Deforestation (Essay Sample)

Investigating the applicability of Landsat 8 and Sentinel 2 in monitoring deforestation: Case study of the Makonde District, ZIMBABWE Almost a third of earth’s surface is covered by forests and they are very important for water cycles and global carbon. They also play a major role as they are provide raw materials for fuel, industry and many other services ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"PsCrGOAd","properties":{"formattedCitation":"(Astola {\\i{}et al.}, 2019)","plainCitation":"(Astola et al., 2019)","noteIndex":0},"citationItems":[{"id":199,"uris":["http://zotero.org/users/local/fT0Onyvw/items/MLQM3PGH"],"uri":["http://zotero.org/users/local/fT0Onyvw/items/MLQM3PGH"],"itemData":{"id":199,"type":"article-journal","abstract":"We compared the performance of Sentinel-2 and Landsat 8 data for forest variable prediction in the boreal forest of Southern Finland. We defined twelve modelling setups to train multivariable prediction models with either multilayer perceptron (MLP) or regression tree models with the brute force forward selection method. The reference data consisted of 739 circular field plots that had been collected by the Finnish Forest Centre concurrently with the Sentinel-2 and Landsat 8 acquisitions. The input data were divided into training, validation and test sets of equal sizes for 100 iterations in each modelling setup. The predicted forest variables were stem volume (V), stem diameter (D), tree height (H) and basal area (G), and their species-wise components for pine (Pine), spruce (Spr) and broadleaved (BL) trees. We recorded the performance figures and the best predictive image bands for each modelling setup.\n\nThe best average performance over the 100 modelling iterations was obtained using all Sentinel-2 bands. The plot-level relative root mean square errors (RMSE%) of the field observed mean were 38.4% for average stem diameter, 42.5% for stem basal area/ha, 30.4% for average tree height, and 59.3% for growing stock volume/ha with variables including all tree species. The corresponding best figures with all Landsat 8 bands were RMSE% = 44.6%, 50.2%, 36.6% and 72.2%, respectively. The Sentinel-2 outperformed Landsat 8 also when using near-equivalent image bands and Sentinel-2 data down-sampled to 30 m pixel resolution. The relative systematic error (bias%) did not show any significant differences between Sentinel-2 and Landsat 8 data: the average of the absolute value of bias% was 0.8% for Sentinel-2 and 1.2% for Landsat 8. The best predictive Sentinel-2 image band was the red-edge 1 (B05_RE1), when variable totals including all species were estimated. The short-wave infrared bands (B11_SWIR1 & B12_SWIR2) and the visible green band (B03_Green) were also among the best predictors. The median number of predictors in the best performing models was 4–6 for the Sentinel-2 and 4–5 for the Landsat 8 models, respectively.\n\nWe conclude that Sentinel-2 Multispectral Instrument (MSI) data can be recommended as the principal Earth observation data source in forest resources assessment.","container-title":"Remote Sensing of Environment","DOI":"10.1016/j.rse.2019.01.019","journalAbbreviation":"Remote Sensing of Environment","page":"257-273","source":"ResearchGate","title":"Comparison of Sentinel-2 and Landsat 8 imagery for forest variable prediction in boreal region","volume":"223","author":[{"family":"Astola","given":"Heikki"},{"family":"Häme","given":"Tuomas"},{"family":"Sirro","given":"Laura"},{"family":"Molinier","given":"Matthieu"},{"family":"Kilpi","given":"Jorma"}],"issued":{"date-parts":[["2019",1,28]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (Astola et al., 2019)(Astola et al., 2019). However excessive uses of trees from these forests has led to an increased rate of deforestation ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"QjSt4BB1","properties":{"unsorted":true,"formattedCitation":"(Astola {\\i{}et al.}, 2019)","plainCitation":"(Astola et al., 2019)","noteIndex":0},"citationItems":[{"id":199,"uris":["http://zotero.org/users/local/fT0Onyvw/items/MLQM3PGH"],"uri":["http://zotero.org/users/local/fT0Onyvw/items/MLQM3PGH"],"itemData":{"id":199,"type":"article-journal","abstract":"We compared the performance of Sentinel-2 and Landsat 8 data for forest variable prediction in the boreal forest of Southern Finland. We defined twelve modelling setups to train multivariable prediction models with either multilayer perceptron (MLP) or regression tree models with the brute force forward selection method. The reference data consisted of 739 circular field plots that had been collected by the Finnish Forest Centre concurrently with the Sentinel-2 and Landsat 8 acquisitions. The input data were divided into training, validation and test sets of equal sizes for 100 iterations in each modelling setup. The predicted forest variables were stem volume (V), stem diameter (D), tree height (H) and basal area (G), and their species-wise components for pine (Pine), spruce (Spr) and broadleaved (BL) trees. We recorded the performance figures and the best predictive image bands for each modelling setup.