Quantifying the aboveground biomass stock changes associated with oil palm expansion on tropical peatlands using plot-based methods and L-band radar

The recent rapid expansion of oil palm (OP, Elaeis guineensis) plantations into tropical forest peatlands has resulted in net ecosystem carbon emissions. However, quantifications of the net carbon flux from biomass changes require accurate estimates of the above ground biomass (AGB) accumulation rate of OP on peat in working plantations. Current efforts that aim to reduce the emissions from OP expansion would also benefit from the development of economically viable remote sensing approaches that enable the detection of OP plantation expansion and monitoring of AGB stocks across at a fine spatial and temporal resolution. Here, destructive harvest and non-destructive plot inventories are conducted across a chronosequence of OP planting blocks (3 to 12 years after planting (YAP)) in plantations on drained peat in Sarawak, Malaysia. The effectiveness of using a timeseries of L-band synthetic aperture radar (SAR) scenes (ALOS PALSAR-1/2) and a novel ‘biomass matching’ approach to detect, quantify and map the AGB stock changes associated with OP establishment and growth was then assessed. Peat specific allometric equations for palm (9 palms, R2 = 0.92) and frond biomass are developed and upscaled to estimate AGB at the plantation block-level (902 palms). Aboveground biomass stocks on peat accumulated at ~6.39 ± 1.12 Mg ha-1 per year in the first 12 years after planting. However, high inter-palm and inter-block AGB variability was observed in mature classes as a result of variations in palm leaning and mortality. The ‘biomass matching’ approach detected statistically significant deforestation associated with OP establishment. OP growth was well estimated between 4 and 10 YAP, however sensitivity to increases in AGB was lost at ~ 45 - 60 Mg ha. Validation of the allometric equations defined and expansion of non-destructive inventories across alternative plantations and age classes on peat would further strengthen our understanding of OP AGB accumulation rates. With further investigation into the relationship between OP structural characteristics and L-band radar cross section (RCS) in the HV and HH polarisations, ‘biomass matching’ could be a feasible tool for monitoring AGB stock changes to inform carbon emission mitigation strategies

Using remote sensing and ecosystem accounting to assess changes in ecosystems, with an illustration for the Orinoco river basin

A further step in understanding the connections between ecosystems and the economy has been the development of ecosystem accounting. Ecosystem accounting assess changes on ecosystems and ecosystem services using cartographical and statistic information. However, such information is often non-existent or scarce, inaccessible and expensive. Remote sensing provides timely data over large coverages and can be a useful source of spatially explicit data at relatively low cost. This thesis shows the use of MODIS land surface products to support ecosystem accounting in the assessment of unsustainable changes in ecosystems. Examples of how the MODIS products can be used to populate the extent, condition and capacity accounts have been demonstrated in the chapters of this thesis. Moreover, examples of how ecosystem accounting can be combined with other multidisciplinary quantitative frameworks and on how ecosystem accounting can be applied in the assessment of human-managed ecosystems have been also provided. The potential use of the moderate resolution sensor VIIRS and the high-resolution sensors on board the Landsat 8 and Sentinel satellites as a source of spatially explicit information to populate accounts was recognized in the synthesis chapter. Moreover, the potential use of other MODIS products such as the atmosphere, cryosphere and ocean products to expand the assessment of other ecological areas such as the atmosphere and the sea were identified in the synthesis chapter.</p

GEO-SPATIAL MODELING OF CARBON SEQUESTRATION ASSESSMENT IN DATE PALM, ABU DHABI: AN INTEGRATED APPROACH OF FIELDWORK, REMOTE SENSING, AND GIS

