in rock mechanics its application in precision conservation structures is relatively new. This study evaluated two multi-temporal surveys in a Western Iowa gullied area under the approach of soil stability regarding precision conservation practice The study computed factor of safety (FS) at the gully area, including headcut and gully side walls using digital elevation models originated from surveys conducted in 1999 and 2014. Outcomes of this assessment have revealed significantly less instability of the actual slopes compared to 1999 survey slopes. The internal friction angle (θ) had the largest effect on slope stability factor (S.D.1999 = 0.18, S.D.2014 = 0.24), according the sensitivity analysis, compared to variations of soil cohesion, failure plane angle and slab thickness. In addition, critically instable slopes within gully, based on units of the slope standard deviation, as a threshold, have produced an area of 61 m2 and 396 m2 considering the threshold of one and two slope standard deviation, respectively. The majority of these critical areas were located near the headcut and in the border of side walls. Based on current literature, association of processed material (geotextile) and crop cover with high root density might be an alternative to improve slope instability, but empirical tests are necessary to validate this approach. Nevertheless, the slope instability must include other factors that capture the dynamics of failure

Machine Learning Approaches for Natural Resource Data

Abstract Real life applications involving efficient management of natural resources are dependent on accurate geographical information. This information is usually obtained by manual on-site data collection, via automatic remote sensing methods, or by the mixture of the two. Natural resource management, besides accurate data collection, also requires detailed analysis of this data, which in the era of data flood can be a cumbersome process. With the rising trend in both computational power and storage capacity, together with lowering hardware prices, data-driven decision analysis has an ever greater role. In this thesis, we examine the predictability of terrain trafficability conditions and forest attributes by using a machine learning approach with geographic information system data. Quantitative measures on the prediction performance of terrain conditions using natural resource data sets are given through five distinct research areas located around Finland. Furthermore, the estimation capability of key forest attributes is inspected with a multitude of modeling and feature selection techniques. The research results provide empirical evidence on whether the used natural resource data is sufficiently accurate enough for practical applications, or if further refinement on the data is needed. The results are important especially to forest industry since even slight improvements to the natural resource data sets utilized in practice can result in high saves in terms of operation time and costs. Model evaluation is also addressed in this thesis by proposing a novel method for estimating the prediction performance of spatial models. Classical model goodness of fit measures usually rely on the assumption of independently and identically distributed data samples, a characteristic which normally is not true in the case of spatial data sets. Spatio-temporal data sets contain an intrinsic property called spatial autocorrelation, which is partly responsible for breaking these assumptions. The proposed cross validation based evaluation method provides model performance estimation where optimistic bias due to spatial autocorrelation is decreased by partitioning the data sets in a suitable way. Keywords: Open natural resource data, machine learning, model evaluationTiivistelmä Käytännön sovellukset, joihin sisältyy luonnonvarojen hallintaa ovat riippuvaisia tarkasta paikkatietoaineistosta. Tämä paikkatietoaineisto kerätään usein manuaalisesti paikan päällä, automaattisilla kaukokartoitusmenetelmillä tai kahden edellisen yhdistelmällä. Luonnonvarojen hallinta vaatii tarkan aineiston keräämisen lisäksi myös sen yksityiskohtaisen analysoinnin, joka tietotulvan aikakautena voi olla vaativa prosessi. Nousevan laskentatehon, tallennustilan sekä alenevien laitteistohintojen myötä datapohjainen päätöksenteko on yhä suuremmassa roolissa. Tämä väitöskirja tutkii maaston kuljettavuuden ja metsäpiirteiden ennustettavuutta käyttäen koneoppimismenetelmiä paikkatietoaineistojen kanssa. Maaston kuljettavuuden ennustamista mitataan kvantitatiivisesti käyttäen kaukokartoitusaineistoa viideltä eri tutkimusalueelta ympäri Suomea. Tarkastelemme lisäksi tärkeimpien metsäpiirteiden ennustettavuutta monilla eri mallintamistekniikoilla ja piirteiden valinnalla. Väitöstyön tulokset tarjoavat empiiristä todistusaineistoa siitä, onko käytetty luonnonvaraaineisto riittävän laadukas käytettäväksi käytännön sovelluksissa vai ei. Tutkimustulokset ovat tärkeitä erityisesti metsäteollisuudelle, koska pienetkin parannukset luonnonvara-aineistoihin käytännön sovelluksissa voivat johtaa suuriin säästöihin niin operaatioiden ajankäyttöön kuin kuluihin. Tässä työssä otetaan kantaa myös mallin evaluointiin esittämällä uuden menetelmän spatiaalisten mallien ennustuskyvyn estimointiin. Klassiset mallinvalintakriteerit nojaavat yleensä riippumattomien ja identtisesti jakautuneiden datanäytteiden oletukseen, joka ei useimmiten pidä paikkaansa spatiaalisilla datajoukoilla. Spatio-temporaaliset datajoukot sisältävät luontaisen ominaisuuden, jota kutsutaan spatiaaliseksi autokorrelaatioksi. Tämä ominaisuus on osittain vastuussa näiden oletusten rikkomisesta. Esitetty ristiinvalidointiin perustuva evaluointimenetelmä tarjoaa mallin ennustuskyvyn mitan, missä spatiaalisen autokorrelaation vaikutusta vähennetään jakamalla datajoukot sopivalla tavalla. Avainsanat: Avoin luonnonvara-aineisto, koneoppiminen, mallin evaluoint