\n\nThe best average performance over the 100 modelling iterations was obtained using all Sentinel-2 bands. The plot-level relative root mean square errors (RMSE%) of the field observed mean were 38.4% for average stem diameter, 42.5% for stem basal area/ha, 30.4% for average tree height, and 59.3% for growing stock volume/ha with variables including all tree species. The corresponding best figures with all Landsat 8 bands were RMSE% = 44.6%, 50.2%, 36.6% and 72.2%, respectively. The Sentinel-2 outperformed Landsat 8 also when using near-equivalent image bands and Sentinel-2 data down-sampled to 30 m pixel resolution. The relative systematic error (bias%) did not show any significant differences between Sentinel-2 and Landsat 8 data: the average of the absolute value of bias% was 0.8% for Sentinel-2 and 1.2% for Landsat 8. The best predictive Sentinel-2 image band was the red-edge 1 (B05_RE1), when variable totals including all species were estimated. The short-wave infrared bands (B11_SWIR1 & B12_SWIR2) and the visible green band (B03_Green) were also among the best predictors. The median number of predictors in the best performing models was 4–6 for the Sentinel-2 and 4–5 for the Landsat 8 models, respectively.\n\nWe conclude that Sentinel-2 Multispectral Instrument (MSI) data can be recommended as the principal Earth observation data source in forest resources assessment.","container-title":"Remote Sensing of Environment","DOI":"10.1016/j.rse.2019.01.019","journalAbbreviation":"Remote Sensing of Environment","page":"257-273","source":"ResearchGate","title":"Comparison of Sentinel-2 and Landsat 8 imagery for forest variable prediction in boreal region","volume":"223","author":[{"family":"Astola","given":"Heikki"},{"family":"Häme","given":"Tuomas"},{"family":"Sirro","given":"Laura"},{"family":"Molinier","given":"Matthieu"},{"family":"Kilpi","given":"Jorma"}],"issued":{"date-parts":[["2019",1,28]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (Astola et al., 2019). Forest degradation and deforestation are some of the major threats worldwide when it comes to forests. Deforestation takes place when forests are cleared for non-forest activities like road construction and agriculture ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"yTV4JBgR","properties":{"formattedCitation":"(International Union for Conservation of Nature, 2017)","plainCitation":"(International Union for Conservation of Nature, 2017)","noteIndex":0},"citationItems":[{"id":224,"uris":["http://zotero.org/users/local/fT0Onyvw/items/LNJNYEFH"],"uri":["http://zotero.org/users/local/fT0Onyvw/items/LNJNYEFH"],"itemData":{"id":224,"type":"webpage","container-title":"IUCN","language":"en","title":"Deforestation and forest degradation","URL":"https://www.iucn.org/resources/issues-briefs/deforestation-and-forest-degradation","author":[{"literal":"International Union for Conservation of Nature"}],"accessed":{"date-parts":[["2021",1,13]]},"issued":{"date-parts":[["2017",11,10]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (International Union for Conservation of Nature, 2017).In developing countries there are a lot of challenges being faced in relation to the field of energy were the prices for purchasing oil and electrical energy have gone up and as a result of this people resort to using firewood as a source of energy ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"2dpIKaLi","properties":{"formattedCitation":"(Allen and Barnes, 2015)","plainCitation":"(Allen and Barnes, 2015)","noteIndex":0},"citationItems":[{"id":209,"uris":["http://zotero.org/users/local/fT0Onyvw/items/APBAKIA6"],"uri":["http://zotero.org/users/local/fT0Onyvw/items/APBAKIA6"],"itemData":{"id":209,"type":"article-journal","issue":"2","page":"163-184","title":"The Causes of Deforestation in Developing Countries - Allen - 1985 - Annals of the Association of American Geographers - Wiley Online Library","volume":"75","author":[{"family":"Allen","given":"Julia"},{"family":"Barnes","given":"Douglas"}],"issued":{"date-parts":[["2015"]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (Allen and Barnes, 2015). There are a number of factors that have led to deforestation like need wood as a source of fuel, poles for construction, urban expansion and the need farming land ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"JDiT96gl","properties":{"formattedCitation":"(Chipika and Kowero, 2000)","plainCitation":"(Chipika and Kowero, 2000)","noteIndex":0},"citationItems":[{"id":213,"uris":["http://zotero.org/users/local/fT0Onyvw/items/7S2F67QU"],"uri":["http://zotero.org/users/local/fT0Onyvw/items/7S2F67QU"],"itemData":{"id":213,"type":"article-journal","issue":"0167-8809","page":"175-185"...