1,890 research outputs found
Remote sensing environmental change in southern African savannahs : a case study of Namibia
Savannah biomes cover a fifth of Earth’s surface, harbour many of the world’s most iconic
species and most of its livestock and rangeland, while sustaining the livelihoods of an
important proportion of its human population. They provide essential ecosystem services and
functions, ranging from forest, grazing and water resources, to global climate regulation and
carbon sequestration. However, savannahs are highly sensitive to human activities and climate
change. Across sub-Saharan Africa, climatic shifts, destructive wars and increasing
anthropogenic disturbances in the form of agricultural intensification and urbanization, have
resulted in widespread land degradation and loss of ecosystem services. Yet, these threatened
ecosystems are some of the least studied or protected, and hence should be given high
conservation priority. Importantly, the scale of land degradation has not been fully explored,
thereby comprising an important knowledge gap in our understanding of ecosystem services
and processes, and effectively impeding conservation and management of these biodiversity
hotspots.
The primary drivers of land degradation include deforestation, triggered by the increasing
need for urban and arable land, and concurrently, shrub encroachment, a process in which the
herbaceous layer, a defining characteristic of savannahs, is replaced with hardy shrubs. These
processes have significant repercussions on ecosystem service provision, both locally and
globally, although the extents, drivers and impacts of either remain poorly quantified and
understood. Additionally, regional aridification anticipated under climate change, will lead to
important shifts in vegetation composition, amplified warming and reduced carbon
sequestration. Together with a growing human population, these processes are expected to
compound the risk of land degradation, thus further impacting key ecosystem services.
Namibia is undergoing significant environmental and socio-economic changes. The most
pervasive change processes affecting its savannahs are deforestation, degradation and shrub
encroachment. Yet, the extent and drivers of such change processes are not comprehensively
quantified, nor are the implications for rural livelihoods, sustainable land management, the
carbon cycle, climate and conservation fully explored. This is partly due to the complexities
of mapping vegetation changes with satellite data in savannahs. They are naturally spatially
and temporally variable owing to erratic rainfall, divergent plant functional type phenologies
and extensive anthropogenic impacts such as fire and grazing. Accordingly, this thesis aims to
(i) quantify distinct vegetation change processes across Namibia, and (ii) develop
methodologies to overcome limitations inherent in savannah mapping. Multi-sensor satellite
data spanning a range of spatial, temporal and spectral resolutions are integrated with field
datasets to achieve these aims, which are addressed in four journal articles.
Chapters 1 and 2 are introductory. Chapter 3 exploits the Landsat archive to track changes in
land cover classes over five decades throughout the Namibian Kalahari woodlands. The
approach addresses issues implicit in change detection of savannahs by capturing the distinct
phenological phases of woody vegetation and integrating multi-sensor, multi-source data.
Vegetation extent was found to have decreased due to urbanization and small-scale arable
farming. An assessment of the limitations leads to Chapter 4, which elaborates on the
previous chapter by quantifying aboveground biomass changes associated with deforestation
and shrub encroachment. The approach centres on fusing multiple satellite datasets, each
acting as a proxy for distinct vegetation properties, with calibration/validation data consisting
of concurrent field and LiDAR measurements. Biomass losses predominate, demonstrating
the contribution of land management to ecosystem carbon changes.
To identify whether biomass is declining across the country, Chapter 5 focuses on regional,
moderate spatial resolution time-series analyses. Phenological metrics extracted from MODIS
data are used to model observed fractional woody vegetation cover, a proxy for biomass.
Trends in modelled fractional woody cover are then evaluated in relation to the predominant
land-uses and precipitation. Negative trends slightly outweighed positive trends, with
decreases arising largely in protected, urban and communal areas. Since precipitation is a
fundamental control on vegetation, Chapter 6 investigates its relation to NDVI, by assessing
to what extent observed trends in vegetation cover are driven by rainfall. NDVI is modelled as
a function of precipitation, with residuals assumed to describe the fraction of NDVI not
explained by rainfall. Mean annual rainfall and rainfall amplitude show a positive trend,
although extensive “greening” is unrelated to rainfall. NDVI amplitude, used as a proxy for
vegetation density, indicates a widespread shift to a denser condition.