Measuring Annual Sedimentation through High Accuracy UAV-Photogrammetry Data and Comparison with RUSLE and PESERA Erosion Models

Model-based soil erosion studies have increased in number, given the availability of geodata and the recent technological advances. However, their accuracy remains rather questionable since the scarcity of field records hinders the validation of simulated values. In this context, this study aims to present a method for measuring sediment deposition at a typical Mediterranean catchment (870 ha) in Greece through high spatial resolution field measurements acquired by an Unmanned Aerial Vehicle (UAV) survey. Three-dimensional modeling is considered to be an emerging technique for surface change detection. The UAV-derived point cloud comparison, applying the Structure-from-Motion (SfM) technique at the Platana sediment retention dam test site, quantified annual topsoil change in cm-scale accuracy (0.02–0.03 m), delivering mean sediment yield of 1620 m3 ± 180 m3 or 6.05 t ha−1yr−1 and 3500 m3 ± 194 m3 or 13 t ha−1yr−1 for the 2020–2021 and 2021–2022 estimation. Moreover, the widely applied PESERA and RUSLE models estimated the 2020–2021 mean sediment yield at 1.12 t ha−1yr−1 and 3.51 t ha−1yr−1, respectively, while an increase was evident during the 2021–2022 simulation (2.49 t ha−1yr−1 and 3.56 t ha−1yr−1, respectively). Both applications appear to underestimate the net soil loss rate, with RUSLE being closer to the measured results. The difference is mostly attributed to the model’s limitation to simulate gully erosion or to a C-factor misinterpretation. To the authors’ better knowledge, this study is among the few UAV applications employed to acquire high-accuracy soil loss measurements. The results proved extremely useful in our attempt to measure sediment yield at the cm scale through UAV-SfM and decipher the regional soil erosion and sediment transport pattern, also offering a direct assessment of the retention dams’ life expectancy.Greece and the European UnionPeer Reviewe

Measuring, modelling and managing gully erosion at large scales: A state of the art

Soil erosion is generally recognized as the dominant process of land degradation. The formation and expansion of gullies is often a highly significant process of soil erosion. However, our ability to assess and simulate gully erosion and its impacts remains very limited. This is especially so at regional to continental scales. As a result, gullying is often overlooked in policies and land and catchment management strategies. Nevertheless, significant progress has been made over the past decades. Based on a review of >590 scientific articles and policy documents, we provide a state-of-the-art on our ability to monitor, model and manage gully erosion at regional to continental scales. In this review we discuss the relevance and need of assessing gully erosion at regional to continental scales (Section 1); current methods to monitor gully erosion as well as pitfalls and opportunities to apply them at larger scales (section 2); field-based gully erosion research conducted in Europe and European Russia (section 3); model approaches to simulate gully erosion and its contribution to catchment sediment yields at large scales (section 4); data products that can be used for such simulations (section 5); and currently existing policy tools and needs to address the problem of gully erosion (section 6). Section 7 formulates a series of recommendations for further research and policy development, based on this review. While several of these sections have a strong focus on Europe, most of our findings and recommendations are of global significance.info:eu-repo/semantics/publishedVersio