Evaluating mangrove conservation and sustainability through spatiotemporal (1990–2020) mangrove cover change analysis in Pakistan

Gilani, Hammad; Naz, Hafiza Iqra; Arshad, Masood; Nazim, Kanwal; Akram, Usman; Abrar, Aneeqa; Asif, Muhammad

doi:10.1016/j.ecss.2020.107128

Cited by 54 publications

(30 citation statements)

References 47 publications

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“…The total mangrove forest cover for all these sites was 924.1 km 2 in this study. The latest study by Gilani et al [83], mapped national-level mangroves in Pakistan, including various sites (9538 km 2 area) for 1990, 1995, 2000, 2005, 2010, 2015, and 2020. Processing and classification of Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI satellite images reported a national-scale increase in a mangrove from 477.22 km 2 in 1990 to 1463.59 km 2 in 2020 with an annual rate of change of 3.74%.…”

Section: Coastal Mangrove Sites Of Pakistanmentioning

confidence: 99%

“…Imran and Din [50] used OBIA and reported overall accuracy of 92.180%, a kappa coefficient of 0.914, the producer's accuracy of 92.70%, and the user's accuracy of 91.17%, using 179 ground validation samples. The study by Gilani et al [83] reported overall accuracies and kappa coefficient values, as shown in Table 2. It can be observed that better accuracies were achieved using a machine-learning algorithm (RF) for more recent years.…”

Section: Accuracies Assessments and Validations For Forest Mappingmentioning

confidence: 99%

“…In this forest ecoregion, eight studies were conducted [32,74,76,77,81,83,99,102]. The studies reported an increase in the mangroves' forest area in the coastal belt of Pakistan and the Indus delta.…”

Section: Indus River Delta-arabian Sea Mangrovesmentioning

confidence: 99%

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A Synthesis of Spatial Forest Assessment Studies Using Remote Sensing Data and Techniques in Pakistan

Ahmad

Gilani

et al. 2021

Forests

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This paper synthesizes research studies on spatial forest assessment and mapping using remote sensing data and techniques in Pakistan. The synthesis states that 73 peer-reviewed research articles were published in the past 28 years (1993–2021). Out of all studies, three were conducted in Azad Jammu & Kashmir, one in Balochistan, three in Gilgit-Baltistan, twelve in Islamabad Capital Territory, thirty-one in Khyber Pakhtunkhwa, six in Punjab, ten in Sindh, and the remaining seven studies were conducted on national/regional scales. This review discusses the remote sensing classification methods, algorithms, published papers' citations, limitations, and challenges of forest mapping in Pakistan. The literature review suggested that the supervised image classification method and maximum likelihood classifier were among the most frequently used image classification and classification algorithms. The review also compared studies before and after the 18th constitutional amendment in Pakistan. Very few studies were conducted before this constitutional amendment, while a steep increase was observed afterward. The image classification accuracies of published papers were also assessed on local, regional, and national scales. The spatial forest assessment and mapping in Pakistan were evaluated only once using active remote sensing data (i.e., SAR). Advanced satellite imageries, the latest tools, and techniques need to be incorporated for forest mapping in Pakistan to facilitate forest stakeholders in managing the forests and undertaking national projects like UN’s REDD+ effectively.

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Section: Coastal Mangrove Sites Of Pakistanmentioning

confidence: 99%

Section: Accuracies Assessments and Validations For Forest Mappingmentioning

confidence: 99%

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A Synthesis of Spatial Forest Assessment Studies Using Remote Sensing Data and Techniques in Pakistan

Ahmad

Gilani

et al. 2021

Forests

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“…Furthermore, in Pakistan’s Indus delta, approximately 16,000 camels and 11,000 cows eat mangrove leaves [ 1 ]. Each year, during the start of the flood season (June–July), camels from the remote areas of Sindh migrate to the mangrove area in herds and stay until October [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Mineral status of soil, sea water, and mangrove (Avicennia marina) forages in several coastal areas of West Sumatra