In Chapter 7, trend analysis is applied to a Landsat time-series to overcome spatial and
temporal limitations characteristic of the previous approaches. Results, together with those of
the previous chapters, are synthesized and a synopsis of the main findings is presented.
Vegetation loss is predominantly caused by demand for urban and arable land. Greening
trends are attributed to shrub encroachment and to a lesser extent conservation laws, agroforestry
and rangeland management, with precipitation presenting little influence. Despite
prevalent greening, degradation processes associated with shrub encroachment, including soil
erosion, are likely to be widespread. Deforestation occurs locally while shrub encroachment
occurs regionally. This thesis successfully integrates multi-source data to map, measure and
monitor distinct change processes across scales
Climate, land use and vegetation trends: Implication of land use change and climate change on northwestern drylands of Ethiopia
Land use / land cover (LULC) change assessment is getting more consideration by global environmental change studies as land use change is exposing dryland environments for transitions and higher rates of resource depletion. The semiarid regions of northwestern Ethiopia are not different as land use transition is the major problem of the region. However, there is no satisfactory study to quantify the change process of the region up to now. Hence, spatiotemporal change analysis is vital for understanding and identification of major threats and solicit solutions for sustainable management of the ecosystem. LULC change studies focus on understanding the patterns, processes and dynamics of land use transitions and driving forces of change. The change processes in dryland ecosystems can be either seasonal, gradual or abrupt changes of random or systematic change processes that result in a pattern or permanent transition in land use. Identification of these processes of change and their type supports adoption of monitoring options and indicate possible measures to be taken to safeguard this dynamic ecosystem.
This study examines the spatiotemporal patterns of LULC change, temporal trends in climate variables and the insights of the communities on change patterns of ecosystems. Landsat imagery, MODIS NDVI, CRU temperature, TAMSAT rainfall and socio-ecological field data were used in order to identify change processes. LULC transformation was monitored using support vector machine (SVM) algorithm. A cross-tabulation matrix assessment was implemented in order to assess the total change of land use categories based on net change and swap change. In addition, the pattern of change was identified based on expected gain and loss under a random process of gain and loss, respectively. Breaks For Additive Seasonal and Trend (BFAST) analysis was employed for determining the time, direction and magnitude of seasonal, abrupt and trend changes within the time series datasets. In addition, Man Kendall test statistic and Sen’s slope estimator were used for assessing long term trends on detrended time series data components. Distributed lag (DL) model was also adopted in order to determine the time lag response of vegetation to the current and past rainfall distribution.
Over the study period of 1972- 2014, there is a significant change in LULC as evidenced by a significant increase in size of cropland of about 53% and a net loss of over 61% of woodland area. The period 2000-2014 has shown a sharp increase of cropland and a sharp decline of woodland areas. Proximate causes include agricultural expansion and excessive wood harvesting; and underlying causes of demographic factor, economic factors and policy contributed the most to an overuse of existing natural resources. In both the observed and expected proportion of random process of change and of systematic changes, woodland has shown the highest loss compared to other land use types. The observed transition and expected transition under random process of gain of woodland to cropland is 1.7%, implies that cropland systematically gains to replace woodland. The comparison of the difference between observed and expected loss under random process of loss also showed that when woodland loses cropland systematically replaces it. The assessment of magnitude and time of breakpoints on climate data and NDVI showed different results. Accordingly, NDVI analysis demonstrated the existence of breakpoints that are statistically significant on the seasonal and long term trends. There is a positive trend, but no breakpoints on the long term precipitation data during the study period. The maximum temperature also showed a positive trend with two breakpoints which are not statistically significant. On the other hand, there is no seasonal and trend breakpoints in minimum temperature, though there is an overall positive trend along the study period.
The Man-Kendall test statistic for long term average Tmin and Tmax showed significant variation where as there is no significant trend within the long term rainfall distribution. The lag regression between NDVI and precipitation indicated a lag of up to forty days. This proves that the vegetation growth in this area is not primarily determined by the current precipitation rather with the previous forty days rainfall. The combined analysis showed declining vegetation productivity and a loss of vegetation cover that contributed for an easy movement of dust clouds during the dry period of the year. This affects the land condition of the region, resulting in long term degradation of the environmen
Enhancing Landsat time series through multi-sensor fusion and integration of meteorological data
Over 50 years ago, the United States Interior Secretary, Stewart Udall, directed space agencies to gather "facts about the natural resources of the earth." Today global climate change and human modification make earth observations from all variety of sensors essential to understand and adapt to environmental change. The Landsat program has been an invaluable source for understanding the history of the land surface, with consistent observations from the Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) sensors since 1982. This dissertation develops and explores methods for enhancing the TM/ETM+ record by fusing other data sources, specifically, Landsat 8 for future continuity, radar data for tropical forest monitoring, and meteorological data for semi-arid vegetation dynamics.
