How Green Is Sugarcane Ethanol?

2021 ◽  
pp. 1-45
Author(s):  
Marcelo Sant'Anna
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Dynamic Model ◽  
Tropical Forests ◽  
Carbon Emissions ◽  
Agricultural Expansion ◽  
Green Is ◽  
Long Run ◽  
Remote Sensing Satellite ◽  
Area Expansion

Abstract Biofuels offer one approach for reducing carbon emissions. However, the necessary agricultural expansion may endanger tropical forests. I use a dynamic model of land use to disentangle the roles of acreage and yields in the supply of sugarcane ethanol in Brazil. The model is estimated using remote sensing (satellite) information of sugarcane activities. Estimates imply that, at the margin, 92% of new ethanol comes from increases in area and only 8% from increases in yield. Direct deforestation accounts for 19% of area expansion at the margin in the long-run. I further assess carbon emissions and deforestation implications from ethanol policies.

scholarly journals Securing tropical forest carbon: the contribution of protected areas to REDD

Oryx ◽  
2010 ◽  
Vol 44 (3) ◽  
pp. 352-357 ◽  
Author(s):  
Jörn P. W. Scharlemann ◽  
Valerie Kapos ◽  
Alison Campbell ◽  
Igor Lysenko ◽  
Neil D. Burgess ◽  
...  
Keyword(s):  
Land Use ◽  
Protected Areas ◽  
Tropical Forests ◽  
Carbon Emissions ◽  
Protected Area ◽  
Forest Cover ◽  
Forest Degradation ◽  
Protected Area Management ◽  
Forest Loss ◽  
The Tropics

AbstractForest loss and degradation in the tropics contribute 6–17% of all greenhouse gas emissions. Protected areas cover 217.2 million ha (19.6%) of the world’s humid tropical forests and contain c. 70.3 petagrams of carbon (Pg C) in biomass and soil to 1 m depth. Between 2000 and 2005, we estimate that 1.75 million ha of forest were lost from protected areas in humid tropical forests, causing the emission of 0.25–0.33 Pg C. Protected areas lost about half as much carbon as the same area of unprotected forest. We estimate that the reduction of these carbon emissions from ongoing deforestation in protected sites in humid tropical forests could be valued at USD 6,200–7,400 million depending on the land use after clearance. This is > 1.5 times the estimated spending on protected area management in these regions. Improving management of protected areas to retain forest cover better may be an important, although certainly not sufficient, component of an overall strategy for reducing emissions from deforestation and forest degradation (REDD).

scholarly journals A Visual Assessment Scale for Rapid Evaluation of Mangrove Degradation, Using Examples from Myanmar and Madagascar

2021 ◽  
Author(s):  
Christoph Zöckler ◽  
Dominic Wodehouse ◽  
Matthias Markolf
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Assessment Tool ◽  
Land Use Changes ◽  
Visual Assessment ◽  
Assessment Scale ◽  
Agricultural Expansion ◽  
Rapid Evaluation ◽  
Practical Guide ◽  
Mangrove Degradation

Mangroves are globally threatened, disappearing and degraded. They are lost due to land use changes, mostly agricultural expansion and aquaculture, but also degraded by cutting by villagers and logging and timber extraction for domestic and economic purposes. Extent and conversion of mangroves can usually be estimated by applying remote sensing and modern drone technology, but the scale of degradation of mangrove habitats is not easily detected by such methods. In this paper we propose an assessment tool for a rapid evaluation on the degradation, using examples from different regions in Myanmar and Madagascar. We propose a visual and practical guide listing a range of 1–6 to identify and quantify the level of degradation. We demonstrate the application by displaying various examples from Myanmar and Madagascar and how this tool can be used for wider applications, discussing advantages scope, and limitations.

scholarly journals Persistent collapse of biomass in Amazonian forest edges following deforestation leads to unaccounted carbon losses

2020 ◽  
Vol 6 (40) ◽  
pp. eaaz8360 ◽  
Author(s):  
Celso H. L. Silva Junior ◽  
Luiz E. O. C. Aragão ◽  
Liana O. Anderson ◽  
Marisa G. Fonseca ◽  
Yosio E. Shimabukuro ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Tropical Forests ◽  
Forest Fragmentation ◽  
Edge Effect ◽  
Carbon Emissions ◽  
Forest Edges ◽  
Amazonian Forest ◽  
Primary Driver ◽  
The Tropics ◽  
Carbon Losses

