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Balancing Land Surface’s Brightness-Shadowing and Spectral Reflectance to Enhance the Discrimination of Built-up Footprint from Surrounding Noise
Alfred Homere Ngandam Mfondoum,
Paul Gerard Gbetkom,
Sofia Hakdaoui,
Ryan Cooper,
Armel Fabrice Mvogo Moto,
Brian Njumeneh
Issue:
Volume 9, Issue 1, June 2021
Pages:
1-15
Received:
14 December 2020
Accepted:
25 December 2020
Published:
4 January 2021
Abstract: Recent evolutions of the geospatial technologies are more accurate in mapping and monitoring land use land cover, LULC, in different environments and at different spatial scales. However, some urban applications keep facing issues such as misclassification and other noise in unplanned cities with disorganized built-up and mixed housing material, and surrounded by a composed biophysical environment. This paper reports the processing leading to a new spectral index, that balances the land surface brightness temperature and spectral reflectance to accurately extract the built-up. The namely Brightness Adjusted Built-up Index, BABI, is proposed as a weighted ratio of Landsat OLI-TIRS bands. The methodology is based on a multi-perceptron layers, MLP, regression between a classified image and individually classified red, SWIR1, SWIR2 and TIR bands reclassified “1 = built-up; 0 = Non-Built-up”, with an average r2=0.78. The same way, a linear regression of popular built-up spectral indices such as Normalized Difference Built-up Index, NDBI, and Urban Index, UI, or recently proposed Modified New Built-up Index, MNBI, and Normalized Difference Built-up and Surroundings Unmixing Index, NDBSUI, on one hand, by light-dark spectral indices such as, Normalized Difference Soil Index, NDSI, Bare Soil Index, BSI, and Shadow index on the other hand, stands for the natural environment noise assessment in and around the built-up, with an r2=0.75. The MLP r2 standing for the built-up information, is rounded to 0.8 and according to their rank in the process, the weights allotted are 0.2, 0.4 and 0.8 in the numerator, and inversely 0.8, 0.6 and 0.2 in the denominator, to the red, SWIR1 and SWIR2 bands respectively. Whereas, the simple linear regression r2 standing for the noise is used to weigh the brightness temperature, TB in the numerator and subtracted from the previous group. The value 0.001 multiplies the whole ratio to lower the decimals of the outputs for an easy interpretation. As results, on the floating images scaled [0-1], built-up values are ≥0.1 in Yaoundé (Cameroon) and ≥0.07 in Bangui (Central African Republic). The overall accuracies are 96% in Yaoundé and 98.5% in Bangui, with corresponding kappa coefficients of 0.94 and 0.97. These scores are better than those of the NDBI, UI, MNBI and NDBSUI.
Abstract: Recent evolutions of the geospatial technologies are more accurate in mapping and monitoring land use land cover, LULC, in different environments and at different spatial scales. However, some urban applications keep facing issues such as misclassification and other noise in unplanned cities with disorganized built-up and mixed housing material, an...
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Landsat8 Satellite Image Classification with ERDAS for Mapping the Kalambatritra Special Reserve
Arisetra Razafinimaro,
Aimé Richard Hajalalaina,
ZoJaona Tantely Reziky,
Eric Delaitre,
Avisoa Andrianarivo
Issue:
Volume 9, Issue 1, June 2021
Pages:
16-22
Received:
17 December 2020
Accepted:
24 December 2020
Published:
23 February 2021
Abstract: This paper focuses on the Landsat 8 satellite image classification of the OLI sensor via the remote sensing software Erdas Imagine in order to calculate the land cover surface and to establish the mapping of the special reserve Kalambatritra of Madagascar for the year 2018. For this, we adopted the methodology of satellite image processing based on supervised classification algorithms. The processing was moved to spectral preparation and improvement of spatial resolution using the blue, green, red, near infrared and panchromatic channels. Then, a comparison study of the supervised classification algorithms was done to obtain a more accurate result. The validation of the classification results was performed using several reference points, a previous national processing result already validated in the field and the Google earth image of the same year. After repeating the classification several times, we obtained accuracies of 77%, 75%, 88%, 84% and 90% with Kappa indices of 0.64, 0.61, 0.80, 0.76 and 0.84 for the Spectral Angle Mapper, Spectral Correlation Mapper, Maximum Likelihood, Mahalanobis Distance and Minimum Distance. Based on these results, the minimum distance showed a higher accuracy and gave us 13462.1842 ha of forest area, 16798.8006 ha of prairie for the year 2018.
Abstract: This paper focuses on the Landsat 8 satellite image classification of the OLI sensor via the remote sensing software Erdas Imagine in order to calculate the land cover surface and to establish the mapping of the special reserve Kalambatritra of Madagascar for the year 2018. For this, we adopted the methodology of satellite image processing based on...
