scholarly journals MODELLING THE AMOUNT OF CARBON STOCK USING REMOTE SENSING IN URBAN FOREST AND ITS RELATIONSHIP WITH LAND USE CHANGE

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 1051-1058
Author(s):  
N. Tavasoli ◽  
H. Arefi ◽  
S. Samiei-Esfahany ◽  
Q. Ronoud
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Carbon Stock ◽  
Urban Forest ◽  
Coefficient Of Determination ◽  
Series Data ◽  
Alos Palsar ◽  
Radar Images ◽  
Wide Range ◽  
Sentinel 2

Abstract. The estimation of biomass has been highly regarded for assessing carbon sources. In this paper, ALOS PALSAR, Sentinel-1, Sentinel-2 and ground data are used for estimating of above ground biomass (AGB) with SVM-genetic model Moreover Landsat satellite data was used to estimate land use change detection. The wide range of vegetation, textural and principal component analysis (PCA) indices (using optical images) and backscatter, decomposition and textural features (from radar images) are derived together with in situ collected AGB data into model to predict AGB. The results indicated that the coefficient of determination (R2) for ALOS PALSAR, Sentinel-1, Sentinel-2 were 0.51, 0.50 and 0.60 respectively. The best accuracy for combining all data was 0.83. Afterwards, the carbon stock map was calculated. Landsat series data were acquired to document the spatiotemporal dynamics of green spaces in the study area. By using a supervised classification algorithm, multi-temporal land use/cover data were extracted from a set of satellite images and the carbon stock time series simulated by using carbon stock maps and green space (urban forest) maps.

Mapping land use on Irish raised bogs using Sentinel-2 imagery and Google Earth Engine

2021 ◽  
Author(s):  
Wahaj Habib ◽  
John Connolly ◽  
Kevin McGuiness
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Land Use Change ◽  
Optical Sensors ◽  
Ghg Emissions ◽  
Google Earth ◽  
Good Prospect ◽  
Raised Bogs ◽  
Google Earth Engine ◽  
Sentinel 2

<p>Peatlands are one of the most space-efficient terrestrial carbon stores. They cover approximately 3 % of the terrestrial land surface and account for about one-third of the total soil organic carbon stock. Peatlands have been under severe strain for centuries all over the world due to management related activities. In Ireland, peatlands span over approximately 14600 km<sup>2</sup>, and 85 % of that has already been degraded to some extent. To achieve temperature goals agreed in the Paris agreement and fulfil the EU’s commitment to quantifying the Carbon/Green House Gases (C/GHG) emissions from land use, land use change forestry, accurate mapping and identification of management related activities (land use) on peatlands is important.</p><p>High-resolution multispectral satellite imagery by European Space Agency (ESA) i.e., Sentinel-2 provides a good prospect for mapping peatland land use in Ireland. However, due to persistent cloud cover over Ireland, and the inability of optical sensors to penetrate the clouds makes the acquisition of clear sky imagery a challenge and hence hampers the analysis of the landscape. Google Earth Engine (a cloud-based planetary-scale satellite image platform) was used to create a cloud-free image mosaic from sentinel-2 data was created for raised bogs in Ireland (images collected for the time period between 2017-2020). A preliminary analysis was conducted to identify peatland land use classes, i.e., grassland/pasture, crop/tillage, built-up, cutover, cutaway and coniferous, broadleaf forests using this mosaicked image. The land-use classification results may be used as a baseline dataset since currently, no high-resolution peatland land use dataset exists for Ireland. It can also be used for quantification of land-use change on peatlands. Moreover, since Ireland will now be voluntarily accounting the GHG emissions from managed wetlands (including bogs), this data could also be useful for such type of assessment.</p>

scholarly journals Ecosystem Carbon Stock Loss after Land Use Change in Subtropical Forests in China

Forests ◽  
2016 ◽  
Vol 7 (12) ◽  
pp. 142 ◽  
Author(s):  
Shaohui Fan ◽  
Fengying Guan ◽  
Xingliang Xu ◽  
David Forrester ◽  
Wu Ma ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Carbon Stock ◽  
Subtropical Forests ◽  
Ecosystem Carbon

scholarly journals Comparative analysis of hydrologic signatures in two agricultural watersheds in east-central Illinois: legacies of the past to inform the future

2013 ◽  
Vol 10 (5) ◽  
pp. 6515-6558 ◽  
Author(s):  
M. A. Yaeger ◽  
M. Sivapalan ◽  
G. F. McIsaac ◽  
X. Cai
Keyword(s):  
Land Use ◽  
Comparative Analysis ◽  
Land Use Change ◽  
Low Flow ◽  
Series Data ◽  
Small Scale ◽  
Agricultural System ◽  
Current Response ◽  
The Past ◽  
Central Illinois