Landsat 8 data may be incorporated into existing time series of Landsat 4-7 data for applications like change detection, but vegetation trend analysis requires calibration, especially when using the near-infrared band. The improvements in radiometric quality and cloud masking provided by Landsat 8 data reduce noise compared to previous sensors.
Tropical forests are notoriously difficult to monitor with Landsat alone because of clouds. This dissertation developed and compared two approaches for fusing Synthetic Aperture Radar (SAR) data from the Advanced Land Observation Satellite (ALOS-1) with Landsat in Peru, and found that radar data increased accuracy of deforestation. Simulations indicate that the benefit of using radar data increased with higher cloud cover.
Time series analysis of vegetation indices from Landsat in semi-arid environments is complicated by the response of vegetation to high variability in timing and amount of precipitation. We found that quantifying dynamics in precipitation and drought index data improved land cover change detection performance compared to more traditional harmonic modeling for grasslands and shrublands in California.
This dissertation enhances the value of Landsat data by combining it with other data sources, including other optical sensors, SAR data, and meteorological data. The methods developed here show the potential for data fusion and are especially important in light of recent and upcoming missions, like Sentinel-1, Sentinel-2, and NASA-ISRO Synthetic Aperture Radar (NISAR)
An assessment of tropical dryland forest ecosystem biomass and climate change impacts in the Kavango-Zambezi (KAZA) region of Southern Africa
The dryland forests of the Kavango-Zambezi (KAZA) region in Southern Africa are highly susceptible to disturbances from an increase in human population, wildlife pressures and the impacts of climate change. In this environment, reliable forest extent and structure estimates are difficult to obtain because of the size and remoteness of KAZA (519,912 km²). Whilst satellite remote sensing is generally well-suited to monitoring forest characteristics, there remain large uncertainties about its application for assessing changes at a regional scale to quantify forest structure and biomass in dry forest environments. This thesis presents research that combines Synthetic Aperture Radar, multispectral satellite imagery and climatological data with an inventory from a ground survey of woodland in Botswana and Namibia in 2019. The research utilised a multi-method approach including parametric and non-parametric algorithms and change detection models to address the following objectives: (1) To assess the feasibility of using openly accessible remote sensing data to estimate the dryland forest above ground biomass (2) to quantify the detail of vegetation dynamics using extensive archives of time series satellite data; (3) to investigate the relationship between fire, soil moisture, and drought on dryland vegetation as a means of characterising spatiotemporal changes in aridity. The results establish that a combination of radar and multispectral imagery produced the best fit to the ground observations for estimating forest above ground biomass. Modelling of the time-series shows that it is possible to identify abrupt changes, longer-term trends and seasonality in forest dynamics. The time series analysis of fire shows that about 75% of the study area burned at least once within the 17-year monitoring period, with the national parks more frequently affected than other protected areas. The results presented show a significant increase in dryness over the past 2 decades, with arid and semi-arid regions encroaching at the expense of dry sub-humid, particularly in the south of the region, notably between 2011-2019
Monitoring Drought Impact on Annual Forage Production in Semi-Arid Grasslands: A Case Study of Nebraska Sandhills
Land management practices and disturbances (e.g. overgrazing, fire) have substantial effects on grassland forage production. When using satellite remote sensing to monitor climate impacts, such as drought stress on annual forage production, minimizing land management practices and disturbance effects sends a clear climate signal to the productivity data. This study investigates the effect of this climate signal by: (1) providing spatial estimates of expected biomass under specific climate conditions, (2) determining which drought indices explain the majority of interannual variability in this biomass, and (3) developing a predictive model that estimates the annual biomass early in the growing season. To address objective 1, this study uses an established methodology to determine Expected Ecosystem Performance (EEP) in the Nebraska Sandhills, US, representing annual forage levels after accounting for non-climatic influences. Moderate Resolution Imaging Spectroradiometer (MODIS)-based Normalized Dierence Vegetation Index (NDVI) data were used to approximate actual ecosystem performance. Seventeen years (2000–2016) of annual EEP was calculated using piecewise regression tree models of site potential and climate data. Expected biomass (EB), EEP converted to biomass in kg*ha-1*yr-1, was then used