Improved Characterization of Forest Transmissivity Within the L-MEB Model Using Multisensor SAR Data

This letter proposes a novel way to assimilate synthetic aperture radar (SAR) data to L-band Microwave Emission of the Biosphere (L-MEB) model to enhance model performance over forested areas. L-and C-band satellite SAR data are used in order to characterize the forest transmissivity within the emission model, instead of the optical satellite imagery-based leaf area index (LAI) parameter. Examination of several combinations of satellite SAR data as a substitute for LAI within the L-MEB model showed that when ALOS PALSAR (L-band) and multitemporal composite Sentinel-1 (C-band) data are applied, an improved agreement was achieved between the measured and simulated brightness temperatures (TBs) over forests. The root mean squared difference between modeled and measured TBs was reduced from 6.1 to 4.7 K with single PALSAR scene-based transmissivity correction and down to 4.1 K with multitemporal Sentinel-1 composite-based transmissivity correction. Suitability of single Sentinel-1 scenes varied based on seasonal and weather conditions. Overall, this indicates the potential of an SAR-based estimation of forest volume transmissivity and opens a possible way of fruitful active-passive microwave satellite data integration.Peer reviewe

Proceedings of the Third Spaceborne Imaging Radar Symposium

This publication contains summaries of the papers presented at the Third Spaceborne Imaging Radar Symposium held at the Jet Propulsion Laboratory (JPL), California Institute of Technology, in Pasadena, California, on 18-21 Jan. 1993. The purpose of the symposium was to present an overview of recent developments in the different scientific and technological fields related to spaceborne imaging radars and to present future international plans. This symposium is the third in a series of 'Spaceborne Imaging Radar' symposia held at JPL. The first symposium was held in Jan. 1983 and the second in 1986

Estimating forest parameters from top of atmosphere multi-angular radiance data using coupled radiative transfer models

ABSTRACT -Traditionally, the estimation of forest parameters using physically-based canopy radiative transfer models (RT

Effect of biochar on microbial biomass and biological nitrogen fixation

Biochar is a product from the pyrolysis of plant derived-biomass and it is intended to be applied to soil given its potential of carbon sequestration and soil fertility improvement. Some studies also suggest that increasing application rate of biochar has a positive feedback on biological nitrogen fixation (BNF) and on soil microbial biomass. However, these effects are not well known for boreal forests. The purpose of this study was to evaluate the effects of different biochar application rates: 0 t ha-1, 5 t ha-1 and 10 t ha-1 on BNF, on microbial biomass carbon and nitrogen (MBC and MBN), and on moss biomass. The field experiment was established in Juupajoki, Southern Finland in young Scots pine stands. The stands were amended with biochar one year before the measurements took place. BNF was determined using acetylene reduction assay (ARA), and microbial biomass was estimated using chloroform fumigation-direct extraction (CFDE). The microbial biomass samples were incubated at the temperatures: 10 °C, 15 °C and 20 °C. Biochar amendment raised soil pH, whereas no differences were verified for BNF, MBC, MBN, nor for moss biomass. There was, however, variation in the response of N fixation to incubation temperature, and variation in the response of MBC and MBN to the time of measurement. Observed changes in pH are often likely to justify variations in the rates of BNF and MB, however in this study they were not shown to be of significance. It is possible, however that biochar will have a positive effect on soil vegetation as it is incorporated into the soil in the long-term. Although this study focuses on BNF and MB, the findings may well have a bearing on the use of biochar as a tool for C sequestration, since amendment with biochar was demonstrated as neither beneficial nor harmful to the soil biota

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

Earth resources: A continuing bibliography with indexes (issue 47)

This bibliography lists 524 reports, articles and other documents introduced into the NASA scientific and technical information system between July 1 and September 30, 1985. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economical analysis

Mapping and Monitoring Forest Cover

This book is a compilation of six papers that provide some valuable information about mapping and monitoring forest cover using remotely sensed imagery. Examples include mapping large areas of forest, evaluating forest change over time, combining remotely sensed imagery with ground inventory information, and mapping forest characteristics from very high spatial resolution data. Together, these results demonstrate effective techniques for effectively learning more about our very important forest resources