Modeling Biomass Production in Seasonal Wetlands Using MODIS NDVI Land Surface Phenology

Plant primary production is a key driver of several ecosystem functions in seasonal marshes, such as water purification and secondary production by wildlife and domestic animals. Knowledge of the spatio-temporal dynamics of biomass production is therefore essential for the management of resources—particularly in seasonal wetlands with variable flooding regimes. We propose a method to estimate standing aboveground plant biomass using NDVI Land Surface Phenology (LSP) derived from MODIS, which we calibrate and validate in the Doñana National Park’s marsh vegetation. Out of the different estimators tested, the Land Surface Phenology maximum NDVI (LSP-Maximum-NDVI) correlated best with ground-truth data of biomass production at five locations from 2001–2015 used to calibrate the models (R2 = 0.65). Estimators based on a single MODIS NDVI image performed worse (R2 ≤ 0.41). The LSP-Maximum-NDVI estimator was robust to environmental variation in precipitation and hydroperiod, and to spatial variation in the productivity and composition of the plant community. The determination of plant biomass using remote-sensing techniques, adequately supported by ground-truth data, may represent a key tool for the long-term monitoring and management of seasonal marsh ecosystems.We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI).David Aragonés, Isabel Afán, Ricardo Díaz-Delgado and Diego García Díaz (EBD-LAST) provided support for remote-sensing and LSP analyses. Alfredo Chico, José Luis del Valle and Rocío Fernández Zamudio (ESPN, ICTS-RBD) provided logistic support and taxonomic expertise during the field work (validation dataset). Ernesto García and Cristina Pérez assisted with biomass harvesting and processing (calibration dataset). Gerrit Heil provided support in the project design. This study received funding from Ministerio de Medio Ambiente-Parque Nacional de Doñana, Consejeria de Medio Ambiente, Junta de Andalucia (1999–2000): RNM118 Junta de Andalucia (2003); the European Union’s Horizon 2020 Research and Innovation Program under grant agreement No. 641762 to ECOPOTENTIAL project; and the Spanish Ministry of Economy, Plan Estatal de I+D+i 2013–2016, under grant agreement CGL2016-81086-R to GRAZE project

Integração da condutividade eléctrica do solo e índices obtidos por imagens de satélite para gestão diferenciada da fertilização em pastagens

As pastagens de sequeiro no Alentejo, região situada no Sul de Portugal, constituem a base da alimentação dos animais em sistemas de produção extensivos. Ocupam normalmente solos que apresentam reduzida fertilidade mas, simultaneamente, grande variabilidade espacial. Esta resulta da conjugação de factores como: relevo ondulado, variabilidade intrínseca ao nível da rocha-mãe originária, existência de afloramentos rochosos, influência das árvores ou o efeito do pastoreio animal.A utilização de pastagens biodiversas (que incluem leguminosas, potencialmente fixadoras do azoto atmosférico) e o predomínio nesta região da rocha-mãe originária rica em potássio, determinam como prática comum em pastagens a aplicação anual e homogénea de adubos fosfatados no final do Outono. A aplicação racional destes factores de produção exige o conhecimento da variabilidade do solo e da resposta da cultura, para além da utilização de tecnologias de aplicação variável (VRT). Os distribuidores VRT são já comuns, especialmente entre os prestadores de serviços. Exige-se, por outro lado, que tecnologias expeditas possam identificar zonas com características (do solo e do desenvolvimento da cultura) semelhantes, conhecidas como “zonas homogéneas de gestão”.Neste trabalho monitorizou-se, em 2018, uma parcela de cerca de 25 ha de pastagem sob montado de azinho, pastoreada por bovinos, situada na Herdade da Mitra, da Universidade de Évora. Foi realizado um levantamento da condutividade eléctrica aparente do solo com um sensor Veris 2000 XA e foram estabelecidos 24 pontos de amostragem, cada com uma área de 900 m2 (quadrados com 30m de lado). Estes foram seleccionados em zonas sem árvores para permitir as leituras dos índices obtidos a partir das imagens de satélite (Sentinel-2) sem interferência da vegetação arbórea. Foram capturados os registos históricos (2017 e 2018) do NDVI e do NDWI nos 24 pontos de amostragem. Foram recolhidas amostras de solo a 30cm de profundidade nos pontos de amostragem para determinação dos teores de matéria orgânica, do pH, do fósforo e do potássio. Foram realizadas medições com sensores próximos diversos para determinação de vários parâmetros: (i) cone índex, para determinação da resistência do solo à penetração; (ii) sensor óptico activo, para determinação do NDVI; (iii) sonda de capacitância, para estimativa da produtividade da pastagem.Após esta primeira etapa de levantamento da variabilidade do solo e da cultura, procedeu-se ao tratamento das várias camadas de informação num Sistema de Informação Geográfica, levando à obtenção de mapas temáticos e dando início à interpretação e análise dos dados obtidos. O culminar deste trabalho levou à elaboração de um mapa de prescrição diferenciada de adubo fosfatado, tendo em conta a variabilidade do solo e da cultura e contribuindo para a sustentabilidade deste ecossistema