to examine the predictive capacity of several drought indices and the onset date of the growing season. Subsets of these indices were used to monitor and predict annual expected grassland biomass. Independent field-based biomass production data available from two Sandhills locations were used for validation of the EEP model. The EB was related to field-based biomass production (R2 = 0.66 and 0.57) and regional rangeland productivity statistics of the Soil Survey Geographic Database (SSURGO) dataset. The Evaporative Stress Index (ESI), the 3- and 6-month Standardized Precipitation Index (SPI), and the U.S. Drought Monitor (USDM), which represented moisture conditions during May, June and July, explained the majority of the interannual biomass variability in this grassland system (three-month ESI explained roughly 72% of the interannual biomass variability). A new model was developed to use drought indices from early in the growing season to predict the total EB for the whole growing season. This unique approach considers only climate-related drought signal on productivity. The capability to estimate annual EB by the end of May will potentially enable land managers to make informed decisions about stocking rates, hay purchase needs, and other management issues early in the season, minimizing their potential drought losses
Climatic, environmental and socio-economic factors for malaria transmission modelling in KwaZulu-Natal, South Africa.
Doctoral Degree. University of KwaZulu-Natal, Durban.Sub-Saharan Africa (SSA) largely bears the burden of the global malaria disease, with the transmission and intensity influenced by the interaction of a variety of climatic, environmental, socio-economic, and human factors. Other factors include parasitic and vectoral factors. In South Africa (SA) in general and KwaZulu-Natal (KZN) in particular, the change of the malaria control intervention policy in 2000, may be responsible for the significant progress over the past two decades in reducing malaria case report to near zero. Currently, malaria incidence in KZN is less than 1 case per 1000 persons at risk placing the province in the malaria elimination stage. To meeting the elimination target, it is necessary to study the dynamics of malaria transmission in KZN employing various analytical/statistical models. Thus, the aim of this study was to explore the factors that influence malaria transmission by employing different analytical models and approaches in a setting with low malaria endemicity and transmission. This involves a sound appraisal of the existing literature on the contribution of remote sensing technology in understanding malaria transmission, evaluation of existing malaria control intervention; delineation of empirical map of malaria risk; provide information on the climatic, environmental and socio-economic factors that influences malaria risk and transmission; and formulation of a relevant malaria forecast and surveillance models. The investigator started with a systemic review of studies in chapter two. The studies were aimed at identifying significant remotely-sensed climatic and environmental determinants of malaria transmission for modelling malaria transmission and risk in SSA via a variety of statistical approaches. Normalised difference vegetation index (NDVI) was identified as the most significant remotely-sensed climatic/environmental determinants of malaria transmission in SSA. Majority of the studies employed the generalised linear modelling approach compared to the Bayesian modelling approach. In the third chapter, malaria cases from the endemic areas of KZN with remotely-sensed climatic and environmental data were used to model the climatic and environmental determinants of malaria transmission and develop a malaria risk map in KZN. The spatiotemporal zero inflated Poisson model formulated indicates that at 95% Bayesian credible interval (BCI) NDVI (0.91; 95% BCI = 0.71, -1.12), precipitation (0.11; 95% BCI = 0.08, 0.14), elevation (0.05; 95% BCI = 0.032, 0.07) and night temperature (0.04; 95% BCI = 0.03, 0.04) are significantly related to malaria transmission in KZN, SA. The area with the highest risk of malaria morbidity in KZN was identified as the north-eastern part of the province. The fourth chapter was to establish the socio-economic status (SES) that influence malaria transmission in the endemic areas of KZN, by employing a Bayesian inference approach. The obtained posterior samples revealed that, significant association existed between malaria disease and low SES such as illiteracy, unemployment, no toilet facilities and no electricity at 95% BCI Lack of toilet facilities (odds ration (OR) =12.54; 95% BCI = 0.63, 24.38) exhibited the strongest association with malaria and highest risk of malaria disease. This was followed by no education (OR =11.83; 95% BCI = 0.54, 24.27) and lack of electricity supply (OR =10.56; 95% BCI = 0.43, 23.92) respectively. In the fifth chapter, the seasonal autoregressive integrated moving average (SARIMA) intervention time series analysis (ITSA) was employed to model the effect of the malaria control intervention, dichlorodiphenyltrichloroethane (DDT) on confirmed monthly malaria cases. The result is an