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Izaya Numata

Bio: Izaya Numata is an academic researcher from South Dakota State University. The author has contributed to research in topics: Deforestation & Land cover. The author has an hindex of 20, co-authored 40 publications receiving 1491 citations. Previous affiliations of Izaya Numata include University of California, Santa Barbara.

Papers
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Journal ArticleDOI
TL;DR: It is shown that 169,074 km2 of Amazonian forest was converted to human-dominated land uses, such as agriculture, from 2000 to 2010 and annual forest degradation increased, equivalent to 20% of the area converted by deforestation.
Abstract: Forest degradation in the Brazilian Amazon due to selective logging and forest fires may greatly increase the human footprint beyond outright deforestation. We demonstrate a method to quantify annual deforestation and degradation simultaneously across the entire region for the years 2000–2010 using high-resolution Landsat satellite imagery. Combining spectral mixture analysis, normalized difference fraction index, and knowledge-based decision tree classification, we mapped and assessed the accuracy to quantify forest (0.97), deforestation (0.85) and forest degradation (0.82) with an overall accuracy of 0.92. We show that 169,074 km2 of Amazonian forest was converted to human-dominated land uses, such as agriculture, from 2000 to 2010. In that same time frame, an additional 50,815 km2 of forest was directly altered by timber harvesting and/or fire, equivalent to 30% of the area converted by deforestation. While average annual outright deforestation declined by 46% between the first and second halves of the study period, annual forest degradation increased by 20%. Existing operational monitoring systems (PRODES: Monitoramento da Florestal Amazonica Brasileira por Satelite) report deforestation area to within 2% of our results, but do not account for the extensive forest degradation occurring throughout the region due to selective logging and forest fire. Annual monitoring of forest degradation across tropical forests is critical for developing land management policies as well as the monitoring of carbon stocks/emissions and protected areas.

224 citations

Journal ArticleDOI
TL;DR: In this paper, the authors examined error sources introduced during accuracy assessment of a regional land-cover map generated from Landsat Thematic Mapper (TM) data in Rondonia, southwestern Brazil.

206 citations

Journal ArticleDOI
TL;DR: In this article, the authors evaluate the connection between field grass biophysical measures and remote sensing, and investigate the impact of grazing intensity on pasture biophysical measure in Rondonia, in the Brazilian Amazon.

152 citations

Journal Article
TL;DR: In this paper, a multistage process was used to map primary forest, pasture, second growth, urban, rock/savanna, and water using 33 Landsat scenes acquired over three contiguous areas between 1975 and 1999.
Abstract: [1] We describe spatiotemporal variation in land cover over 80,000 km 2 in central Rondonia. We use a multistage process to map primary forest, pasture, second growth, urban, rock/savanna, and water using 33 Landsat scenes acquired over three contiguous areas between 1975 and 1999. Accuracy of the 1999 classified maps was assessed as exceeding 85% based on digital airborne videography. Rondonia is highly fragmented, in which forests outside of restricted areas consist of numerous, small irregular patches. Pastures in Rondonia persist over many years and are not typically abandoned to second growth, which when present rarely remains unchanged longer than 8 years. Within the state, annual deforestation rates, pasture area, and ratio of second growth to cleared area varied spatially. Highest initial deforestation rates occurred in the southeast (Luiza), at over 2%, increasing to 3% by the late 1990s. In this area, the percentage of cleared land in second growth averaged 18% and few pastures were abandoned. In central Rondonia (Ji-Parana), deforestation rates rose from 1.2% between 1978 and 1986 to a high of 4.2% in 1999. In the northwest (Ariquemes), initial deforestation rates were lowest at 0.5% but rose substantially in the late 1990s, peaking at 3% in 1998. The ratio of second growth to cleared area was more than double the ratio in Luiza and few pastures remained unchanged beyond 8 years. Land clearing was most intense close to the major highway, BR364, except in Ariquemes. Intense forest clearing extended at least 50 km along the margins of BR364 in Ji-Parana and Luiza. Spatial differences in land use are hypothesized to result from a combination of economic factors and soil fertility.

