QUANTIFYING GRASSLAND NON-PHOTOSYNTHETIC VEGETATION BIOMASS USING REMOTE SENSING DATA

Non-photosynthetic vegetation (NPV) refers to vegetation that cannot perform a photosynthetic function. NPV, including standing dead vegetation and surface plant litter, plays a vital role in maintaining ecosystem function through controlling carbon, water and nutrient uptake as well as natural fire frequency and intensity in diverse ecosystems such as forest, savannah, wetland, cropland, and grassland. Due to its ecological importance, NPV has been selected as an indicator of grassland ecosystem health by the Alberta Public Lands Administration in Canada. The ecological importance of NPV has driven considerable research on quantifying NPV biomass with remote sensing approaches in various ecosystems. Although remote images, especially hyperspectral images, have demonstrated potential for use in NPV estimation, there has not been a way to quantify NPV biomass in semiarid grasslands where NPV biomass is affected by green vegetation (PV), bare soil and biological soil crust (BSC). The purpose of this research is to find a solution to quantitatively estimate NPV biomass with remote sensing approaches in semiarid mixed grasslands. Research was conducted in Grasslands National Park (GNP), a parcel of semiarid mixed prairie grassland in southern Saskatchewan, Canada. Multispectral images, including newly operational Landsat 8 Operational Land Imager (OLI) and Sentinel-2A Multi-spectral Instrument (MSIs) images and fine Quad-pol Radarsat-2 images were used for estimating NPV biomass in early, middle, and peak growing seasons via a simple linear regression approach. The results indicate that multispectral Landsat 8 OLI and Sentinel-2A MSIs have potential to quantify NPV biomass in peak and early senescence growing seasons. Radarsat-2 can also provide a solution for NPV biomass estimation. However, the performance of Radarsat-2 images is greatly affected by incidence angle of the image acquisition. This research filled a critical gap in applying remote sensing approaches to quantify NPV biomass in grassland ecosystems. NPV biomass estimates and approaches for estimating NPV biomass will contribute to grassland ecosystem health assessment (EHA) and natural resource (i.e. land, soil, water, plant, and animal) management

How does the global Moderate Resolution Imaging Spectroradiometer (MODIS) Fraction of Photosynthetically Active Radiation (FPAR) product relate to regionally developed land cover and vegetation products in a semi-arid Australian savanna?

Spatio-temporally variable information on total vegetation cover is highly relevant to water quality and land management in river catchments adjacent to the Great Barrier Reef, Australia. A time series of the global Moderate Resolution Imaging Spectroradiometer (MODIS) Fraction of Photosynthetically Active Radiation (FPAR; 2000-2006) and its underlying biome classification (MOD12Q1) were compared to national land cover and regional, remotely sensed products in the dry-tropical Burdekin River. The MOD12Q1 showed reasonable agreement with a classification of major vegetation groups for 94% of the study area. We then compared dry-seasonal, quality controlled MODIS FPAR observations to (i) Landsat-based woody foliage projective cover (wFPC) (2004) and (ii) MODIS bare ground index (BGI) observations (2001-2003). Statistical analysis of the MODIS FPAR revealed a significant sensitivity to Landsat wFPC-based Vegetation Structural Categories (VSC) and VSC-specific temporal variability over the 2004 dry season. The MODIS FPAR relation to 20 coinciding MODIS BGI dry-seasonal observations was significant (ρ < 0.001) for homogeneous areas of low wFPC. Our results show that the global MODIS FPAR can be used to identify VSC, represent VSC-specific variability of PAR absorption, and indicate that the amount, structure, and optical properties of green and non-green vegetation components contribute to the MODIS FPAR signal

Mapping fractional woody cover in an extensive semi-arid woodland area at different spatial grains with Sentinel-2 and very high-resolution data

