Temporal Stability of Seagrass Extent, Leaf Area, and Carbon Storage in St. Joseph Bay, Florida: A Semi-Automated Remote Sensing Analysis

Seagrasses are globally recognized for their contribution to blue carbon sequestration. However, accurate quantification of their carbon storage capacity remains uncertain due, in part, to an incomplete inventory of global seagrass extent and assessment of its temporal variability. Furthermore, seagrasses are undergoing significant decline globally, which highlights the urgent need to develop change detection techniques applicable to both the scale of loss and the spatial complexity of coastal environments. This study applied a deep learning algorithm to a 30-year time series of Landsat 5 through 8 imagery to quantify seagrass extent, leaf area index (LAI), and belowground organic carbon (BGC) in St. Joseph Bay, Florida, between 1990 and 2020. Consistent with previous field-based observations regarding stability of seagrass extent throughout St. Joseph Bay, there was no temporal trend in seagrass extent (23 ± 3 km2, τ = 0.09, p = 0.59, n = 31), LAI (1.6 ± 0.2, τ = -0.13, p = 0.42, n = 31), or BGC (165 ± 19 g C m−2, τ = - 0.01, p = 0.1, n = 31) over the 30-year study period. There were, however, six brief declines in seagrass extent between the years 2004 and 2019 following tropical cyclones, from which seagrasses recovered rapidly. Fine-scale interannual variability in seagrass extent, LAI, and BGC was unrelated to sea surface temperature or to climate variability associated with the El Niño-Southern Oscillation or the North Atlantic Oscillation. Although our temporal assessment showed that seagrass and its belowground carbon were stable in St. Joseph Bay from 1990 to 2020, forecasts suggest that environmental and climate pressures are ongoing, which highlights the importance of the method and time series presented here as a valuable tool to quantify decadal-scale variability in seagrass dynamics. Perhaps more importantly, our results can serve as a baseline against which we can monitor future change in seagrass communities and their blue carbon

Pengaruh Resolusi Spasial Citra terhadap Hasil Pemetaan Kandungan Hara Nitrogen Perkebunan Karet

Nitrogen merupakan salah satu unsur hara yang dibutuhkan dalam jumlah banyak oleh tanaman. Tanaman yang mengalami kekurangan unsur hara nitrogen akan menyebabkan terhambatnya pertumbuhan dan penurunan produktivitas tanaman. Penerapan sistem pertanian presisi pada kegiatan pemupukan di perkebunan karet dilakukan dengan cara dosis pemupukan dibuat berdasarkan kandungan hara tanah dan kandungan hara pada tanaman. Pada areal yang luas membutuhkan biaya analisa hara tanaman yang cukup mahal. Oleh karena itu sangat dibutuhkan suatu teknologi yang dapat mengestimasi kondisi hara tanaman dengan cepat dan biaya yang murah. Teknologi penginderaan jauh merupakan alternatif yang dapat digunakan untuk areal yang luas dan dengan waktu yang cepat serta biaya yang relatif murah. Penelitian ini bertujuan untuk mengetahui pengaruh resolusi spasial citra terhadap peta hasil estimasi kandungan nitrogen perkebunan karet. Citra multi resolusi yang digunakan antara lain GeoEye-1 (2 m) Sentinel-2A (10 dan 20 m) dan Landsat 8 OLI (30 m). Metode yang digunakan adalah membangun hubungan semi-empiris antara band tunggal dan indeks vegetasi citra dengan kandungan hara nitrogen perkebunan karet. Hasil penelitian menunjukkan bahwa peta hasil estimasi kandungan hara nitrogen perkebunan karet menggunakan citra Sentinel-2A (SE 0,369) memiliki akurasi yang lebih tinggi dibandingkan dengan menggunakan citra GeoEye-1 (SE 0,519) dan Landsat 8 OLI (SE 0,462)

Impact of Atmospheric Correction on Classification and Quantification of Seagrass Density from WorldView-2 Imagery

