Crop type mapping from pansharpened Landsat 8 NDVI data: A case of a highly fragmented and intensive agricultural system

Ouzemou, Jamal-Eddine; Harti, Abderrazak El; Lhissou, Rachid; Moujahid, Ali El; Bouch, Naima; Ouazzani, Rabii El; Bachaoui, El Mostafa; Ghmari, Abderrahmène El

doi:10.1016/j.rsase.2018.05.002

Cited by 38 publications

(48 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…O sensor MODIS apresentou a relação mais forte com os dados do SIDRA, com R² de 0,83, ou seja, a regressão linear pode explicar 83% da variação das áreas agrícolas (Figura 4a). Resultados similares a esses foram encontrados por Santos et al (2014) Para melhorar a separação das classes pode-se aumentar o número de amostras de treinamento para pastagem, ou mesmo, agrupar as amostras em classes como agricultura e não-agricultura, por exemplo, afim de aumentar os vetores de separação e melhorar a acurácia da classificação e evitar a superestimativa para a classe de agricultura (Ouzemou et al, 2018). Segundo Schmidt et al (2016), a identificação de culturas agrícolas, por meio de aprendizado de máquina, tem maior precisão com uso de imagens no inverno, já que áreas de pastagem não irrigadas apresentam menor fitomassa e não seriam confundidas com culturas agrícolas.…”

Section: Mapeamento De áReas Agrícolas Com Máquina De Vetor De Suportunclassified

Mapping of Agricultural Areas with Support Vector Machine in the Northwest of Minas Gerais, Brazil

et al. 2020

View full text Add to dashboard Cite

ResumoA agricultura é um dos setores que mais se destaca na economia do Brasil, sendo necessário muitas vezes o emprego do sensoriamento remoto, para identificação da expansão das áreas agrícolas e estimativas da sua produção. Esse trabalho tem por objetivo mapear as áreas agrícolas do noroeste de Minas Gerais por meio de máquina de vetor de suporte e comparar os resultados obtidos com o censo estatístico do Instituto Brasileiro de Geografia e Estatística. Para identificação das áreas agrícolas foi utilizado o algoritmo Service Vector Machine e imagens dos satélites e sensores Landsat 8 / OLI e Terra / MODIS. As amostras de treinamento do algoritmo foram obtidas por meio de imagem de alta resolução espacial, disponível no programa Google Earth Pro, nas classes rios, floresta, agricultura, pastagem e silvicultura. O mapeamento utilizando imagem do sensor OLI apresentou melhor Acurácia Global (0,81) e Kappa (0,66). A classificação com imagem OLI e MODIS apresentaram maior precisão na classe agricultura quando comparada as demais classes, apresentando confusão com pastagem, em decorrência da alta fitomassa da pastagem na época de aquisição das imagens (verão). O cálculo das áreas agrícolas demonstra superestimativa do Service Vector Machine (SVM) na classificação das imagens OLI e MODIS, com forte relação dos dados MODIS com o censo do IBGE (R²=0,83). Apenas municípios com áreas agrícolas superiores a 50.000 ha apresentaram menor erro na estimativa das áreas agrícolas. A aplicação do algoritmo mostra-se potencial para mapeamento da agricultura por meio de imagens dos sensores MODIS e OLI, porém deve-se avaliar a época de aquisição das imagens orbitais e variações nos parâmetros do algoritmo para melhorar a acurácia da classificação. Palavras-chave: agricultura; classificação supervisionada; aprendizado de máquina AbstractAgriculture is one of the sectors that stands out most in the Brazilian economy, often requiring the use of remote sensing to identify the expansion of agricultural areas and estimates of their production. This work aims to map the agricultural areas of the northwest of Minas Gerais by means of a support vector machine and compare the results obtained with the statistical census of the Brazilian Institute of Geography and Statistics. For the identification of the agricultural areas, the Service Vector Machine algorithm and images of the Landsat 8 / OLI and Terra / MODIS satellites and sensors were used. The training samples of the algorithm were obtained by high resolution spatial image, available in Google Earth Pro software, in the categories rivers, forest, agriculture, pasture and forestry. The OLI image mapping showed better Global Accuracy (0.81) and Kappa (0.66). The classification with OLI and MODIS images showed greater precision in the agriculture class when compared to the other classes, presenting confusion with pasture, due to the high phytomass of the pasture at the time of acquisition of the images (summer). The calculation of the agricultural areas shows an overestimation of the Serv...

show abstract

Section: Mapeamento De áReas Agrícolas Com Máquina De Vetor De Suportunclassified

Mapping of Agricultural Areas with Support Vector Machine in the Northwest of Minas Gerais, Brazil