abrupt and permanent decline of monthly malaria cases (w0= −1174.781, p-value = 0.003) following the implementation of the intervention policy. Finally, the sixth chapter employed a SARIMA modelling approach to predict malaria cases in the endemic areas of KZN. Three plausible models were identified, and based on the goodness of fit statistics and parameter estimation, the SARIMA (0,1,1) (0,1,1)12 model was identified as the best fit model. The SARIMA (0,1,1)(0,1,1)12 model was used to forecast malaria cases during 2014, and it was observed to fit closely with the reported malaria cases during January to December 2014. The models generated in this study demonstrated the need for the KZN malaria program, relevant policy makers and stakeholders to further strengthen the KZN malaria elimination efforts. The required malaria elimination fortification are not limited to the implementation of additional sustainable developmental approach that combines both improved malaria intervention resources and socio-economic conditions, strengthening of existing community health workers, and strengthening of the already existing cross-border collaborations. However, more studies in the area of statistical modelling as well as practical applications of the generated models are encouraged. These can be accomplished by exploring new avenues via cross-sectional survey to understand the impact of community and social related structures in malaria burden; strengthening of existing community health workers; knowledge, attitude and practices in malaria control and intervention; and the likely effects of temporal/seasonal and spatial variations of malaria incidence in neighbouring endemic countries should be explored
Water transport among the world ocean basins within the water cycle
The global water cycle involves water-mass transport on land, in the atmosphere, in the ocean, and among them. Quantification of such transport, especially its time evolution, is essential to identify the footprints of climate change, and it also helps to constrain and improve climatic models. In the ocean, net water-mass transport among the ocean basins is a key process, but it is currently a poorly estimated parameter. We propose a new methodology that incorporates the time-variable gravity observations from the Gravity Recovery and Climate Experiment (GRACE) satellite (2003–2016) to estimate the change in water content; this new approach also overcomes some fundamental limitations of existing methods. We show that the Pacific and Arctic oceans receive an average of 1916 (95 % confidence interval of [1812, 2021]) Gt per month (∼0.72±0.02 Sv) of excess freshwater from the atmosphere and the continents that is discharged into the Atlantic and Indian oceans, where net evaporation minus precipitation returns the water to complete the cycle. This is in contrast to previous GRACE-based studies, where the notion of a see-saw mass exchange between the Pacific and the Atlantic and Indian oceans has been reported. Seasonal climatology as well as the interannual variability of water-mass transport are also reported.This research has been supported by the Spanish Ministry of Science, Innovation and Universities (grant no. RTI2018-093874-B-100)
Satellite-Based Monitoring of Primary Production in a Mediterranean Islet Post Black Rat Eradication
Invasive rodents have a detrimental impact on terrestrial ecosystem functioning, this is often exacerbated on small islands. Rat eradication campaigns are often used to deal with this environmental perturbation given their classification as invasive species. Studies assessing the effects of rodent control at ecosystem scale are scarce and thus little is known about the subsequent functional response of vegetation subsequent to rat control. In this work, we use remote sensing to assess the effects of black rat (Rattus rattus) eradication on Mediterranean vegetation productivity in the Sa Dragonera Islet, Mallorca (Spain). Rats feed on seeds, sprouts, and leaves of woody vegetation and hence we expect primary production to increase nine years after the rodenticide campaign. The Break Detection approach for additive season and trend (BFAST method) was adopted to examine changes in vegetation density before and after the eradication campaign in Sa Dragonera Islet (Balearic Islands), using a temporal series of monthly NDVI data extracted from Landsat imagery. The same temporal trends were examined for a control zone where no rat eradication took place, in order to control for weather-driven changes. The results of this study revealed changes across the 21-year monthly NDVI time series. However, the dates, magnitude, and trend of these changes could not be explicitly attributed to the action of rats, when compared to the historical changes on the islet and the changes found to co-occur within the control zone. These finding could, perhaps, be explained by the high resilience of Mediterranean shrubs to browsing including that of rat invasion. However, the results from the study appear to show that rat damage on specific plant species, with little contribution to global NDVI values, would be overshadowed by the effects of broader environmental factors in this remote sensing approach. The results suggest that the current passive restoration scheme imposed following eradication is not sufficient for effective ecosystem restoratio