Remote Detection of Disturbance from Motorized Vehicle Use in Appalachian Wetlands

Wetland disturbance from motorized vehicle use is a growing concern across the Appalachian coalfields of southwestern Virginia and portions of adjacent states, particularly as both extractive industries and outdoor recreation development expand in regional communities. However, few attempts have been made in this region or elsewhere to adapt approaches that can assist researchers and land managers in remotely identifying and monitoring wetland habitats disturbed by motorized vehicle use. A comparative analysis of wetlands impacted and unimpacted by off-road vehicle activity at a public recreation area in Tazewell County, Virginia was conducted to determine if and how a common, satellite-derived index of vegetation health, normalized difference vegetation index (NDVI), can remotely detect wetland disturbance. NDVI values were consistently lower in wetlands impacted by several years of off-road vehicle use when compared to adjacent, unimpacted sites, with statistically-significant NDVI coldspots growing in size in impacted wetlands across the same time period. While considerations exist related to the resolution of data sources and the identification of specific modes of disturbance, NDVI and associated spatial analysis tools may provide a simple and cost-effective way for researchers and land managers to remotely monitor rates of wetland disturbance across mountainous portions of the eastern United States

Assessing the effect of rotational grazing adoption in Iberian silvopastoral systems with Normalized Difference Vegetation Index time series

Adaptive Multi-Paddock (AMP) is a grazing system which combines intensive, rapid grazing livestock rotation with relatively short grazing periods and long recovery time after grazing. The study assesses, under Mediterranean silvopastoral systems, changes in pasture phenology and spatial variability after adopting the AMP under contrasting land cover (Wooded Grassland vs Grassland ) with a remote sensing approach based on the time-series analysis of Normalized Difference Vegetation Index (NDVI) from remote sensing through Landsat satellite. The study revealed an overall positive effect of rotational grazing on pasture phenology and NDVI spatial variability. The AMP adoption resulted in higher estimated values of NDVI at the beginning (under grassland land cover), the end, and the peak of the growing season, while no differences were observed in parameters estimating the length of the growing season. The spatial variability of NDVI was always lower under AMP than in continuously grazed areas, except in the early stages of the growing season under grassland land cover. The results suggested that in a relatively short period (4-5 years), the AMP grazing system can represent a strategy to improve forage availability and exploitation by grazing animals under low stocking rates in extensively managed Mediterranean silvopastoral systems

Observation of Saltwater Marsh Resiliency to Sea Level Rise in Jamaica Bay, Long Island’s Oyster and Great South Bay: 1995 to 2020

Abstract: It is hypothesized that sea-level rise and storm events are influencing the disappearance of marsh island vegetation in Long Island’s South Shore Estuary. A significant negative correlation was found when comparing mean local sea level rise to marsh vegetation NDVI values in Jamaica Bay, Queens, and Nassau County

Järviruovikon (Phragmites australis) biomassan arviointi Sentinel 2 -satelliittikuvista