The United Arab Emirates (UAE) has undertaken huge efforts to green the desert and afforestation projects (planted mainly with date palms) hence, reducing its carbon footprint, which have never been accounted for, because of lack of implemented mechanisms and tools to assess the amount of biomass and carbon stock (CS) sequestered by plants in the country. The purpose of this dissertation is to implement a new approach towards assessing the carbon sequestered by date palm (DP) plantations in Abu Dhabi, in both their biomass compartment as well as the soils under beneath, using geospatial technologies (RS and GIS) assessed by field measurements. The methodology proposed in this dissertation relied on both fieldwork and labwork, besides the intensive use of geospatial technology including, digital image processing of multi-scale, multi-resolution satellite imagery as well as Geographical Information Systems (GIS) modelling. For detecting and mapping the DP, the research proposes a framework based on using multi-source/ multi-sensor data in a hierarchical integrated approach (HIA) to map DP plantations at different age stages: young, medium, and mature. The outcomes of the implemented approach were the creation of detailed and accurate maps of DP at three age stages. The overall accuracies for mixed-ages DP the value reached up to 94.5%, with an overall Kappa statistic estimated at 0.888 with total area of DP equal to 7,588.04 ha and the total number of DP planted in the study area counted an estimated number of 8,966,826 palms.The study showed that the correlation of mature DP class alone (\u3e10 years) with single bands was significant with shorwave infrared 1 (SWIR1) and shortwave infrared 2 (SWIR2), while the correlation was significant with all tested vegetation indices (VI) except for tasseled cap transformation index for brightness (TCB) and for greenness (TCG). By using different types of regression equations, tasseled cap transformation index for wetness (TCW) showed the strongest correlation using a second-order polynomial equation to estimate the biomass of mature DP with R² equal to 0.7643 and P value equal to 0.007. The exponential regression equation that uses renormalized difference vegetation index (RDVI) as RS predictor was the best single VI and had the strongest correlation among all RS variables of Landsat 8 OLI for AGB of non-mature DP, with an R2 value of 0.4987 and P value equal 0.00002. The findings of the dissertation work are promising and can be used to estimate the amount of biomass and carbon stock in DP plantations in the country as well as in arid land in general. Therefore, it can be applied to enhance the decision-making process on sustainable monitoring and management of carbon sequestration by date palms in other similar ecosystems. The research’s approach has never been developed elsewhere for date palms in arid areas

Mapping the structure of Borneo's tropical forests across a degradation gradient

South East Asia has the highest rate of lowland forest loss of any tropical region, with logging and deforestation for conversion to plantation agriculture being flagged as the most urgent threats. Detecting and mapping logging impacts on forest structure is a primary conservation concern, as these impacts feed through to changes in biodiversity and ecosystem functions. Here, we test whether high-spatial resolution satellite remote sensing can be used to map the responses of aboveground live tree biomass (AGB), canopy leaf area index (LAI) and fractional vegetation cover (FCover) to selective logging and deforestation in Malaysian Borneo. We measured these attributes in permanent vegetation plots in rainforest and oil palm plantations across the degradation landscape of the Stability of Altered Forest Ecosystems project. We found significant mathematical relationships between field-measured structure and satellite-derived spectral and texture information, explaining up to 62% of variation in biophysical structure across forest and oil palm plots. These relationships held at different aggregation levels from plots to forest disturbance types and oil palms allowing us to map aboveground biomass and canopy structure across the degradation landscape. The maps reveal considerable spatial variation in the impacts of previous logging, a pattern that was less clear when considering field data alone. Up-scaled maps revealed a pronounced decline in aboveground live tree biomass with increasing disturbance, impacts which are also clearly visible in the field data even a decade after logging. Field data demonstrate a rapid recovery in forest canopy structure with the canopy recovering to pre-disturbance levels a decade after logging. Yet, up-scaled maps show that both LAI and FCover are still reduced in logged compared to primary forest stands and markedly lower in oil palm stands. While uncertainties remain, these maps can now be utilised to identify conservation win–wins, especially when combining them with ongoing biodiversity surveys and measurements of carbon sequestration, hydrological cycles and microclimate

Modelling Land Use Changes in the Republic of Congo 2000-2030 . A report by the REDD-PAC project.

This study is intended to assist institutions involved in REDD+ and the planning of National Strategies and Action plans for Biodiversity in the Republic of Congo by attempting to identify the areas under the greatest conversion pressures in the future and the consequences in terms of agricultural production, greenhouse gas emissions and biodiversity loss.Cette étude essaye d’identifier les zones soumises aux pressions de conversion les plus fortes dans le futur et les conséquences en termes de production agricole, d’émissions de gaz à effet de serre et de risque de perte de biodiversité. L’objectif du projet REDD-PAC est d’accompagner les institutions impliquées dans la REDD+ ainsi que dans la planification de la Stratégie Nationale et du Plan d’Action pour la Biodiversité en République du Congo

Mapping Species Composition of Forests and Tree Plantations in Northeastern Costa Rica with an Integration of Hyperspectral and Multitemporal Landsat Imagery