Abstract. Historically, the central Midwestern US has undergone drastic anthropogenic land use change, having been transformed, in part through federal government policy, from a natural grassland system to an artificially-drained agricultural system devoted to row cropping corn and soybeans. Current federal policies are again influencing land use change in this region with increased corn acreage and new biomass crops proposed as part of an energy initiative emphasizing biofuels. To better address these present and future challenges it is helpful to understand how the legacies of past changes have shaped the current response of the system. To this end, a comparative analysis of the hydrologic signatures in both spatial and time series data from two central Illinois watersheds was undertaken. The past history of these catchments is reflected in their current hydrologic responses, which are highly heterogeneous, more so in the extensively tile-drained Sangamon watershed. The differences in geologic history, artificial drainage patterns, and to some extent, reservoir construction, manifest at all time scales, from annual to daily, and spatially within the watersheds. These differences can also be seen in the summer low flow patterns, where the more tile-drained watershed shows more variability than does the more naturally drained one. Of interest is the scaling behavior of the low flows; generally as drainage area increases, small-scale heterogeneity decreases. This is not seen in the more tile-drained watershed, thus adding complexity to the problem of predicting the catchment response to future changes.

Land-use change and climate change mitigation potential of agricultural soils in Finland

2020 ◽  
Author(s):  
Boris Tupek ◽  
Aleksi Lehtonen ◽  
Raisa Mäkipää ◽  
Pirjo Peltonen-Sainio ◽  
Saija Huuskonen ◽  
...  
Keyword(s):  
Land Use ◽  
Carbon Sequestration ◽  
Land Use Change ◽  
Soil Carbon ◽  
Carbon Balance ◽  
Carbon Stock ◽  
Total Carbon ◽  
Litter Input ◽  
Spruce Stands ◽  
Woody Litter

<p>We aimed to estimate a nation-wide potential to improve the carbon balance of the land use sector by removing part of the current croplands on mineral soil from food and feed production to extensive grasslands or afforestation in Finland.  We combined the existing data on forest and agricultural production, and climate with predictive capacity of YASSO07 soil carbon model to estimate changes of soil carbon stock (SOC) in Finland over the past land use change (LUC) from forest to agriculture in comparison with alternative LUC or continuous agriculture in future.</p><p>The model analysis revealed that SOC loss after deforestation during the cultivation period originated mainly from the absence of woody litter input. The non-woody litter input of the forest was comparable to that of the agricultural residues thus the SOC originating from non-woody litter has not changed much during cultivation. The model estimated approximately a 30 year delay in positive soil carbon balance after the afforestation. Longer for Norway spruce than for the Pubescent birch. The comparison of two dominant tree species used for afforestation highlighted a difference in soil versus biomass carbon sequestration. The total forest biomass production and total carbon stock was larger for spruce stands than for birch stands. However, due to larger foliar and fineroot litter input birch stands sequestered more carbon into the soil than spruce stands. The analysis further revealed that extensification of cropland to grassland would not meet 4 per mill soil carbon sequestration criterion needed for achieving Paris climate CO2 reduction target and due to the spatial limitation of afforestation other management measures need to be considered e.g. adding biochar to soils for successful and more permanent CO2 offsetting.</p>

scholarly journals THE PERFORMANCE OF LAND USE CHANGE CAUSATIVE FACTOR ON LANDSLIDE SUSCEPTIBILITY MAP IN UPPER UJUNG-LOE WATERSHEDS SOUTH SULAWESI, INDONESIA

2017 ◽  
Vol 4 (2) ◽  
pp. 157 ◽  
Author(s):  
Andang Suryana Soma ◽  
Tetsuya Kubota
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Landslide Susceptibility ◽  
Time Series Data ◽  
Google Earth ◽  
Series Data ◽  
Susceptibility Map ◽  
Landslide Susceptibility Map ◽  
Causative Factors ◽  
Predictive Rate

The study aims to develop and apply land use change (LUC) performance on landslide susceptibility map using frequency ratio (FR), and Logistic regression (LR) method in a geographic information system. In the study area, Upper Ujung-loe Watersheds area of Indonesia, landslides were detected using field survey and air photography from time series data image of Google Earth Pro from 2012 to 2016 and LUC from 2004 to 2011. Landslide susceptibility map (LSM) was constructed using FR and LR with nine causative factors. The result indicated that LUC affect the production of LSM. Validation of landslide susceptibility was carried out in this study at both with and without LUC causative factors. First, performances of each landslide model were tested using AUC curve for success and predictive rate. The highest value of predictive rate at with LUC in both FR and LR method were 83.4 % and 85.2 %, respectively. In the second stage, the ratio of landslides falling on high to a very high class of susceptibility was obtained, which indicates the level of accuracy of the method.LR method with LUC had the highest accuracy of 80.24 %. Taken together, the results suggested that changing the vegetation to another landscape causes slopes unstable and increases probability to landslide occurrence.

scholarly journals Use of Sentinel-2 and LUCAS Database for the Inventory of Land Use, Land Use Change, and Forestry in Wallonia, Belgium

