High accuracy Land Use Land Cover (LULC) maps for detecting agricultural drought effects in rainfed agro-ecosystems in central Mexico

Sierra-Soler, Andres; Adamowski, Jan; Qi, Zhiming; Saadat, Hossein; Pingale, Santosh Murlidhar

doi:10.1515/jwld-2015-0014

Cited by 11 publications

(7 citation statements)

References 60 publications

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“…The application of the zonal statistics tool in ArcGIS is used in various studies in classifying and relating land surface in land use change (Youneszadeh et al 2015;Rahmana 2016;Sierra-Soler et al 2015). Recently in various studies, for instance, Feyissa et al (2018) and Woodruff et al (2017) used the application of the zonal statistics tool in ArcGIS to assess community vulnerability to climate variability and extreme events.…”

Section: Zonal Statisticsmentioning

confidence: 99%

Rural households’ livelihood vulnerability to climate variability and extremes: a livelihood zone-based approach in the Northeastern Highlands of Ethiopia

Mekonen

Berlie²

2021

Ecol Process

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Background The adverse effects of climate variability and extremes exert increasing pressure on rural farm households whose livelihoods are dependent on nature. However, integrated and area-specific vulnerability assessments in Ethiopia in general and the study area, in particular, are scarce and insufficient for policy implications. Therefore, this study aims to quantify, map, classify, and prioritize the level of vulnerability in terms of the components of exposure, sensitivity, and adaptive capacity in the Northeastern Highlands of Ethiopia. The study area is divided into six livelihood zones, namely, Abay-Beshilo Basin (ABB), South Wollo and Oromia eastern lowland sorghum and cattle (SWS), Chefa Valley (CHV), Meher-Belg, Belg, and Meher. A total of 361 sample households were selected using proportional probability sampling techniques. Survey questionnaire, key informant interview, and focus group discussions were used to collect the necessary data. Rainfall and temperature data were also used. Following the IPCC’s climate change vulnerability assessment approach, the climate vulnerability index (CVI) framework of Sullivan and Meigh’s model was used to assess the relative vulnerability of livelihoods of rural households. Twenty-four vulnerability indicators were identified for exposure, sensitivity, and adaptive capacity components. In this regard, Iyengar and Sudarshan’s unequal weighting system was applied to assign a weight to indicators. Results The results revealed that Belg and Meher were found to be the highest exposure livelihood zones to vulnerability with an aggregated value of 0.71. Equally, SWS, ABB, Belg, and CHV livelihood zones showed moderate level of sensitivity to vulnerability with an aggregated value between 0.45 and 0.60. The study noted that livelihood zone of Belg (0.75) was found to be at high level of livelihood vulnerability. ABB (0.57) and CHV (0.45) were at a moderate level of livelihood vulnerability while Meher-Belg (0.22) was the least vulnerable livelihood zone due to a high level of adaptive capacity such as infrastructure, asset accumulation, and social networks. Conclusion It was identified that disparities of livelihood vulnerability levels of rural households were detected across the study livelihood zones due to differences in the interaction of exposure, sensitivity, and adaptive capacity components. The highest levels of exposure and sensitivity combined with low level of adaptive capacity have increased households’ livelihood vulnerability. More importantly, the biophysical and socioeconomic sensitivity to livelihood vulnerability were exacerbated by slope/topography, soil erodibility, and population pressure. Therefore, designing livelihood zone-based identifiable adaptation strategies are essential to reduce the exposure and sensitivity of crop-livestock mixed agricultural systems to climate calamity.

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Section: Zonal Statisticsmentioning

confidence: 99%

Rural households’ livelihood vulnerability to climate variability and extremes: a livelihood zone-based approach in the Northeastern Highlands of Ethiopia

Mekonen

Berlie²

2021

Ecol Process

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“…The average of the values in each zone is assigned to all output cells in the same zone. Zonal statistics tool is used in classifying and relating land surface in land use change in various studies (Youneszadeh et al 2015;Rahmana 2016;Sierra-Soler et al 2015). Based on the calculations, the land surface temperature changes with time and the elevation, their statistics and spatial correlations has been utilized.…”

Section: Zonal Statisticsmentioning

confidence: 99%

GIS based quantification and mapping of climate change vulnerability hotspots in Addis Ababa