Järviruoko (Phragmites australis) on 1-3 metriä korkea putkilokasveihin kuuluva kasvi, joka tulee toimeen monenlaisissa ympäristöissä. Järviruokoa kasvaa Suomessa muun muassa rannikoilla, matalissa järvissä ja avoimilla vesialueilla. Järviruoko on levinnyt viimeisten vuosikymmenten aikana voimakkaasti. Järviruoko kasvaa laajoina ja tiheinä monokulttuureina. Levitessään järviruoko valtaa tilaa muulta kasvustolta. Ruovikoituminen aiheuttaa vesistöjen umpeenkasvua ja vähentää virkistyskäyttöä. Mädäntyessään ruovikoista vapautuu metaanipäästöjä, joista aiheutuu voimakkaita hajuhaittoja. Järviruo’on levinneisyys- ja biomassatiedoista hyötyvät tutkijat, asiantuntijat sekä järviruokoyrittäjät. Tietoa järviruovikoista tarvitaan niin kaupallisista kuin ympäristönhoidollisista syistä. Kestävä järviruo’on hyödyntäminen on keino ruovikoitumisen estämiseksi, kunhan se tehdään oikea-aikaisesti. Järviruovikoiden maanpäällisen biomassan (AGB) arviointiin vaikuttaa se, että vaikka ruovikot esiintyvät usein monokulttuureina, ne ovat heterogeenisia biomassaltaan. Järviruovikoiden kartoittaminen maastotöinä vie paljon aikaa ja resursseja. Kaukokartoitusmenetelmien avulla saadaan tarkkoja ajantasaisia tuloksia, joita voidaan hyödyntää järviruovikoiden biomassan arvioinnissa.Tutkielmassa selvitettiin, kuinka tarkasti tutkimukseen valituilla menetelmillä ja satelliittikuvilla voidaan selvittää järviruovikon biomassa koko tutkimusalueella Paraisten kaupungin alueella Varsinais-Suomessa. Aineistona tutkielmassa käytettiin Sentinel 2 -satelliittikuvia päiviltä 02.10.2020 ja 30.08.2022. Lisäksi kolmelta tutkimusalueelta leikattiin järviruokoa näyteleikkuita varten. Kassorin ja Rapusvikenin lahdelta näytteet leikattiin 28.10.2020 ja Brattnäsvikenin lahdelta 08.09.2022. Yhteensä biomassatietoja saatiin 14 näyteleikkuupisteestä.Valittu ohjatun luokittelun Random Forest -algoritmi luokitteli järviruovikon Sentinel 2 -satelliittikuvista kohtuullisen tarkasti. Tässä tutkimuksessa tuottajan tarkkuus molemmissa kuvissa oli järviruovikon osalta 85 prosenttia ja käyttäjän tarkkuus 58 ja 68 prosenttia. Useamman selittäjän lineaarinen regressiomalli soveltuu hyvin järviruovikoiden biomassan arviointiin Etelä-Suomen olosuhteissa. Lineaarisen regressiomallin selitysaste 2020 kuvalle oli 47 prosenttia ja 2022 kuvalle 94,5 prosenttia. Biomassa-arviot olivat lähellä näyteleikkuita. Biomassa-arvion onnistumisprosentti näyteleikkuiden osalta oli 89 prosenttia ja biomassa-arviot ovat linjassa aikaisempien tutkimusten kanssa. Tässä tutkimuksessa järviruo’on märkäbiomassa vaihteli 4,61-14,3 (t/ha) välillä. Keskimääräinen järviruo’on märkäbiomassa koko tutkimusalueella lokakuussa 2020 oli 6,6 tonnia per hehtaari. Kassorin lahdella 4,61 tonnia per hehtaari ja Rapusvikenin lahdella 8,14 tonnia per hehtaari. Brattnäsvikenin lahdella vuonna 2022 märkäbiomassaa oli 14,3 tonnia per hehtaari. Tulevaisuudessa olisi tärkeää tutkia ruovikoiden biomassan vuodenaikaisuutta sekä biomassan muutosta. Myös muiden algoritmien ja tarkempien satelliitti -tai dronekuvien tarkastelu on tärkeää, jotta löydetään paras mahdollinen menetelmä ruovikoiden kaukokartoitukseen.Common reed (Phragmites australis) is 1-3-meter-tall halophyte, which belongs to the vascular plant family, which can grow in a wide variety of environments. Common reed grows in Finland, for example, on coasts, in shallow lakes and open water areas. Common reed has spread strongly during the last decades. Common reed grows in large and dense monocultures. When common reed spreads it takes up space from other plants. The spreading of reed causes overgrowth of waterways and reduces recreational use. When reeds rot, methane emissions are released, which causes strong odors. Researchers, experts, and reed entrepreneurs benefit from information on the distribution and biomass of common reed. Information about common reeds is needed for both commercial and environmental reasons. Sustainable utilization of common reed is a way to prevent the spreading of reed, as long as it is done at the correct time. The above-ground biomass (AGB) assessment of common reeds is influenced by the fact that although reeds often occur as monocultures, they are heterogeneous in their biomass. Mapping reed beds takes a lot of time, resources, and fieldwork. With the help of remote sensing methods, accurate up-to-date results can be obtained, which can be used in the assessment of the biomass of reed beds. In the study, it was found out how accurately the biomass of common reeds can be determined with the methods and satellite images chosen for the study in the entire study area of the Parainen town in Southwest Finland. Sentinel 2 satellite images from 02.10.2020 and 30.08.2022 were used as data in the thesis. In addition, common reeds were cut from three study areas for sample cuttings. Samples from Kassor and Rapusviken bays were cut on 28.10.2020 and from Brattnäsviken bay on 08.09.2022. In total, biomass data were obtained from 14 sample points. The selected Random Forest algorithm of supervised classification classified the common reed from the Sentinel 2 satellite images quite accurately. In this study, the producer's accuracy in both images was 85 percent and the user's accuracy were 75 and 58 percent, respectively. The linear regression model of several exponents is well suited for the assessment of the biomass of common reed biomass under the conditions of Southern Finland. The degree of explanation of the linear regression model for the 2020 image was 47 percent and 94.5 percent for the 2022 image. Biomass estimates were close to sample cuts. The success rate of the biomass estimation for the samples was 89 percent and the biomass estimations are in line with previous studies. In this study, the wet biomass of common reed varied between 4.61-14.3 (t/ha). The average wet biomass of common reed in the entire study area in October 2020 was 6.6 tons per hectare. In the bay of Kassor 4.61 tons per hectare and in the bay of Rapusviken 8.14 tons per hectare. In Brattnäsviken bay in 2022, 14.3 tons per hectare. In the future, it would be important to study the seasonality of reed biomass and the change in biomass. Examining other algorithms and more accurate satellite or drone images is also important in order to find the best possible method for remote mapping of reed beds