Woody canopy cover is an essential variable to characterize and monitor vegetation health, carbon accumulation and land–atmosphere exchange processes. Remote sensing-based global woody and forest cover maps are available, yet with varying qualities. In arid and semi-arid areas, existing global products often underestimate the presence of woody cover due to the sparse woody cover and bright soil background. Case studies on smaller regions have shown that a combination of collected field data and medium-to-high resolution free satellite data (e.g., Landsat / Sentinel-2) can provide woody cover estimates with practically-sufficient accuracies. However, most earlier studies focused on comparably small regions and relied on costly field data. Here, we present a fully remote sensing-based work-flow to derive woody cover estimates over an area covering more than 0.5 million km2. The work-flow is showcased over the Zagros Mountains, a semi-arid mountain range covering western Iran, the northeast of Iraq and some smaller fraction of southeast Turkey. We use the Google Earth Engine to create homogeneous Sentinel-2 mosaics of the region using data from several years. These data are combined with reference woody cover values derived by a semi-automatic procedure from Google® and Bing® very high resolution (VHR) imagery. Several random forest (RF) models at different spatial grains were trained and at each grain validated with iterative splits of the reference data into training and validation sets (100 repetitions). Best results (considering the trade-off between model performance and spatial detail) were obtained for the model with 40 m spatial grain which showed stable relationships between the VHR-derived reference data and the Sentinel-2 based estimates of woody cover density. The model resulted in median values of coefficient of determination (R2) and RMSE of 0.67 and 0.11, respectively. Our work-flow is potentially also applicable to other arid and semi-arid regions and can contribute to improve currently available global woody cover products, which often perform poorly in semi-arid and arid regions. Comparisons between our woody cover products with common global woody or forest-cover products indicate a clear superiority of our approach. In future studies, these results may be further improved by taking into account regional differences in the drivers of woody-cover patterns along the environmental gradient of the Zagros area

Spatio-temporal mixed pixel analysis of savanna ecosystems : a review

Reliable estimates of savanna vegetation constituents (i.e., woody and herbaceous vegetation) are essential as they are both responders and drivers of global change. The savanna is a highly heterogenous biome with high variability in land cover types while also being very dynamic at both temporal and spatial scales. To understand the spatial-temporal dynamics of savannas, using Earth Observation (EO) data for mixed-pixel analysis is crucial. Mixed pixel analysis provides detailed land cover data at a sub-pixel level which are essential for conservation purposes, understanding food supply for herbivores, quantifying environmental change, such as bush encroachment, and fuel availability essential for understanding fire dynamics, and for accurate estimation of savanna biomass. This review paper consulted 197 studies employing mixed-pixel analysis in savanna ecosystems. The review indicates that studies have so far attempted to resolve the savanna mixed-pixel issues by using mainly coarse resolution data, such as Terra-Aqua MODIS and AVHRR and medium resolution Landsat, to provide fractional cover data. Hence, there is a lack of spatio-temporal mixed-pixel analysis for savannas at high spatial resolutions. Methods used for mixed-pixel analysis include parametric and non-parametric methods which range from pixel-unmixing models, such as linear spectral mixture analysis (SMA), time series decomposition, empirical methods to link the green vegetation parameters with Vegetation Indices (VIs), and machine learning methods, such as regression trees (RT) and random forests (RF). Most studies were undertaken at local and regional scale, highlighting a research gap for savanna mixed pixel studies at national, continental, and global level. Parametric methods for modeling spatio-temporal mixed pixel analysis were preferred for coarse to medium resolution remote sensing data, while non-parametric methods were preferred for very high to high spatial resolution data. The review indicates a gap for long time series spatio-temporal mixed-pixel analysis of savannas using high resolution data at various scales. There is potential to harmonize the available low resolution EO data with new high-resolution sensors to provide long time series of the savanna mixed pixel, which, according to this review, is missing.The Deutscher Akademischer Austauschdienst and the Federal Ministry of Education and Research (BMBF) within the framework of the Strategy “Research for Sustainability” (FONA).http://www.mdpi.com/journal/remotesensingpm2022Geography, Geoinformatics and Meteorolog

Fire

Vegetation plays a crucial role in regulating environmental conditions, including weather and climate. The amount of water and carbon dioxide in the air and the albedo of our planet are all influenced by vegetation, which in turn influences all life on Earth. Soil properties are also strongly influenced by vegetation, through biogeochemical cycles and feedback loops (see Volume 1A—Section 4). Vegetated landscapes on Earth provide habitat and energy for a rich diversity of animal species, including humans. Vegetation is also a major component of the world economy, through the global production of food, fibre, fuel, medicine, and other plantbased resources for human consumptio

NASA's surface biology and geology designated observable: A perspective on surface imaging algorithms