Feyissa

Zeleke

Gebremariam

et al. 2018

Geoenviron Disasters

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Background: Unlike the studies undertaken on agricultural and hydrological sectors, focused climate change vulnerability researches in urban centers in Ethiopia is not widely available and of recent history. However, as many signals of climate change vulnerability started to happen in urban centers as well, it is inevitable to analyze, quantify, map, prioritize and be prepared for adaptation measures. This study is therefore, tried to assess, quantify and map climate change vulnerability in Addis Ababa, Ethiopia, by integrating two climate change vulnerability assessment models, namely, the Sullivan and Meigh's Model of composite climate change vulnerability index and the IPCC's approach of vulnerability assessment which comprises exposure, sensitivity and adaptive capacity. Fifteen subcomponents of vulnerability indicators were identified in ten sub-cities (Addis Ketema, Arada, Akaki-Kality, Bole, Gulelle, Kirkos, Kolfe-Keraniyo, Lideta, Nifasilk-Lafto and Yeka) of Addis Ababa. Due to the scale, degree, amount and unit of measurement for the selected indicators varied, their values were normalized to a number which ranges between 0 and 1, indicating as the values increased to 1, vulnerability to climate change increases. The study uses Iyengar and Sudarshan's unequal weighting system, to assign a weight to all indicators. The results were mapped using ArcGIS 10.2 package. Results: The results indicated that the ten sub-cities in Addis Ababa were found in different levels of vulnerability to climate change. The exposure and sensitivity were highest for Addis Ketema, Arada, and Lideta which are found in central parts of the city, with a normalization index value greater than 0.5. The adaptive capacity index is the highest in Gulelle, Bole, and Arada sub-cities. These sub-cities have better quality houses, well-planned districts, good infrastructural facilities and good coverage of green areas compared to others. The overall climate change vulnerability was the highest (normalized index > 0.5) in Arada, Addis Ketema and Lideta, due to the adaptation capacity is the lowest compared to other sub-cities. Conclusion: Addis Ababa is vulnerable to climate change impacts and the degree of vulnerability is underpinned by the interaction of multiple factors mainly adaptive capacities of sub-cities, location based characteristics and changes in climatic parameters. These present a need to strengthen mitigation and adaptation activities and prioritize sub cities for intervention based on the degree of vulnerability. It is also understood that the Sullivan and Meigh's Model and IPCC's approach for climate change analyses, could be used simultaneously for preparing vulnerability index per different geographical locations.

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“…Besides detecting damage due to logging, NDMI is also sensitive to forest disturbances caused by diseases related to forest health. Reference [64] and vegetation damage due to drought efects [65]; it is also benefcial to monitor water status as an early warning against drought [66].…”

Section: Learn About Mangrove Changes From the Mangrovementioning

confidence: 99%

AMMI Automatic Mangrove Map and Index: Novelty for Efficiently Monitoring Mangrove Changes with the Case Study in Musi Delta, South Sumatra, Indonesia

Suyarso¹,

Avianto²

2022

International Journal of Forestry Research

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Mapping mangroves using satellite imagery has been done for decades. It helps reduce obstacles in inaccessible places caused by the mangroves’ intricate root system, thick mud, and loss of position signals. There is an urgent need to produce a mangrove map that automatically and accurately covers the mangroves with the density index of the canopy as visually represented in satellite imagery. The research was conducted through an analytical desk study of the mangrove features from space. The study aims to develop a simple formula for automatically tracing, capturing, and mapping mangroves and determining the canopy density index from open access of satellite data to eliminate manual digitization work, make it easy to use, and save cost and time. The goal is to monitor, assess, and manage the condition of mangroves for anyone interested in mangroves, including the central government, local authorities, and local communities. As a result, the authors proposed an algorithm: (ρNIR − ρRed)/(ρRed + ρSWIR1) ∗ (ρNIR − ρSWIR1)/(ρSWIR1 − 0.65 ∗ ρRed). Experimental results in many mangrove forests using Landsat 5 TM, Landsat 7 ETM, Landsat 8 OLI, and Sentinel 2 imageries show satisfactory performance. The maps capture the spatial extent of the mangroves automatically and match the satellite imagery visually. The index correlates significantly with the Normalized Difference Water Index (NDWI), with R2 reaching 0.99. The research will apply the formula of the Musi Delta mangrove complex in South Sumatra, Indonesia. The advantage of the algorithm is that it works well, is easy to use, produces mangrove maps faster, informs the index, and efficiently monitors the change in mangrove conditions from time to time.

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High accuracy Land Use Land Cover (LULC) maps for detecting agricultural drought effects in rainfed agro-ecosystems in central Mexico

Cited by 11 publications

References 60 publications

Rural households’ livelihood vulnerability to climate variability and extremes: a livelihood zone-based approach in the Northeastern Highlands of Ethiopia

Rural households’ livelihood vulnerability to climate variability and extremes: a livelihood zone-based approach in the Northeastern Highlands of Ethiopia

GIS based quantification and mapping of climate change vulnerability hotspots in Addis Ababa

AMMI Automatic Mangrove Map and Index: Novelty for Efficiently Monitoring Mangrove Changes with the Case Study in Musi Delta, South Sumatra, Indonesia

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