Mapping the Spatial-Temporal Dynamics of Vegetation Response Lag to Drought in a Semi-Arid Region

Drought, as an extreme climate event, affects the ecological environment for vegetation and agricultural production. Studies of the vegetative response to drought are paramount to providing scientific information for drought risk mitigation. In this paper, the spatial-temporal pattern of drought and the response lag of vegetation in Nebraska were analyzed from 2000 to 2015. Based on the long-term Daymet data set, the standard precipitation index (SPI) was computed to identify precipitation anomalies, and the Gaussian function was applied to obtain temperature anomalies. Vegetation anomaly was identified by dynamic time warping technique using a remote sensing Normalized Difference Vegetation Index (NDVI) time series. Finally, multilayer correlation analysis was applied to obtain the response lag of different vegetation types. The results show that Nebraska suffered severe drought events in 2002 and 2012. The response lag of vegetation to drought typically ranged from 30 to 45 days varying for different vegetation types and human activities (water use and management). Grasslands had the shortest response lag (~35 days), while forests had the longest lag period (~48 days). For specific crop types, the response lag of winter wheat varied among different regions of Nebraska (35–45 days), while soybeans, corn and alfalfa had similar response lag times of approximately 40 days

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

Fernerkundung der Vegetationsphänologie über MODIS NDVI Daten - Herausforderungen bei der Datenverarbeitung und -validierung mittels Bodenbeobachtungen zahlreicher Arten und LiDAR

Phenology, the cyclic events in living organisms is triggered by climatic conditions and indicators of climate change. They are important factors influencing species interactions and ecosystem functioning. This thesis deals with the estimation of phenological metrics (Land Surface Phenology or LSP) from MODIS based time series NDVI data. Results of data analysis emphasises the role of ground observations, topography and LiDAR characteristics of forest stand in describing the variability in LSP.Phänologie, die zyklischen Stadien von lebenden Organismen werden über klimatische Verhältnisse gesteuert und dienen als Indikatoren des Klimawandels. Diese Faktoren beeinflussen maßgeblich die Interaktionen zwischen Arten und sind für das Funktionieren von Ökosystemen ausschlaggebend. Diese Arbeit behandelt die Bestimmung von phänologischen Metriken (Phänologie der Landoberfläche oder LSP) unter Verwendung von MODIS basierten NDVI Zeitreihen. Die Ergebnisse der Datenanalyse hebt die Wichtigkeit von Bodenbeobachtungen, Topographie und LiDAR Merkmalen von Waldbeständen bei der Beschreibung der LSP Variabilität hervor

Salt Marsh Health and Biomass Responses to a Changing Environment

Coastal salt marshes are important ecosystems not only for their aesthetic beauty but also for their ecosystem services that they provide including improving water quality, providing protection from storm surges and hurricanes, and carbon sequestration. With climate change, including drought, warmer temperatures and sea-level rise, these systems are going to be impacted. Understanding how salt marshes will respond, or already have responded, to climate change will help us be better prepared for the future. By scripting a model to project how marshes may migrate with sea-level rise, I discover that salt marshes within Beaufort and Jasper counties, South Carolina will largely keep pace with sea-level rise. However, there are portions of the marsh area within these counties that will likely drown and development will impede areas of projected marsh migration. Additionally, I explored how above and belowground biomass changes with elevation above sea level, which are important relationships for modeling efforts. Using high-resolution satellite data, I mapped aboveground biomass across the entire marsh. Pairing this with elevation data, I created a growth curve of biomass versus elevation. The established growth curve is particularly useful as an input for biogeomorphic models of marsh development. Through computed tomography analysis, I analyzed belowground biomass. I found that belowground biomass is also a function of elevation, but there can be significant inter-site variability regardless of elevation. Looking at fall/spring variability, biomass abundance does not largely change, which indicates that belowground biomass is more likely longer lived. In the last part of this dissertation I looked at a past marsh dieback event to better understand drivers that lead to decline in marsh health. Using Landsat data, I created a map of change in salt marsh health by using differences in Normalized Difference Vegetation Indices. It is likely that the vegetation within higher elevations experienced stress due to hypersalinity, while vegetation within the lower marsh experienced stress from hypoxia leading to increased rates of vegetation decline in these zones. Overall this dissertation improves our understanding of drivers of marsh health and increases awareness of how salt marshes may respond under a changing climate