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Nilai TDS yang terukur pada lokasi penelitian berkisar antara 38,10 -49,80 dengan rata-rata 43,00 ± 3,47 . TDS terkait dengan jumlah sedimen terlarut di (Ouzemou et al, 2018). Adanya gangguan atmosfir menyebabkan nilai NDVI menjadi sangat rendah.…”

Section: Total Dissolve Sediment (Tds)unclassified

Analisis Karakteristik Dimensi Ekologi Untuk Menunjang Pengelolaan Pulau – Pulau Kecil Terluar (Ppkt) Secara Berkelanjutan Di Kabupaten Nunukan, Kalimantan Utara

Mustikasari

Ramdhan²,

Amry³

et al. 2019

Jurnal Kelautan Nasional

View full text Add to dashboard Cite

Sebagai salah satu pulau terluar dari propinsi termuda di Indonesia, tingkat pembangunan di Pulau Nunukan berkembang dengan pesat. Pembangunan tersebut tentu berdampak terhadap lingkungan perairan sekitar Pulau Nunukan. Pada Bulan April 2016 telah dilakukan pengambilan dan pengukuran data hidro oseanografi dan data luasan mangrove. Sedangangkan pada musim peralihan I (April) dan Musim Peralihan II (September) 2016 telah dilakukan survey pengambilan data fisika-kimia air laut di perairan Pulau Nunukan. Metode yang digunakan dalam penelitian ini adalah metode kualitatif dan kuantitatif dengan melakukan pengukuran in situ dan analisis laboratorium. Hasil menunjukkan bahwa kecepatan arus rata-rata mencapai 40 cm/detik, pH perairan berkisar antara 7.3 – 8.2, salinitas 30 – 37 ppm, sedangkan suhu berkisar antara 27 – 30 derajat Celsius. Terdapat 8 parameter yang dihasilkan yaitu: Suhu, derajat keasaman, Salinitas, Dissolve oxygen, Total Dissolve Solid, Kekeruhan, Konduktifitas, Densitas. Secara umum parameter yang terukur di perairan Pulau Nunukan masih dalam kondisi baik, hanya terlihat nilai suhu dan kekeruhan yang berada di luar ambang batas baku yang ditetapkan pemerintah. Dari karakteristik tersebut diatas maka perairan nunukan sangat cocok untuk area budidaya rumput laut Khusus untuk jenis Eucheuma sp. Sebaran indeks vegetasi pada tahun 2016 menunjukkan data kepadatan vegetasi mangrove yang sangat rendah.

show abstract

“…Many studies of this kind are, however, conducted with satellite data [8,9], but the spatial resolution of the majority of these data is too low to provide accurate results at the field level in highly fragmented agricultural areas with small field plots [6,10,11]. Although satellite data with high spatial resolutions exist, they are often too expensive.…”

Section: Introductionmentioning

confidence: 99%

Crop Separability from Individual and Combined Airborne Imaging Spectroscopy and UAV Multispectral Data

2020

View full text Add to dashboard Cite

Crop species separation is essential for a wide range of agricultural applications—in particular, when seasonal information is needed. In general, remote sensing can provide such information with high accuracy, but in small structured agricultural areas, very high spatial resolution data (VHR) are required. We present a study involving spectral and textural features derived from near-infrared (NIR) Red Green Blue (NIR-RGB) band datasets, acquired using an unmanned aerial vehicle (UAV), and an imaging spectroscopy (IS) dataset acquired by the Airborne Prism EXperiment (APEX). Both the single usage and combination of these datasets were analyzed using a random forest-based method for crop separability. In addition, different band reduction methods based on feature factor loading were analyzed. The most accurate crop separation results were achieved using both the IS dataset and the two combined datasets with an average accuracy (AA) of >92%. In addition, we conclude that, in the case of a reduced number of IS features (i.e., wavelengths), the accuracy can be compensated by using additional NIR-RGB texture features (AA > 90%).

show abstract

Crop type mapping from pansharpened Landsat 8 NDVI data: A case of a highly fragmented and intensive agricultural system

Cited by 38 publications

References 45 publications

Mapping of Agricultural Areas with Support Vector Machine in the Northwest of Minas Gerais, Brazil

Mapping of Agricultural Areas with Support Vector Machine in the Northwest of Minas Gerais, Brazil

Analisis Karakteristik Dimensi Ekologi Untuk Menunjang Pengelolaan Pulau – Pulau Kecil Terluar (Ppkt) Secara Berkelanjutan Di Kabupaten Nunukan, Kalimantan Utara

Crop Separability from Individual and Combined Airborne Imaging Spectroscopy and UAV Multispectral Data

Contact Info

Product

Resources

About