et al. 2021

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Background and Aim: The availability of minerals in the soil affect the mineral content of mangrove leaves. This study aimed to determine the macro- and micromineral contents in the environment and mangrove leaves (Avicennia marina) as animal feed in the coastal areas of West Sumatra, Indonesia. Materials and Methods: In this study, soil, water, and mangrove leaves were extracted from the mangrove plant's environment. The mineral contents were determined using the atomic absorption spectrophotometer of Beijing Rayleigh Analytical Instrument Corporation (make and country of origin). The total phenol and tannin contents were determined using the Folin–Ciocalteu and hide-powder methods, respectively. Results: The mineral content of the soil affected the mineral content of the plants. The soil and leaves of A. marina in the Pariaman area were richer in terms of macro- and microminerals. The soil had pH value, organic carbon content, leaf nitrogen content, phosphorus, calcium, and potassium of 5.65, 4.21%, 3.39%, 0.17%, 1.99%, and 0.54%, respectively. A. marina leaves had a total phenol and tannin contents of 24.51 mg GAE/g check the unit and 4.09%, respectively. Conclusion: This research showed that the mineral content in the soil in several mangrove areas in West Sumatra has a positive correlation with the mineral content in the leaves of A. marina, which have a complete mineral content. Therefore, A. marina leaves could be recommended as a mineral source for ruminants.

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“…GEE has been used to map and analyze UGS on a global and national scale [28][29][30]. Furthermore, some studies evaluated changes in forest ecosystems, which can provide valuable information about climate change adaptation, using the GEE platform.…”

Section: Introductionmentioning

confidence: 99%

Rapid Mapping and Annual Dynamic Evaluation of Quality of Urban Green Spaces on Google Earth Engine

Chen

Zhong

Jing

et al. 2021

IJGI

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In order to achieve the United Nations 2030 Sustainable Development Goals (SDGs) related to green spaces, monitoring dynamic urban green spaces (UGSs) in cities around the world is crucial. Continuous dynamic UGS mapping is challenged by large computation, time consumption, and energy consumption requirements. Therefore, a fast and automated workflow is needed to produce a high-precision UGS map. In this study, we proposed an automatic workflow to produce up-to-date UGS maps using Otsu’s algorithm, a Random Forest (RF) classifier, and the migrating training samples method in the Google Earth Engine (GEE) platform. We took the central urban area of Beijing, China, as the study area to validate this method, and we rapidly obtained an annual UGS map of the central urban area of Beijing from 2016 to 2020. The accuracy assessment results showed that the average overall accuracy (OA) and kappa coefficient (KC) were 96.47% and 94.25%, respectively. Additionally, we used six indicators to measure quality and temporal changes in the UGS spatial distribution between 2016 and 2020. In particular, we evaluated the quality of UGS using the urban greenness index (UGI) and Shannon’s diversity index (SHDI) at the pixel level. The experimental results indicate the following: (1) The UGSs in the center of Beijing increased by 48.62 km2 from 2016 to 2020, and the increase was mainly focused in Chaoyang, Fengtai, and Shijingshan Districts. (2) The average proportion of relatively high and above levels (UGI > 0.5) in six districts increased by 2.71% in the study area from 2016 to 2020, and this proportion peaked at 36.04% in 2018. However, our result revealed that the increase was non-linear during this assessment period. (3) Although there was no significant increase or decrease in SHDI values in the study area, the distribution of the SHDI displayed a noticeable fluctuation in the northwest, southwest, and northeast regions of the study area between 2016 and 2020. Furthermore, we discussed and analyzed the influence of population on the spatial distribution of UGSs. We found that three of the five cold spots were located in the east and southeast of Haidian District. Therefore, the proposed workflow could provide rapid mapping and dynamic evaluation of the quality of UGS.

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Evaluating mangrove conservation and sustainability through spatiotemporal (1990–2020) mangrove cover change analysis in Pakistan

Cited by 54 publications

References 47 publications

A Synthesis of Spatial Forest Assessment Studies Using Remote Sensing Data and Techniques in Pakistan

A Synthesis of Spatial Forest Assessment Studies Using Remote Sensing Data and Techniques in Pakistan

Mineral status of soil, sea water, and mangrove (Avicennia marina) forages in several coastal areas of West Sumatra

Rapid Mapping and Annual Dynamic Evaluation of Quality of Urban Green Spaces on Google Earth Engine

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