Forest disturbance and recovery: A general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure

Abrupt forest disturbances generating gaps \u3e0.001 km2 impact roughly 0.4–0.7 million km2a−1. Fire, windstorms, logging, and shifting cultivation are dominant disturbances; minor contributors are land conversion, flooding, landslides, and avalanches. All can have substantial impacts on canopy biomass and structure. Quantifying disturbance location, extent, severity, and the fate of disturbed biomass will improve carbon budget estimates and lead to better initialization, parameterization, and/or testing of forest carbon cycle models. Spaceborne remote sensing maps large-scale forest disturbance occurrence, location, and extent, particularly with moderate- and fine-scale resolution passive optical/near-infrared (NIR) instruments. High-resolution remote sensing (e.g., ∼1 m passive optical/NIR, or small footprint lidar) can map crown geometry and gaps, but has rarely been systematically applied to study small-scale disturbance and natural mortality gap dynamics over large regions. Reducing uncertainty in disturbance and recovery impacts on global forest carbon balance requires quantification of (1) predisturbance forest biomass; (2) disturbance impact on standing biomass and its fate; and (3) rate of biomass accumulation during recovery. Active remote sensing data (e.g., lidar, radar) are more directly indicative of canopy biomass and many structural properties than passive instrument data; a new generation of instruments designed to generate global coverage/sampling of canopy biomass and structure can improve our ability to quantify the carbon balance of Earth\u27s forests. Generating a high-quality quantitative assessment of disturbance impacts on canopy biomass and structure with spaceborne remote sensing requires comprehensive, well designed, and well coordinated field programs collecting high-quality ground-based data and linkages to dynamical models that can use this information

Spatio-temporal and structural analysis of vegetation dynamics of Lowveld Savanna in South Africa

Savanna vegetation structure parameters are important for assessing the biomes status under various disturbance scenarios. Despite free availability remote sensing data, the use of optical remote sensing data for savanna vegetation structure mapping is limited by sparse and heterogeneous distribution of vegetation canopy. Cloud and aerosol contamination lead to inconsistency in the availability of time series data necessary for continuous vegetation monitoring, especially in the tropics. Long- and medium wavelength microwave data such as synthetic aperture radar (SAR), with their low sensitivity to clouds and atmospheric aerosols, and high temporal and spatial resolution solves these problems. Studies utilising remote sensing data for vegetation monitoring on the other hand, lack quality reference data. This study explores the potential of high-resolution TLS-derived vegetation structure variables as reference to multi-temporal SAR datasets in savanna vegetation monitoring. The overall objectives of this study are: (i) to evaluate the potential of high-resolution TLS-data in extraction of savanna vegetation structure variables; (ii) to estimate landscape-wide aboveground biomass (AGB) and assess changes over four years using multi-temporal L-band SAR within a Lowveld savanna in Kruger National Park; and (iii) to assess interactions between C-band SAR with various savanna vegetation structure variables. Field inventories and TLS campaign were carried out in the wet and dry seasons of 2015 respectively, and provided reference data upon which AGB, CC and cover classes were modelled. L-band SAR modelled AGB was used for change analysis over 4 years, while multitemporal C-band SAR data was used to assess backscatter response to seasonal changes in CC and AGB abundant classes and cover classes. From the AGB change analysis, on average 36 ha of the study area (91 ha) experienced a loss in AGB above 5 t/ha over 4 years. A high backscatter intensity is observed on high abundance AGB, CC classes and large trees as opposed to low CC and AGB abundance classes and small trees. There is high response to all structure variables, with C-band VV showing best polarization in savanna vegetation mapping. Moisture availability in the wet season increases backscatter response from both canopy and background classes

Ecological impacts of deforestation and forest degradation in the peat swamp forests of northwestern Borneo

Tropical peatlands have some of the highest carbon densities of any ecosystem and are under enormous development pressure. This dissertation aimed to provide better estimates of the scales and trends of ecological impacts from tropical peatland deforestation and degradation across more than 7,000 hectares of both intact and disturbed peatlands in northwestern Borneo. We combined direct field sampling and airborne Light Detection And Ranging (LiDAR) data to empirically quantify forest structures and aboveground live biomass across a largely intact tropical peat dome. The observed biomass density of 217.7 ± 28.3 Mg C hectare-1 was very high, exceeding many other tropical rainforests. The canopy trees were ~65m in height, comprising 81% of the aboveground biomass. Stem density was observed to increase across the 4m elevational gradient from the dome margin to interior with decreasing stem height, crown area and crown roughness. We also developed and implemented a multi-temporal, Landsat resolution change detection algorithm for identify disturbance events and assessing forest trends in aseasonal tropical peatlands. The final map product achieved more than 92% user’s and producer’s accuracy, revealing that after more than 25 years of management and disturbances, only 40% of the area was intact forest. Using a chronosequence approach, with a space for time substitution, we then examined the temporal dynamics of peatlands and their recovery from disturbance. We observed widespread arrested succession in previously logged peatlands consistent with hydrological limits on regeneration and degraded peat quality following canopy removal. We showed that clear-cutting, selective logging and drainage could lead to different modes of regeneration and found that statistics of the Enhanced Vegetation Index and LiDAR height metrics could serve as indicators of harvesting intensity, impacts, and regeneration stage. Long-term, continuous monitoring of the hydrology and ecology of peatland can provide key insights regarding best management practices, restoration, and conservation priorities for this unique and rapidly disappearing ecosystem