Trends in vegetation productivity and seasonality for Namaqualand, South Africa between 1986 and 2011: an approach combining remote sensing and repeat photography
This thesis presents an assessment of vegetation change and its drivers across a subset of Namaqualand, South Africa. Namaqualand forms part of the Succulent Karoo biome, which is characterised by exceptionally high species biodiversity but which has undergone severe transformation since the arrival of pastoral colonists. Vegetation productivity in Namaqualand is of great importance since there is a high dependence on natural resources, livestock and agriculture for both subsistence and income. However, there is considerable debate on the relative contribution of land-use change and climate change to vegetation change and land degradation in Namaqualand. Early studies based on bioclimatic envelop models suggest that an increase in temperature and more arid conditions could result in the vegetation cover of the Succulent Karoo being significantly reduced. On the other hand, more recent studies show that less extreme changes in rainfall could result in the vegetation of the biome remaining fairly stable with possible increases in the spatial extent by 2050. Furthermore, field observations and repeat photography, suggest that the change in vegetation in the region over the course of the 20th century generally portrays an increase in cover largely as a result of changes in land-use. By combining repeat photography and satellite data from NOAA-AVHRR and TERRA-MODIS sensors as well as baseline climatology data from the CRU TS 3.2 data set this study aimed to: (1) Determine the critical pathways of inter-annual and intra-seasonal vegetation change in the Namaqualand; (2) Investigate the role of land-use and climate variability as key drivers of vegetation change in Namaqualand
Modelling peatland water table depth using remotely sensed satellite data
Peatlands are carbon-rich wetland ecosystems and represent the largest terrestrial carbon store.
Although they are natural carbon sinks, damage, drainage and extraction over past decades have turned
peatlands into a global carbon source. To tackle this nearly irreversible loss, peatland conservation and
restoration projects on global and national levels have been increasing in numbers. High water table
depth (WTD) is a highly important factor that influences peatland condition, resilience and ability to
accumulate carbon. Given the extent of peatlands, a regular physical collection of data in situ, looking
forward, would be impractical and difficult to accomplish, and the development of a remote sensing
methods for peatland WTD monitoring would be highly beneficial.
The accessibility to satellite data along with advancements in sensors, both in variety - optical,
microwave, thermal, and their resolutions - spatial, spectral, and temporal, has greatly increased in the
last decade. Combined with advances in image processing using cloud computing and machine learning,
it has made it easier to access and process remotely sensed data. Synthetic aperture radar (SAR), with
its ability to provide data regardless of the weather, has emerged as an important source of data for
environmental applications.
This project aimed to advance the usage of remotely sensed SAR data to predict peatland water
table depth. First, a unique high resolution laboratory study was completed confirming SAR backscatter
sensitivity to changes in peatland soil moisture and water table depth. This was followed by a case study
for the Forsinard Flows area, where Sentinel-1 SAR data were used to build and test three models of
different complexity for WTD prediction. The random forest model was found to be the most suited
with an overall good temporal fit, highest correlation scores and lowest RMSE values. The model was
later tested on a wider Peatland ACTION dataset, reaching an even higher score, affirming its
applicability to peatlands in various conditions (near natural, degraded and undergoing restoration). In
the final section of the thesis, up to twenty year-long time series of remote sensing data were analysed
to investigate trends and change points in peatland restoration areas. The trends found using lower
resolution satellite data from MODIS gave mixed results and would only be indicative of very abrupt
changes, such as tree felling. The trends from the modelled WTD series based on Sentinel-1 data were
indicative of positive trajectories towards higher WTD, following restoration.
The results from this thesis suggest that remotely sensed data can be informative about changes
in the WTD and overall peatland condition, can be used to look at seasonal change, and can be indicative
of restoration progress and response to droughts. Recent studies have shown a close link between
greenhouse gasses and peatland WTD, therefore, if methods of predicting WTD based on remotely
sensed data are developed further, they ultimately could be used as a proxy for greenhouse gas emission
reporting
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