Mapping the seagrass distribution and density in the underwater landscape can improve global Blue Carbon estimates. However, atmospheric absorption and scattering introduce errors in space-based sensors’ retrieval of sea surface reflectance, affecting seagrass presence, density, and above-ground carbon (AGCseagrass) estimates. This study assessed atmospheric correction’s impact on mapping seagrass using WorldView-2 satellite imagery from Saint Joseph Bay, Saint George Sound, and Keaton Beach in Florida, USA. Coincident in situ measurements of water-leaving radiance (Lw), optical properties, and seagrass leaf area index (LAI) were collected. Seagrass classification and the retrieval of LAI were compared after empirical line height (ELH) and dark-object subtraction (DOS) methods were used for atmospheric correction. DOS left residual brightness in the blue and green bands but had minimal impact on the seagrass classification accuracy. However, the brighter reflectance values reduced LAI retrievals by up to 50% compared to ELH-corrected images and ground-based observations. This study offers a potential correction for LAI underestimation due to incomplete atmospheric correction, enhancing the retrieval of seagrass density and above-ground Blue Carbon from WorldView-2 imagery without in situ observations for accurate atmospheric interference correction

Performance Across Worldview-2 and RapidEye for Reproducible Seagrass Mapping

Satellite remote sensing offers an effective remedy to challenges in ground-based and aerial mapping that have previously impeded quantitative assessments of global seagrass extent. Commercial satellite platforms offer fine spatial resolution, an important consideration in patchy seagrass ecosystems. Currently, no consistent protocol exists for image processing of commercial data, limiting reproducibility and comparison across space and time. Additionally, the radiometric performance of commercial satellite sensors has not been assessed against the dark and variable targets characteristic of coastal waters. This study compared data products derived from two commercial satellites: DigitalGlobe\u27s WorldView-2 and Planet\u27s RapidEye. A single scene from each platform was obtained at St. Joseph Bay in Florida, USA, corresponding to a November 2010 field campaign. A reproducible processing regime was developed to transform imagery from basic products, as delivered from each company, into analysis-ready data usable for various scientific applications. Satellite-derived surface reflectances were compared against field measurements. WorldView-2 imagery exhibited high disagreement in the coastal blue and blue spectral bands, chronically overpredicting. RapidEye exhibited better agreement than WorldView-2, but overpredicted slightly across all spectral bands. A deep convolutional neural network was used to classify imagery into deep water, land, submerged sand, seagrass, and intertidal classes. Classification results were compared to seagrass maps derived from photointerpreted aerial imagery. This study offers the first radiometric assessment of WorldView-2 and RapidEye over a coastal system, revealing inherent calibration issues in shorter wavelengths of WorldView-2. Both platforms demonstrated as much as 97% agreement with aerial estimates, despite differing resolutions. Thus, calibration issues in WorldView-2 did not appear to interfere with classification accuracy, but could be problematic if estimating biomass. The image processing routine developed here offers a reproducible workflow for WorldView-2 and RapidEye imagery, which was tested in two additional coastal systems. This approach may become platform independent as more sensors become available

Remote sensing for developing an operational monitoring scheme for the Sundarban Reserved Forest, Bangladesh <engl.>