An efficient means to map tree plantations is needed to detect tropical land use change and evaluate reforestation projects. To analyze recent tree plantation expansion in northeastern Costa Rica, we examined the potential of combining moderate-resolution hyperspectral imagery (2005 HyMap mosaic) with multitemporal, multispectral data (Landsat) to accurately classify (1) general forest types and (2) tree plantations by species composition. Following a linear discriminant analysis to reduce data dimensionality, we compared four Random Forest classification models: hyperspectral data (HD) alone; HD plus interannual spectral metrics; HD plus a multitemporal forest regrowth classification; and all three models combined. The fourth, combined model achieved overall accuracy of 88.5%. Adding multitemporal data significantly improved classification accuracy (p less than 0.0001) of all forest types, although the effect on tree plantation accuracy was modest. The hyperspectral data alone classified six species of tree plantations with 75% to 93% producer's accuracy; adding multitemporal spectral data increased accuracy only for two species with dense canopies. Non-native tree species had higher classification accuracy overall and made up the majority of tree plantations in this landscape. Our results indicate that combining occasionally acquired hyperspectral data with widely available multitemporal satellite imagery enhances mapping and monitoring of reforestation in tropical landscapes

Land Change History of Oil Palm Plantations in Northern Bengkulu Province, Sumatra Island, Reconstructed from Landsat Satellite Archives

The aim of this study is to reconstruct the history of land conversion to oil palm plantation in tropical Asia using multi-temporal satellite data. A new method was constructed with a newly developed computer model, Land Change Detection and Land Definition Model (LC/LD Model) to map out spatio-temporal distribution of land changes. A comprehensive, cloud-free Landsat dataset was created from all the available Landsat data from 1988 to 2015. The pixel-based dataset was converted into a polygon-based dataset by applying the multi-temporal image segmentation method. The representation of the spectral information was also reduced to a single index of IB45, the ratio of the near-infrared (Band 4) to mid-infrared (Band 5) bands, which was the most suitable index for detecting and tracking the transformation of land to oil palm plantation. To extract targeted land changes and land uses from a given temporal profile, land change scenarios were assumed and temporal segmentation method was developed for Land Change Detection Model (LCM). The segmented profiles were then evaluated by using bio-physical metrics in the Land Definition Model (LDM) to determine the land uses. The two-tiered LC/LD Model could detect not only large-scale land changes caused by private companies but also small-scale changes caused by smallholders, which is supposedly the most uncertain factor for the future development of oil palm plantation. Relationships between local factors and two land change phenomena, conversion to oil palm plantation and deforestation, were investigated using quantitative assessments such as Logistic Regression analysis. The results explicitly showed the positive impacts of proximities to 1) pre-existing oil palm plantations and 2) nearest mill and negative impacts of 1) elevation and 2) slope on the occurrence of small oil palm plantations. These findings strongly imply that oil palm development in the neighborhood initiated further development in nearby areas. The accessibility to mills also increased the chance of oil palm development. From topographical aspects, flat and low altitude land was more favored than steep and high altitude one. The results also indicated that large size enterprise plantations were more responsible for directly converting untouched natural land than smallholders and were the main contributor to deforestation. In contrast, smallholders mainly converted preexisting farmland to oil palm plantations

Land cover change interacts with drought severity to change fire regimes in Western Amazonia

Fire is becoming a pervasive driver of environmental change in Amazonia and is expected to intensify, given projected reductions in precipitation and forest cover. Understanding of the influence of post-deforestation land cover change on fires in Amazonia is limited, even though fires in cleared lands constitute a threat for ecosystems, agriculture, and human health. We used MODIS satellite data to map burned areas annually between 2001 and 2010. We then combined these maps with land cover and climate information to understand the influence of land cover change in cleared lands and dry-season severity on fire occurrence and spread in a focus area in the Peruvian Amazon. Fire occurrence, quantified as the probability of burning of individual 232-m spatial resolution MODIS pixels, was modeled as a function of the area of land cover types within each pixel, drought severity, and distance to roads. Fire spread, quantified as the number of pixels burned in 3 × 3 pixel windows around each focal burned pixel, was modeled as a function of land cover configuration and area, dry-season severity, and distance to roads. We found that vegetation regrowth and oil palm expansion are significantly correlated with fire occurrence, but that the magnitude and sign of the correlation depend on drought severity, successional stage of regrowing vegetation, and oil palm age. Burning probability increased with the area of nondegraded pastures, fallow, and young oil palm and decreased with larger extents of degraded pastures, secondary forests, and adult oil palm plantations. Drought severity had the strongest influence on fire occurrence, overriding the effectiveness of secondary forests, but not of adult plantations, to reduce fire occurrence in severely dry years. Overall, irregular and scattered land cover patches reduced fire spread but irregular and dispersed fallows and secondary forests increased fire spread during dry years. Results underscore the importance of land cover management for reducing fire proliferation in this landscape. Incentives for promoting natural regeneration and perennial crops in cleared lands might help to reduce fire risk if those areas are protected against burning in early stages of development and during severely dry years