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Employing Remote Sensing Tools for Assessment of Land Use/Land Cover (LULC) Changes in Eastern Province, Rwanda
Jean Paul Nkundabose,
Felix Nshimiyimana,
Gratien Twagirayezu,
Olivier Irumva
Issue:
Volume 9, Issue 1, June 2021
Pages:
23-32
Received:
27 February 2021
Accepted:
11 March 2021
Published:
22 March 2021
Abstract: The present paper attempted to study land use/land cover (LULC) changes in a rural region of Eastern Province, Rwanda. The particular study area consists of part of Ngoma, Rwamagana, Kayonza, Bugesera districts of Eastern province, Rwanda, and a tiny part of Burundi. The study considered LULC changes that happened in 15 years from 2005 to 2020. By means of Remote Sensing and GIS tools, Land use/Land cover (LULC) changes were detected. Possible causes linked to historical changes were highlighted accordingly. Multi-temporal remote sensing images (Landsat imagery) were used to generate land use/land cover (LULC) maps. Two temporal satellite images were collected, preprocessed, and classified through supervised Image classification stages in ENVI 5.1. Corresponding maps were exported by ArcGIS 10.7. Seven important classes including water, bare land, wetlands, agriculture, vegetation, forest, and built-up area were classified and detected for changes using both Image change workflow and Thematic change workflow tools in ENVI 5.1. Among seven classes of land use/land cover (LULC), four experienced gains while built-up area, forest, and bare land witnessed decrease/losses over the last 15 years period (2005-2020). Like Forest diminished from 197.8821 km2 in 2005 to 56.9304 km2 in 2020. Several factors including government policies and regulations, population growth, and economic development can be attributed to these changes. The present work can provide important insights on land use planning and management for the area under consideration and we believe this work to contribute to the literature on the application of ENVI and related remote sensing tools.
Abstract: The present paper attempted to study land use/land cover (LULC) changes in a rural region of Eastern Province, Rwanda. The particular study area consists of part of Ngoma, Rwamagana, Kayonza, Bugesera districts of Eastern province, Rwanda, and a tiny part of Burundi. The study considered LULC changes that happened in 15 years from 2005 to 2020. By ...
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The Degradation of the Bafut-Ngemba Forest Reserve Revisited: A Spatio-temporal Analysis of Forest Cover Change Dynamics
Josephine Akenji Maghah,
Reeves Meli Fokeng
Issue:
Volume 9, Issue 1, June 2021
Pages:
33-41
Received:
17 February 2021
Accepted:
3 March 2021
Published:
26 March 2021
Abstract: Globally, forest reserves are created with a premier objective to conserve important biodiversity and to ensure ecosystems services provision. Unfortunately, forest reserves in the global south are threatened by the tremendous rise in human numbers and the unsustainable exploitation of forest resources. This is the problem facing protected areas (PAs), including forest reserves in Cameroon. The Bafut-Ngemba Forest Reserve (BNFR) is just a case in point of the many transformed and ecological twisted forest reserves in the Western Highlands of Cameroon. The BNFR is no biodiversity paradise as the humanisation of the reserve has taken an unprecedented toll in recent times. The study updated forest cover changes within the reserve from previous studies spanning across 2010-2021 as a baseline data towards the effective design of sustainable forest conservation planning. Satellite remote sensing employing high resolution ASTER (15m) and real-time Google Earth images were used to assess the forest cover dynamics. Between 2010 and 2015, forest loss was mild, either -27.135ha. From 2015-2021, in just less than 6 years, 696.397ha of forest cover was lost. For the entire study period (2010-2021), at total of 723.532ha of forest is estimated to have been lost. Forest loss in the BNFR is linked to some four anthropogenic stressors; farmland encroachment, eucalyptus colonisation, wood harvesting and cattle grazing alongside inter-annual fires used for pasture regeneration and rangeland management. Conservation efforts are urgently needed should the remaining threatened biodiversity, mostly avifauna is to be protected in line with monitoring progress to global targets and SDG 15.1.1.
Abstract: Globally, forest reserves are created with a premier objective to conserve important biodiversity and to ensure ecosystems services provision. Unfortunately, forest reserves in the global south are threatened by the tremendous rise in human numbers and the unsustainable exploitation of forest resources. This is the problem facing protected areas (P...
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A Web Based Google Earth Engine Approach for Irrigation Scheduling in Uttar Pradesh India Using Crop Water Stress Index
Pragati Singh,
Ashutosh Singh,
Rajesh Kumar Upadhyay
Issue:
Volume 9, Issue 1, June 2021
Pages:
42-46
Received:
10 March 2021
Accepted:
25 March 2021
Published:
1 April 2021
Abstract: Upgrading water use in agricultural crops requires advancements in location of crop water stress for irrigation scheduling, at different phases of the developing season to limit crop physiological harm and yield reduction. Potential of satellite data provide spatial and temporal dynamics of crop growth condition under water stress and analyse for suggestion of irrigation. This study is based on real time open-source web-based Google Earth Engine (GEE) approach for irrigation scheduling at field level based on its water stress condition. Sentinel-2 data was used for detecting water stress using the NDVI and NDWI indices. NDVI shows the crop growth and health whereas NDWI its water stress condition, by combining both the indices we have generated a new index, which is Crop Water Stress Index (CWSI) to schedule the irrigation. The real time Sentinel-2 data was used extract NDVI and NDWI indices and by combining both the indices a new indice was generated for detecting crop water stress condition to schedule the irrigation in real time. The output comes in five group of water stress condition as; No Stress, Low stress, Moderate stress, High stress and Severe stress. Using the result of CWSI the immediate irrigation should be given to those fields which are facing severe and high stress, delayed in moderate and low stress and no irrigation in no-stress. The overall study indicates that, GEE provide a real time better platform for analysing Crop Water Stress situation for scheduling proper irrigation practices for proper growth of crops to improve the production and income of farmers as well as It helps the irrigation planner for proper management of canals and other irrigation resources to the wastage of water.