Land ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 154 ◽  
Author(s):  
Odile Close ◽  
Beaumont Benjamin ◽  
Sophie Petit ◽  
Xavier Fripiat ◽  
Eric Hallot
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Land Cover ◽  
Greenhouse Gas ◽  
Nearest Neighbor ◽  
Training Sample ◽  
Training Data ◽  
K Nearest Neighbor ◽  
Statistical Survey ◽  
Sentinel 2

Due to its cost-effectiveness and repeatability of observations, high resolution optical satellite remote sensing has become a major technology for land use and land cover mapping. However, inventory compilers for the Land Use, Land Use Change, and Forestry (LULUCF) sector are still mostly relying on annual census and periodic surveys for such inventories. This study proposes a new approach based on per-pixel supervised classification using Sentinel-2 imagery from 2016 for mapping greenhouse gas emissions and removals associated with the LULUCF sector in Wallonia, Belgium. The Land Use/Cover Area frame statistical Survey (LUCAS) of 2015 was used as training data and reference data to validate the map produced. Then, we investigated the performance of four widely used classifiers (maximum likelihood, random forest, k-nearest neighbor, and minimum distance) on different training sample sizes. We also studied the use of the rich spectral information of Sentinel-2 data as well as single-date and multitemporal classification. Our study illustrates how open source data can be effectively used for land use and land cover classification. This classification, based on Sentinel-2 and LUCAS, offers new opportunities for LULUCF inventory of greenhouse gas on a European scale.

scholarly journals Biogeophysical and biogeochemical impacts of land-use change simulated by MIROC-ES2L

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Akihiko Ito ◽  
Tomohiro Hajima
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Land Surface ◽  
Carbon Stock ◽  
Climate Change Impacts ◽  
Global Scale ◽  
Model Complexity ◽  
Earth System ◽  
Land Use Impacts ◽  
Soil Carbon Stocks

Abstract Land-use change is one of the focal processes in Earth system models because it has strong impacts on terrestrial biogeophysical and biogeochemical conditions. However, modeling land-use impacts is still challenging because of model complexity and uncertainty. This study examined the results of simulations of land-use change impacts by the Model for Interdisciplinary Research on Climate, Earth System version 2 for long-term simulations (MIROC-ES2L) conducted under the Land-Use Model Intercomparison Project protocol. In a historical experiment, the model reproduced biogeophysical impacts such as decreasing trends in land-surface net radiation and evapotranspiration by about 1970. Among biogeochemical impacts, the model captured the global decrease of vegetation and soil carbon stocks caused by extensive deforestation. By releasing ecosystem carbon stock to the atmosphere, land-use change shortened the mean residence time of terrestrial carbon and accelerated its turnover rate, especially in low latitudes. Future projections based on Shared Socioeconomic Pathways indicated substantial alteration of land conditions caused primarily by climatic change and secondarily by land-use change. Sensitivity experiments conducted by exchanging land-use data between different future projection baseline experiments showed that, at the global scale, the anticipated extent of land-use conversion would likely play a modest role in the future terrestrial radiation, water, and carbon budgets. Regional investigations revealed that future land use would exert a considerable influence on runoff and vegetation carbon stock. Further model refinement is required to improve its capability to analyze its complicated terrestrial linkages or nexus (e.g., food, bioenergy, and carbon sequestration) to climate-change impacts.

LAND-USE CHANGE AND GREENHOUSE GAS EMISSIONS IN THE FAPRI-CARD MODEL SYSTEM: ADDRESSING BIAS AND UNCERTAINTY

2012 ◽  
Vol 03 (03) ◽  
pp. 1250014 ◽  
Author(s):  
AMANI E. ELOBEID ◽  
MIGUEL A. CARRIQUIRY ◽  
JACINTO F. FABIOSA
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Greenhouse Gas ◽  
Agricultural Sector ◽  
Ghg Emissions ◽  
Primary Concern ◽  
Wide Range ◽  
Viable Approach ◽  
The Many ◽  
The Impact

Even with a normalized and standardized biofuel shock, the wide range of land-use change estimates and their associated greenhouse gas (GHG) emissions have raised concern on the adequacy of existing agricultural models in this new area of analysis. In particular, reducing bias and improving precision of impact estimates are of primary concern to policy makers. This paper provides a detailed overview of the FAPRI-CARD agricultural modeling system, with particular emphasis on the modifications recently introduced to reduce bias in the results. We illustrate the impact of these new model features using the example of the new yield specification that now includes updated trend parameter, intensification and extensification effects, and a spatially disaggregated Brazil specification. The paper also provides a taxonomy of the many types of uncertainty surrounding any analysis, including parameter-coefficient uncertainty and exogenous variable uncertainty, identifying where specific types of uncertainty originate, and how they interact. Finally, FAPRI-CARD's long experience in using stochastic analysis is presented as a viable approach in addressing uncertainty in the analysis of changes in the agricultural sector, associated land-use change, and impacts on GHG emissions.

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