Deforestation is the primary driver of carbon losses in tropical forests, but it does not operate alone. Forest fragmentation, a resulting feature of the deforestation process, promotes indirect carbon losses induced by edge effect. This process is not implicitly considered by policies for reducing carbon emissions in the tropics. Here, we used a remote sensing approach to estimate carbon losses driven by edge effect in Amazonia over the 2001 to 2015 period. We found that carbon losses associated with edge effect (947 Tg C) corresponded to one-third of losses from deforestation (2592 Tg C). Despite a notable negative trend of 7 Tg C year−1 in carbon losses from deforestation, the carbon losses from edge effect remained unchanged, with an average of 63 ± 8 Tg C year−1. Carbon losses caused by edge effect is thus an additional unquantified flux that can counteract carbon emissions avoided by reducing deforestation, compromising the Paris Agreement’s bold targets.

scholarly journals Approaches of Satellite Remote Sensing for the Assessment of Above-Ground Biomass across Tropical Forests: Pan-tropical to National Scales

2020 ◽  
Vol 12 (20) ◽  
pp. 3351
Author(s):  
Sawaid Abbas ◽  
Man Sing Wong ◽  
Jin Wu ◽  
Naeem Shahzad ◽  
Syed Muhammad Irteza
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Carbon Sequestration ◽  
Tropical Forest ◽  
Tropical Forests ◽  
Satellite Remote Sensing ◽  
Forest Cover ◽  
Above Ground Biomass ◽  
Forest Cover Change ◽  
Ground Biomass

Tropical forests are acknowledged for providing important ecosystem services and are renowned as “the lungs of the planet Earth” due to their role in the exchange of gasses—particularly inhaling CO2 and breathing out O2—within the atmosphere. Overall, the forests provide 50% of the total plant biomass of the Earth, which accounts for 450–650 PgC globally. Understanding and accurate estimates of tropical forest biomass stocks are imperative in ascertaining the contribution of the tropical forests in global carbon dynamics. This article provides a review of remote-sensing-based approaches for the assessment of above-ground biomass (AGB) across the tropical forests (global to national scales), summarizes the current estimate of pan-tropical AGB, and discusses major advancements in remote-sensing-based approaches for AGB mapping. The review is based on the journal papers, books and internet resources during the 1980s to 2020. Over the past 10 years, a myriad of research has been carried out to develop methods of estimating AGB by integrating different remote sensing datasets at varying spatial scales. Relationships of biomass with canopy height and other structural attributes have developed a new paradigm of pan-tropical or global AGB estimation from space-borne satellite remote sensing. Uncertainties in mapping tropical forest cover and/or forest cover change are related to spatial resolution; definition adapted for ‘forest’ classification; the frequency of available images; cloud covers; time steps used to map forest cover change and post-deforestation land cover land use (LCLU)-type mapping. The integration of products derived from recent Synthetic Aperture Radar (SAR) and Light Detection and Ranging (LiDAR) satellite missions with conventional optical satellite images has strong potential to overcome most of these uncertainties for recent or future biomass estimates. However, it will remain a challenging task to map reference biomass stock in the 1980s and 1990s and consequently to accurately quantify the loss or gain in forest cover over the periods. Aside from these limitations, the estimation of biomass and carbon balance can be enhanced by taking account of post-deforestation forest recovery and LCLU type; land-use history; diversity of forest being recovered; variations in physical attributes of plants (e.g., tree height; diameter; and canopy spread); environmental constraints; abundance and mortalities of trees; and the age of secondary forests. New methods should consider peak carbon sink time while developing carbon sequestration models for intact or old-growth tropical forests as well as the carbon sequestration capacity of recovering forest with varying levels of floristic diversity.

scholarly journals Global rules for translating land-use change (LUH2) to land-cover change for CMIP6 using GLM2

2020 ◽  
Vol 13 (7) ◽  
pp. 3203-3220 ◽  
Author(s):  
Lei Ma ◽  
George C. Hurtt ◽  
Louise P. Chini ◽  
Ritvik Sahajpal ◽  
Julia Pongratz ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Use Change ◽  
Land Cover ◽  
Land Cover Change ◽  
Carbon Emissions ◽  
Forest Cover ◽  
Land Use Changes ◽  
Change Models ◽  
Translation Rule