Abstract: Upgrading water use in agricultural crops requires advancements in location of crop water stress for irrigation scheduling, at different phases of the developing season to limit crop physiological harm and yield reduction. Potential of satellite data provide spatial and temporal dynamics of crop growth condition under water stress and analyse for s...
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Household Solid Waste, a Challenge of Urban Sanitation: Case of Ngor Municipality (Dakar, Senegal)
Ramatoulaye Mbengue Ndiaye,
Bouna Ndiaye,
Vieux Boukhaly Traore,
Amadou Tahirou Diaw
Issue:
Volume 9, Issue 1, June 2021
Pages:
47-54
Received:
15 November 2019
Accepted:
17 December 2019
Published:
26 April 2021
Abstract: This study aims to understand factors that are causing the malfunction of the garbage collection system in the municipality of Ngor. To achieve this goal, we have adopted a methodological approach that focuses essentially on three components: (i) the investigation of 1/4 of Ngor's concessions through observation, interviews, questionnaires and documentation for collect socio-economic data; (ii) GPS surveys and mapping of the study area using GIS techniques and tools based on satellite images; (iii) analysis of collected information based on descriptive statistics. The results obtained reveal a bad garbage collection, which is at the origin of the insalubrities, the development of flies, mosquitoes and rodents having repercussions on the frame of life of the populations. It is also at the origin of the narrow streets, thus making access to neighborhoods difficult; this leads people to dispose of garbage in the open air or on the beach and in the evacuation channels, to the detriment of environmental concerns. This study has clearly highlighted deficiencies of human nature in the system of collection of household garbage in the municipality of Ngor. These results should provide the municipal authorities with a basis for thinking about the reformulation of their system of governance of such garbage and their political will to definitively solve the problem.
Abstract: This study aims to understand factors that are causing the malfunction of the garbage collection system in the municipality of Ngor. To achieve this goal, we have adopted a methodological approach that focuses essentially on three components: (i) the investigation of 1/4 of Ngor's concessions through observation, interviews, questionnaires and docu...
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Land Use / Land Cover Change in the Western Highlands of Cameroon: Case of the Sabga-Bamunka Area (1980-2020)
Tankie Quinta Shegwe,
Wirsiy Destain Yungsi,
Sirri Erika Suh,
Tchana Christian Brice,
Aloysious Kohtem Lebga,
Takem Mbi Bienvenu Magloire,
Chretien Ngouanet
Issue:
Volume 9, Issue 1, June 2021
Pages:
55-64
Received:
14 April 2021
Accepted:
29 April 2021
Published:
8 May 2021
Abstract: Man has greatly influenced the environment through his different activities, technology and dynamism. These modifications are land use/land cover changes. This research describes and analyses the implication of land use dynamics on resources in the Sabga-Bamunka area over time and space from 1980 to 2020. Remote sensing techniques and Geographic Information System were used in describing and analysing land use/cover changes in the Sabga-Bamunka area. Primary data was obtained through field observation in order to confirm observation on satellite imageries. Land use maps were produced using GIS software. Maps were generated to show changes in land use/land cover which were transposed into table and bar graphs to show the magnitude of changes. The analysis of static land use maps of 1980, 2010 and 2020 all showed that there are significant changes observed on forest cover, farmland, grazing land and settlement area. The findings revealed that the study area has experienced a drastic change in land use/land cover during the last forty years. The study area is characterised with decrease in grassland areas, forest and increase in settlement and farmland due to the increasing population which are the main triggering force of land use/land cover changes that has led to the reduction in vegetal cover. Forest decreased from 6 568 hectares (26%) in 1980 to 2 842 hectares (10%) in 2020 indicating a magnitude change of -3726 (-29%) and grass land decreased from 16 434 hectares (64%) in 1980 to 14 585 hectares (53%) in 2020 that is a magnitude change of -1849 (-14%). These decreases gave way to settlements and farmland. Therefore, a reduction in the excessive consumption of fuel wood, the practice of eco-forestry, raising of awareness and a dialogue plate-form are amongst the measures recommended to reduce land use/land cover change in the study area.
Abstract: Man has greatly influenced the environment through his different activities, technology and dynamism. These modifications are land use/land cover changes. This research describes and analyses the implication of land use dynamics on resources in the Sabga-Bamunka area over time and space from 1980 to 2020. Remote sensing techniques and Geographic In...
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