Sundarban Reserved Forest in Bangladesh is playing a significant role in local and national economy and is providing protection to the coastline as well as to the indigenous people. During the past decades and also in recent time this forest was heavily disturbed by human intervention in many aspects. As a consequence the resources of the forest are fragmenting, shrinking and declining, which in turn leads to an increasing failure of satisfying increasing demands both at local and national levels. Therefore accurate and continuously updated spatial information is needed for optimising forest management and environmental planning on both levels to support the fulfilment of urgent needs of sustainability of the forest. Considering the specific topography and the poor accessibility of the forest versus the task of collecting information, remote sensing is an attractive, if not the only means of obtaining sound full-coverage spatial information on forest cover of Sundarban. This research used medium resolution Landsat ETM data of November 2000 and Landsat TM data of January 1989 to assess and monitor the forest for 1. Identification of the operational tools for mapping and monitoring the forest as well as on the examination of the reliability of the application of multitemporal satellite remote sensing data for building spatial databases on forest cover in Sundarban. 2. Based on the existing management plan of the forest as well as the spectral properties of Landsat ETM imagery a level III classification system was developed. 3. This classification strategy was tested by applying several methods to achieve the classification result with the highest accuracy and thus to build the most reliable methodology for mapping forest cover in Sundarban. 4. Forest cover change was assessed for the period of eleven years. Significant changes have been observed due to illegal removal of trees from the forest although a governmental moratorium on banning timber extraction exists since 1989. 5. Development of an operational monitoring scheme by means of multitemporal satellite imagery analysis, which will allow concerned authorities to set up sustainable and appropriate monitoring of the Sundarban Reserved Forest.Das Schutzgebiet des Sundarban Mangrovenwaldes in Bangladesh spielt eine entscheidende Rolle in Hinsicht auf nationale und lokale sozio-ökonomische und sozio-ökologische Aspekte. Das Waldgebiet stabilisiert nicht nur die Küstenlinie, sondern schützt auch die Bevölkerung vor den Einflüssen von Flutkatastrophen. Durch menschlichen Einfluss wurde die Region während der letzten Jahrzehnte mehr und mehr unmittelbar gestört. Der Rückgang des Ertrags an Ressourcen aus dem Wald führte zu wachsender Unzufriedenheit in der von diesen Nutzungs-möglichkeiten abhängigen Bevölkerung. Um eine Optimierung des Waldmanagements durchführen zu können, werden kontinuierliche und genaue raumbezogene Daten benötigt. Betrachtet man die spezifische Topographie und die schlechte Zugänglichkeit der Waldgebiete, so bietet die Fernerkundung eine attraktive Möglichkeit, raumbezogene Informationen für die großen Flächen des Sundurban Mangrovenwaldes zu erfassen. Zur Analyse und Überwachung der Waldgebiete wurden zwei Satellitenbild-Datensätze mit mittlerer Auflösung verwendet, und zwar Landsat ETM Daten aus dem Jahre 2000 (November) sowie Landsat TM Daten aus dem Jahre 1989 (Januar). Die zentralen Aktivitäten im Rahmen der Bearbeitung der Dissertation beziehen sich auf 1. die Identifikation der notwendigen Werkzeuge für eine erfolgreiche Kartierung und Überwachung der Waldgebiete sowie Untersuchung der Zuverlässigkeit multi-temporaler Fernerkundungsdaten für den Aufbau einer Datenbasis für die Kartierung von Waldbedeckungsarten im Untersuchungsgebiet des Sunderban Mangroven-waldes, 2. die Entwicklung eines Klassifikationssystems nach dem USGS-Schlüssel (Auflösungsebene III) auf Grundlage des existierenden Managementplanes und der spektralen Qualität der Landsat ETM Satellitenbilddaten, 3. den Test der Klassifikationsstrategie durch Adaption unterschiedlicher Methoden und Optimierung in bezug auf Erzielung eines Ergebnisses in maximal erreichbarer Genauigkeit als Ausgangspunkt für den Aufbau einer Methodologie zum Monitoring des Sunderban Mangrovenwaldes, 4. die Extraktion der Veränderungen der Waldbedeckung über ein Zeitintervall von 11 Jahren mit weitreichenden Erkenntnissen zur Dynamik der Degradations-effekte, die hauptsächlich durch illegales Fällen trotz Verbot durch ein Regierungs-memorandum seit 1989 beschleunigt wird, 5. die Entwicklung einer operationellen Monitoring-Struktur mit Hilfe von multi-temporaler Satellitenbildanalyse für ein nachhaltiges und angepasstes raumbezo-genes Management des Sunderban-Mangrovenwaldes

Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass

This Special Issue (SI), entitled "Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass”, resulted from 13 peer-reviewed papers dedicated to Forestry and Biomass mapping, characterization and accounting. The papers' authors presented improvements in Remote Sensing processing techniques on satellite images, drone-acquired images and LiDAR images, both aerial and terrestrial. Regarding the images’ classification models, all authors presented supervised methods, such as Random Forest, complemented by GIS routines and biophysical variables measured on the field, which were properly georeferenced. The achieved results enable the statement that remote imagery could be successfully used as a data source for regression analysis and formulation and, in this way, used in forestry actions such as canopy structure analysis and mapping, or to estimate biomass. This collection of papers, presented in the form of a book, brings together 13 articles covering various forest issues and issues in forest biomass calculation, constituting an important work manual for those who use mixed GIS and RS techniques