The 2017–2027 National Academies' Decadal Survey, Thriving on Our Changing Planet, recommended Surface Biology and Geology (SBG) as a “Designated Targeted Observable” (DO). The SBG DO is based on the need for capabilities to acquire global, high spatial resolution, visible to shortwave infrared (VSWIR; 380–2500 nm; ~30 m pixel resolution) hyperspectral (imaging spectroscopy) and multispectral midwave and thermal infrared (MWIR: 3–5 μm; TIR: 8–12 μm; ~60 m pixel resolution) measurements with sub-monthly temporal revisits over terrestrial, freshwater, and coastal marine habitats. To address the various mission design needs, an SBG Algorithms Working Group of multidisciplinary researchers has been formed to review and evaluate the algorithms applicable to the SBG DO across a wide range of Earth science disciplines, including terrestrial and aquatic ecology, atmospheric science, geology, and hydrology. Here, we summarize current state-of-the-practice VSWIR and TIR algorithms that use airborne or orbital spectral imaging observations to address the SBG DO priorities identified by the Decadal Survey: (i) terrestrial vegetation physiology, functional traits, and health; (ii) inland and coastal aquatic ecosystems physiology, functional traits, and health; (iii) snow and ice accumulation, melting, and albedo; (iv) active surface composition (eruptions, landslides, evolving landscapes, hazard risks); (v) effects of changing land use on surface energy, water, momentum, and carbon fluxes; and (vi) managing agriculture, natural habitats, water use/quality, and urban development. We review existing algorithms in the following categories: snow/ice, aquatic environments, geology, and terrestrial vegetation, and summarize the community-state-of-practice in each category. This effort synthesizes the findings of more than 130 scientists

ENVIRONMENTAL SECURITY AND SEASONAL VARIABILITY:REMOTE SENSING AND MODELING APPLICATION FOR THE MONITORING OF SAHELIAN NATURAL RESOURCES