Abstract. Anthropogenic land-use and land-cover change activities play a critical role in Earth system dynamics through significant alterations to biogeophysical and biogeochemical properties at local to global scales. To quantify the magnitude of these impacts, climate models need consistent land-cover change time series at a global scale, based on land-use information from observations or dedicated land-use change models. However, a specific land-use change cannot be unambiguously mapped to a specific land-cover change. Here, nine translation rules are evaluated based on assumptions about the way land-use change could potentially impact land cover. Utilizing the Global Land-use Model 2 (GLM2), the model underlying the latest Land-Use Harmonization dataset (LUH2), the land-cover dynamics resulting from land-use change were simulated based on multiple alternative translation rules from 850 to 2015 globally. For each rule, the resulting forest cover, carbon density and carbon emissions were compared with independent estimates from remote sensing observations, U.N. Food and Agricultural Organization reports, and other studies. The translation rule previously suggested by the authors of the HYDE 3.2 dataset, that underlies LUH2, is consistent with the results of our examinations at global, country and grid scales. This rule recommends that for CMIP6 simulations, models should (1) completely clear vegetation in land-use changes from primary and secondary land (including both forested and non-forested) to cropland, urban land and managed pasture; (2) completely clear vegetation in land-use changes from primary forest and/or secondary forest to rangeland; (3) keep vegetation in land-use changes from primary non-forest and/or secondary non-forest to rangeland. Our analysis shows that this rule is one of three (out of nine) rules that produce comparable estimates of forest cover, vegetation carbon and emissions to independent estimates and also mitigate the anomalously high carbon emissions from land-use change observed in previous studies in the 1950s. According to the three translation rules, contemporary global forest area is estimated to be 37.42×106 km2, within the range derived from remote sensing products. Likewise, the estimated carbon stock is in close agreement with reference biomass datasets, particularly over regions with more than 50 % forest cover.

scholarly journals Utilization of Remote Sensing Technology for Carbon Offset Identification in Malaysian Forests

2021 ◽  
Author(s):  
Hamdan Omar ◽  
Thirupathi Rao Narayanamoorthy ◽  
Norsheilla Mohd Johan Chuah ◽  
Nur Atikah Abu Bakar ◽  
Muhamad Afizzul Misman
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Tropical Forests ◽  
Optical Sensors ◽  
Landsat Imagery ◽  
Remote Sensing Data ◽  
Tropical Country ◽  
Sensing Technology ◽  
Carbon Offset ◽  
Carbon Dioxide Co2

Rapid growth of Malaysia’s economy recently is often associated with various environmental disturbances, which have been contributing to depletion of forest resources and thus climate change. The need for more spaces for numerous land developments has made the existing forests suffer from deforestation. This chapter presents an overview and demonstrates how remote sensing data is used to map and quantify changes of tropical forests in Malaysia. The analysis dealt with image processing that produce seamless mosaics of optical satellite data over Malaysia, within 15 years period, with 5-year intervals. The challenges were about the production of cloud-free images over a tropical country that always covered by clouds. These datasets were used to identify eligible areas for carbon offset in land use, land use change and forestry (LULUCF) sector in Malaysia. Altogether 580 scenes of Landsat imagery were processed to complete the observation period and came out with a seamless, wall to wall images over Malaysia from year 2005 to 2020. Forests have been identified from the image classification and then classified into three major types, which are dry-inland forest, peat swamp and mangroves. Post-classification change detection technique was used to determine areas that have been undergoing conversions from forests to other land uses. Forest areas were found to have declined from about 19.3 Mil. ha (in 2005) to 18.2 Mil. ha in year 2020. Causes of deforestation have been identified and the amount of carbon dioxide (CO2) that has been emitted due to the deforestation activity has been determined in this study. The total deforested area between years 2005 and 2020 was at 1,087,030 ha with rate of deforestation of about 72,469 ha yr.−1 (or 0.37% yr.−1). This has contributed to the total CO2 emission of 689.26 Mil. Mg CO2, with an annual rate of 45.95 Mil. Mg CO2 yr.−1. The study found that the use of a series satellite images from optical sensors are the most appropriate sensors to be used for monitoring of deforestation over the Malaysia region, although cloud covers are the major issue for optical imagery datasets.

scholarly journals Building Land Cover Objects Following the IPCC Guidelines for Carbon Emission Estimation. Case Study in the Central Highlands of Vietnam

2021 ◽  
Vol 6 (1) ◽  
pp. p45
Author(s):  
Le Q. Hung ◽  
Vu T. P. Thao
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Greenhouse Gas ◽  
Carbon Emissions ◽  
Input Data ◽  
Carbon Emission ◽  
Greenhouse Gas Inventory ◽  
Emission Estimation

We estimated the carbon emissions in the field of Land Use, Land Use Change, and Forestry (LULUCF) by using advanced technology to build the input data. Remote sensing, including satellite remote sensing and Unmanned Aerial Vehicles (UAV) with transparency, multi-time, and wide coverage characteristics, is useful in this area. The article focuses on the proposed regulations and building process of subjects in the land cover of the Vietnamese mainland following the guidance of the Intergovernmental Panel on Climate Change as applied to carbon emission estimation. We propose a process to estimate carbon emissions using the Agriculture and Land Use Greenhouse Gas Inventory software with input data extracted from the remote sensing images. An experiment on land cover change was carried out over ten years between 2006 and 2016 in the Central Highlands of Vietnam. The results obtained with remote sensing data classification for land cover categories achieved a reliability of 69% for the year 2006 and 66% for the year 2016. The carbon emission estimation data were checked and used in Vietnam’s biennial update report to the United Nations Framework Convention on Climate Change, including content and updated information on the greenhouse gas inventory.

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