Estimación de biomasa en bosques nativos usando datos de satélite y radiometría espectral

The management of forests as carbon (C) reservoirs could be a valid strategy for mitigating global climate change. In Salta, Argentina, there is an urgent need for updated information on biomass stocks in order to assess the C sequestering and release made by native forests. We studied three ecosystems (Chaco, Yungas and shrubland) by combining different data: a) field-estimated above-ground biomass (AGB); b) field-spectral data, and c) spectral data from remote sensing. AGB was estimated through allometric equations. Radiometric measurements were synthesized into a set of spectral vegetation indices (VI). The satellite data was calibrated with those obtained through field radiometry, allowing us to find a predictive AGB model which indicates an AGB average of 85 ± 250 t.ha-1 for the center of the province of Salta. The model which was finally selected increases the level of estimate detail made at the national level and will allow the monitoring of such data.El manejo de bosques como reservorios de carbono (C) puede ser una estrategia válida para mitigar el cambio climático global. En Salta, Argentina, hay una urgente necesidad de información actualizada sobre el stock de biomasa para evaluar el secuestro y la liberación de C hecha por esos bosques nativos. Estudiamos tres ecosistemas (Chaco, Yungas y arbustales), combinando diferentes datos: a) biomasa (AGB) estimada por mediciones de campo; b) radiometría de campo y datos c) espectrales de sensores remotos. La AGB fue estimada por ecuaciones alométricas. Los registros radiométricos fueron sintetizados en índices de vegetación (VI) y los datos de satélite fueron calibrados con aquellos obtenidos por radiometría de campo. Construimos un modelo predictivo de AGB que indica un promedio de 85 ± 250 t.ha-1 para el centro de la provincia de Salta. El modelo finalmente seleccionado aumenta el nivel de detalle de las estimaciones realizadas a nivel nacional y permitirá el seguimiento de estos datos.Fil: Manrique, Silvina Magdalena. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Salta. Instituto de Investigaciones en Energía no Convencional; Argentina. Universidad Nacional de Salta. Facultad de Cs.naturales. Instituto de Recursos Naturales y Ecodesarrollo; ArgentinaFil: Nuñez, Virgilio. Universidad Nacional de Salta. Facultad de Cs.naturales. Instituto de Recursos Naturales y Ecodesarrollo; ArgentinaFil: Franco, Ada Judith. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Salta. Instituto de Investigaciones en Energía no Convencional; Argentina. Universidad Nacional de Salta; Argentin

Contribution of water-limited ecoregions to their own supply of rainfall

The occurrence of wet and dry growing seasons in water-limited regions remains poorly understood, partly due to the complex role that these regions play in the genesis of their own rainfall. This limits the predictability of global carbon and water budgets, and hinders the regional management of naturalresources. Using novel satellite observations and atmospheric trajectory modelling, we unravel the origin and immediate drivers of growing-season precipitation, and the extent to which ecoregions themselves contribute to their own supply of rainfall. Results show that persistent anomalies in growing-season precipitation—and subsequent biomass anomalies—are caused by a complex interplay of land and ocean evaporation, air circulation and local atmospheric stability changes. For regions such as the Kalahari and Australia, the volumes of moisture recycling decline in dry years, providing a positive feedback that intensifies dry conditions. However, recycling ratios increase up to40%, pointing to the crucial role of these regions in generating their own supply of rainfall; transpiration in periods of water stress allows vegetation to partly offset the decrease in regional precipitation. Findings highlight the need to adequately represent vegetation–atmosphere feedbacks in models to predict biomass changes and to simulate the fate of water-limited regions in our warming climate

Spectral radiometric technique for carbon estimation in Omo Forest Reserve, South Western, Nigeria

Field-estimated above-ground biomass (AGB) and spectral data from remote sensing were collected from randomly selected 50 sample plots. AGB  was estimated through the biomass density equation. Radiometric measurements were carried out using a set of spectral vegetation indices. The  remote sensing data was calibrated with those obtained from the field using GPS points. The average model-based estimation using satellite image canopy cover was 30.71 t/plot, while the multispectral data was 69.07 t/plot in the biosphere. This gave a difference of 1.44 t/plot and 36.91 t/plot  respectively from the calculated carbon 32.16 t/plot. The canopy cover based estimation deviated from the ground measurement with 1.44 t/plot, while the estimation based on vegetation indices was twice that of field measurement. The result indicated that calibrated field measurements with forest canopy cover from high resolution image was the most reliable remote sensing technique in estimating AGB in a natural forest as compared  to vegetation index. The model selected for a single tree forest based on modified soil adjusted vegetation index with value of 61.18 t/plot compared to the calculated value of 49.84 t/plot may to some extent improve AGB estimation. Keywords: Carbon sink, Biosphere, Above-ground biomass, Vegetation index and Remote sensin