Il lavoro sviluppato in questa Tesi si \ue8 incentrato sullo studio dei sistemi pascolivi delle regioni del Sahel, Africa Occidentale, tramite tecniche e strumenti del telerilevamento satellitare. L\u2019area oggetto di studio \ue8 una fascia di savana semi-arida, rappresentate la zona di transizione tra il Sahara a nord e le foreste del golfo di Guinea a sud. La regione \ue8 caratterizzata da ana marcata stagionalit\ue0, con una breve stagione umida (da Giugno ad Ottobre) in cui concentra gran parte delle produzione di biomassa vegetale e di conseguenza la produzione di derrate alimentari, seguita da una lunga stagione secca (Novembre-Maggio). A seconda della distanza dal Sahara le precipitazioni medie annuali vanno dai 150 mm annui ai 500, con elevata variabilit\ue0 tra le annate. In questo sistema cos\uec mutevole la pastorizia transumante \ue8 l\u2019attivit\ue0 antropica che meglio si adattata alle dinamiche stagionali. Difatti le uniche fonti di cibo sono date dalla pastorizia e, ove possibile, da agricoltura di sussistenza di specie molto rustiche come il Miglio (Pennisetum glaucum). Nonostante questi adattamenti la regione ha subito una serie di crisi umanitarie a partire dagli anni 70\u2019 del secolo scorso, causate da un brusco calo delle precipitazioni annuali. Il fenomeno climatico \ue8 risultato essere dovuto ad anomalie di temperature dell\u2019oceano Atlantico, similmente al fenomeno de El Ni\uf1o. Nonostante le piogge siano in lenta ripresa dall\u2019inizio degli anni 90\u2019, ricorrenti crisi umanitarie continuano ad interessare l\u2019area (l\u2019ultima nel 2010), motivo per cui le strategie da adottare per incrementare la sicurezza alimentare dell\u2019area rimangono questioni dibattute. In particolare, essendo il Sahel un\u2019area marginale a ridosso di una zona iper-arida, non vi sono gli estremi per attuare due comuni strategie di food security, l\u2019incremento delle aree coltivate e l\u2019intensificazione delle produzioni. In questo contesto, in cui strategie top-down sono inefficaci o dannose, \ue8 il monitoraggio del territorio che riveste un ruolo cruciale. In particolare in un\u2019area semi-naturale vasta come quella Saheliana, gli strumenti del telerilevamento satellitare sono strategici grazie alla loro capacit\ue0 di fornire dati spazializzati ed ad elevata risoluzione temporale. Scopo del lavoro \ue8 stato quello di contribuire a due aspetti del monitoraggio delle risorse naturali: lo studio di serie storiche di dati satellitari per individuare zone sottoposte a cronico degrado e studiare parametri correlati allo sviluppo della biomassa vegetale ad al suo stato idrico. Mentre il primo lavoro vuole dare informazioni utili alla pianificazione della gestione delle risorse naturali, il secondo vuole fornire informazioni in grado di fotografare in tempo reale l\u2019andamento della stagione corrente. La prima parte del lavoro ha riguardo il confronto sull\u2019intera Africa Occidentale tra il 1998 e il 2009) dei i trend delle cumulate stagionali di NDVI come proxy dello sviluppo vegetazionale, e delle precipitazioni in quanto variabile climatiche guida. I risultati hanno confermato che larga parte del territorio saheliano ha visto queste due variabili come perfettamente concordi durante il decennio passato. Tuttavia sono state evidenziate aree in cui i trend di produzione vegetale non sono spiegati dalle piogge. Aree in cui le produzioni sono aumentate nonostante la sostanziale stabilit\ue0 delle precipitazioni (Anomalous Greening, AG) risultano pi\uf9 frequenti nelle aree pi\uf9 meridionali dell\u2019Africa Occidentale ove \ue8 preponderante l\u2019attivit\ue0 agricola (West Sudanian savannah, 46% degli AG rilevati), mentre zone localizzate di anomalo decremento dell\u2019NDVI (Anomalous Degradation, AD) sono state rilevate nelle zone pi\uf9 aride del Sahel (Sahelian Acacia savannah, 59% degli AD rilevati). La analisi condotte a scala pi\uf9 di dettaglio con immagini ad alta risoluzione (30 m) hanno mostrato come queste anomalie si correlino ad usi e coperture del suolo differenti, con l\u2019AG in aree agricole l\u2019AD in aree marginali ove \ue8 praticabile unicamente la pastorizia. Due casi particolari di AG hanno mostrato eventi particolarmente drammatici in Chad e in Sudan. Entrambi i fenomeni sono risultati, da remoto, in un incremento dello sviluppo vegetazionale non legato alle piogge, dovuto al ritiro delle acque del lago Chad ed all\u2019abbandono delle terre di pascolo a seguito del conflitto del Darfur (2005-2006). I risultati sino a qui ottenuti permettono di sviluppare una mappatura tematica di aree localizzate soggette a cronico degrado, evidenziando in un sistema semi-naturale largamente legato alle precipitazioni zone in cui altre variabili vanno ad incidere sullo sviluppo vegetazionale. Queste possono essere approfondite dagli esperi locali, in modo da verificare se una popolazione rurale in continua crescita demografia sia incidendo sulla capacit\ue0 di carico dell\u2019ecosistema. La seconda parte del lavoro si \ue8 concentrata sullo stima dello stress idrico e della biomassa, due variabili fondamentali nel monitoraggio delle risorse naturali e pascolive in aree semi-aride. Serie temporali di frazione evapotraspirativa (EF) a bassa risoluzione sono state ottenute grazie alla relazione tra albedo e temperature superficiale. L\u2019EF \ue8 una componente del bilancio energetico ed \ue8 strettamente correlata con la disponibilit\ue0 idrica per la pianta. Le stime risultano avere dinamiche spaziotemporali coerenti con quelle che sono le dinamiche ecologiche della regione (piogge, fase vegetativa etc.) . Inoltre, l\u2019EF \ue8 risultata altamente correlata con flussi energetici misurati a terra da una stazione eddy covariance in Niger (r2 > 0.7). Il metodo implementato \ue8 di sicura utilit\ue0 per la stima dello stress idrico su vaste aree come quella Saheliana, frequentemente interessata da siccit\ue0 e piogge scarse. Stime di produzioni di biomassa sono state ottenute dal prodotto operativo satellitare Dry Matter Productivity (DMP) basato su di un modello di Light Use Efficiency (LUE). Le stime satellitari sono state valutate grazie al confronto con dati di produzione di biomassa pascoliva in 46 siti di misura in Niger lungo 10 anni (2000-2009). Le stime da remoto riportano valori di biomassa (kg/ha) in linea con le produzioni medie annuali dell\u2019area, tra i 200 kg/ha (aree iper-aride in annate sfavorevoli) e i 2000 kg/ha (pascoli