Estimating Carbon Pool and Carbon Release due to Tropical Deforestation Using High-resolution Satellite Data: Carbon Release due to Tropical Deforestation

Forest-cover in the tropics is changing rapidly due to indiscriminate removal of timber from many localities. The main focus of the study is to develop an operational tool for monitoring biomass and carbon pool of tropical forest ecosystems. The method was applied to a test site of Bangladesh. The research used Landsat ETM+, Landsat TM and IRS pan images of 2001, 1992 and 1999 respectively. Geometrically corrected Landsat ETM+ imagery was obtained from USGS and adjusted to the field using GPS. Historical images were corrected using image-to-image registration. Atmospheric correction was done by modified dark object subtraction method. Stratified sampling design based on the remote sensing image was applied for assessing the above-ground biomass and carbon content of the study area. Field sampling was done during 2002-2003. Dbh and height of all the trees inside the sample plots were measured. Field measurement was finally converted to carbon content using allometric relations. Three different methods: stratification, regression and k-nearest neighbors were tested for combining remote sensing image information and field-based terrestrial carbon pool. Additional field sampling was conducted during 2003-2004 for testing the accuracy. Finally regression method was selected. The amount of carbon released and sequestrated from the ecosystem was estimated. The application of the developed method would be quite useful for understating the terrestrial carbon dynamics and global climate change

Derivation of forest inventory parameters from high-resolution satellite imagery for the Thunkel area, Northern Mongolia. A comparative study on various satellite sensors and data analysis techniques.

With the demise of the Soviet Union and the transition to a market economy starting in the 1990s, Mongolia has been experiencing dramatic changes resulting in social and economic disparities and an increasing strain on its natural resources. The situation is exacerbated by a changing climate, the erosion of forestry related administrative structures, and a lack of law enforcement activities. Mongolia’s forests have been afflicted with a dramatic increase in degradation due to human and natural impacts such as overexploitation and wildfire occurrences. In addition, forest management practices are far from being sustainable. In order to provide useful information on how to viably and effectively utilise the forest resources in the future, the gathering and analysis of forest related data is pivotal. Although a National Forest Inventory was conducted in 2016, very little reliable and scientifically substantiated information exists related to a regional or even local level. This lack of detailed information warranted a study performed in the Thunkel taiga area in 2017 in cooperation with the GIZ. In this context, we hypothesise that (i) tree species and composition can be identified utilising the aerial imagery, (ii) tree height can be extracted from the resulting canopy height model with accuracies commensurate with field survey measurements, and (iii) high-resolution satellite imagery is suitable for the extraction of tree species, the number of trees, and the upscaling of timber volume and basal area based on the spectral properties. The outcomes of this study illustrate quite clearly the potential of employing UAV imagery for tree height extraction (R2 of 0.9) as well as for species and crown diameter determination. However, in a few instances, the visual interpretation of the aerial photographs were determined to be superior to the computer-aided automatic extraction of forest attributes. In addition, imagery from various satellite sensors (e.g. Sentinel-2, RapidEye, WorldView-2) proved to be excellently suited for the delineation of burned areas and the assessment of tree vigour. Furthermore, recently developed sophisticated classifying approaches such as Support Vector Machines and Random Forest appear to be tailored for tree species discrimination (Overall Accuracy of 89%). Object-based classification approaches convey the impression to be highly suitable for very high-resolution imagery, however, at medium scale, pixel-based classifiers outperformed the former. It is also suggested that high radiometric resolution bears the potential to easily compensate for the lack of spatial detectability in the imagery. Quite surprising was the occurrence of dark taiga species in the riparian areas being beyond their natural habitat range. The presented results matrix and the interpretation key have been devised as a decision tool and/or a vademecum for practitioners. In consideration of future projects and to facilitate the improvement of the forest inventory database, the establishment of permanent sampling plots in the Mongolian taigas is strongly advised.2021-06-0