altamente produttivi). Tuttavia le correlazioni coi dati di campo risultano basse (r2<0.3), ed il lavoro propone due approcci per incrementare l\u2019accuratezza del modello satellitare. La prima consiste nell\u2019integrazione dell\u2019EF come fattore di efficienza di disponibilit\ue0 idrica, attualmente non considerata dal DMP. L\u2019EF ha permesso di incrementare la capacit\ue0 del modello di LUE di spiegare la variabilit\ue0 dei dati di campo, specialmente su quei siti ove \ue8 pi\uf9 marcata la carenza idrica. Inoltre \ue8 stato verificato che il modello pu\uf2 incrementare la sua accuratezza nel caso in cui diverse Radiation Use Efficency (RUE) siano considerate, e seconda delle differenti coperture vegetali presenti al suolo. Le biomasse di queste \u201cunit\ue0 ecologiche\u201d presentano correlazioni staticamente differenti con le stime satellitari, e si differenziano tra di loro per la loro produttivit\ue0 media (max NDVI) e la loro fenologia (inizio della stagione, SoS). In conclusione, una stima satellitare di biomassa corretta per la disponibilit\ue0 idrica e l\u2019efficienza d\u2019uso della radiazione da parte delle diverse specie vegetali, una volta prodotta operativamente potr\ue0 fornire indicazioni sulla capacit\ue0 di carico dei pascoli nel corso della stagione, permettendo, se necessario, di produrre tempestive indicazioni sulle aree soggette a criticit\ue0.The research thesis here discussed focused on the Sahelian semi-arid rangeland, a region characterized by strong rainfall seasonality, with few dry months followed by a long dry season. In that area rangeland vegetation and human livelihoods of pastoralism and rainfed crop relies on this peculiar climatic condition. Unfavorable years whit poor or erratic rain results in reducing food supply from agropastoral activities possibly creating food insecurity condition. The work conducted address to main aspects of natural resources monitoring: long term study to identify critical condition that require further analysis to assess potential unbalanced human activities and near real time production of herbaceous biomass relate parameters to support on-going season early warning. In order to achieve the first goal satellite time-series of vegetation index and estimated rainfall were exploited (1998-2009) to identify areas where the two variables have opposite trends. These areas of anomalous hot spots highlight situations where the trend in the development of vegetation is locally driven by other factors mainly linked to human activity, rather than climatic driving force. In the humid regions of the southern part of the study area an increase of NDVI was observed even in conditions where rainfall remained stable (i.e. no significant trend), or even decreased (anomalous greening). These patches of increased NDVI are associated to crop land and savannah land cover classes. A number of hot spots of anomalous conditions along the boundary between the Sahelian and the Sahelian-Sudanian zones were analyzed in details using multi-temporal Landsat TM/ETM+ images and a more detailed analysis was conducted on a test area in Niger analyzing the anomalies in terms in changes of land cover and land use through years. The analysis of changes occurred between pairs of images acquired over the same area confirmed at local scale the trends of land degradation or recovery identified at the coarser resolution of 1km. It is important to underline that these anomalous situations are driven by local causes. Anomalous greening occurring north of Lake Chad is indicative of a critical environmental situation: the shrinking of Lake Chad has uncovered new lands colonized by new agricultural fields. On the contrary, small pockets anomalous degradation have been identified mainly in the Northern part of the study area, in the belt from West Mali to the Chad-Sudan border, which is well-known as fragile zone, where increasing population and human activity (rainfed agriculture, pastoralism and wood exploitation) are in instable equilibrium. Their strong dependence on climatic conditions determines frequent humanitarian crises due to food shortage. In Niger anomalous greening corresponds to the intensification of cropping in a fertile floodplain, whereas in Western Sudan it is associated to the abandonment of agro-pastoral land as a consequence of Darfur conflict. In areas where anomalous vegetation degradation is observed, the demographic framework and associated increase of the exploitation of environmental resources provide the general framework but are not sufficient to explain the local patterns. This result would be a support for natural resources exploitation planning, highlighting local chronic rangeland condition that need detailed analysis to identify causes and specific strategies to compensate the negative effect. The second part of the thesis focused on the estimation of two crucial variables in rangeland monitoring, the water availability for vegetation and the biomass production. Time series of evaporative fraction (EF), strongly linked to the vegetation water status and able to increase the performances of biomass estimation , were estimated from low resolution satellite data exploiting the albedo vs. land surface temperature relation. EF satellite derived resulted highly correlated to flux tower evapotranspiration (ET) measurements. In order to monitoring regional biomass the reliability of an operational LUE based product called Dry Matter Productivity (DMP) was evaluated in Niger rangeland thanks to ground biomass measurements on 46 sites over 10 years. In order to improve this useful biomass prediction at large scales the contribution of EF as a water stress efficiency in DMP algorithm was tested. Moreover the DMP performances were analyzed in relation to different ecological units, homogeneous in terms of vegetation cover and vegetation seasonal behaviour. Results suggest and discuss feasible LUE modelling improvement over the Sahel, taking into account satellite estimation of water availability and different radiation use efficiency for distinct plant communities. In conclusion, satellite biomass estimation corrected by water availability and including eco-types radiation use efficiency, once operationally produced and validated, could provide the necessary information for i) the creation of near real time bulletin of ongoing season and ii) if the case, the identification potential critical situation occurrence due to food shortage