140 citations

Journal ArticleDOI
TL;DR: In this paper, a multistage process was used to map primary forest, pasture, second growth, urban, rock/savanna, and water using 33 Landsat scenes acquired over three contiguous areas between 1975 and 1999.
Abstract: [1] We describe spatiotemporal variation in land cover over 80,000 km2 in central Rondonia. We use a multistage process to map primary forest, pasture, second growth, urban, rock/savanna, and water using 33 Landsat scenes acquired over three contiguous areas between 1975 and 1999. Accuracy of the 1999 classified maps was assessed as exceeding 85% based on digital airborne videography. Rondonia is highly fragmented, in which forests outside of restricted areas consist of numerous, small irregular patches. Pastures in Rondonia persist over many years and are not typically abandoned to second growth, which when present rarely remains unchanged longer than 8 years. Within the state, annual deforestation rates, pasture area, and ratio of second growth to cleared area varied spatially. Highest initial deforestation rates occurred in the southeast (Luiza), at over 2%, increasing to 3% by the late 1990s. In this area, the percentage of cleared land in second growth averaged 18% and few pastures were abandoned. In central Rondonia (Ji-Parana), deforestation rates rose from 1.2% between 1978 and 1986 to a high of 4.2% in 1999. In the northwest (Ariquemes), initial deforestation rates were lowest at 0.5% but rose substantially in the late 1990s, peaking at 3% in 1998. The ratio of second growth to cleared area was more than double the ratio in Luiza and few pastures remained unchanged beyond 8 years. Land clearing was most intense close to the major highway, BR364, except in Ariquemes. Intense forest clearing extended at least 50 km along the margins of BR364 in Ji-Parana and Luiza. Spatial differences in land use are hypothesized to result from a combination of economic factors and soil fertility.

137 citations


Cited by
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Journal ArticleDOI
TL;DR: It is suggested that designing a suitable image‐processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map and the selection of a suitable classification method is especially significant for improving classification accuracy.
Abstract: Image classification is a complex process that may be affected by many factors. This paper examines current practices, problems, and prospects of image classification. The emphasis is placed on the summarization of major advanced classification approaches and the techniques used for improving classification accuracy. In addition, some important issues affecting classification performance are discussed. This literature review suggests that designing a suitable image-processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map. Effective use of multiple features of remotely sensed data and the selection of a suitable classification method are especially significant for improving classification accuracy. Non-parametric classifiers such as neural network, decision tree classifier, and knowledge-based classification have increasingly become important approaches for multisource data classification. Integration of remote sensing, geographical information systems (GIS), and expert system emerges as a new research frontier. More research, however, is needed to identify and reduce uncertainties in the image-processing chain to improve classification accuracy.

2,741 citations

01 Jan 1993

2,271 citations

Journal ArticleDOI
TL;DR: This work provides practitioners with a set of “good practice” recommendations for designing and implementing an accuracy assessment of a change map and estimating area based on the reference sample data.

1,708 citations

Journal ArticleDOI
01 Mar 1980-Nature

1,327 citations

Journal ArticleDOI
TL;DR: In this article, the first 30 m resolution global land cover maps using Landsat Thematic Mapper TM and enhanced thematic mapper plus ETM+ data were produced. And the authors used four classifiers that were freely available were employed, including the conventional maximum likelihood classifier MLC, J4.8 decision tree classifier, Random Forest RF classifier and support vector machine SVM classifier.
Abstract: We have produced the first 30 m resolution global land-cover maps using Landsat Thematic Mapper TM and Enhanced Thematic Mapper Plus ETM+ data. We have classified over 6600 scenes of Landsat TM data after 2006, and over 2300 scenes of Landsat TM and ETM+ data before 2006, all selected from the green season. These images cover most of the world's land surface except Antarctica and Greenland. Most of these images came from the United States Geological Survey in level L1T orthorectified. Four classifiers that were freely available were employed, including the conventional maximum likelihood classifier MLC, J4.8 decision tree classifier, Random Forest RF classifier and support vector machine SVM classifier. A total of 91,433 training samples were collected by traversing each scene and finding the most representative and homogeneous samples. A total of 38,664 test samples were collected at preset, fixed locations based on a globally systematic unaligned sampling strategy. Two software tools, Global Analyst and Global Mapper developed by extending the functionality of Google Earth, were used in developing the training and test sample databases by referencing the Moderate Resolution Imaging Spectroradiometer enhanced vegetation index MODIS EVI time series for 2010 and high resolution images from Google Earth. A unique land-cover classification system was developed that can be crosswalked to the existing United Nations Food and Agriculture Organization FAO land-cover classification system as well as the International Geosphere-Biosphere Programme IGBP system. Using the four classification algorithms, we obtained the initial set of global land-cover maps. The SVM produced the highest overall classification accuracy OCA of 64.9% assessed with our test samples, with RF 59.8%, J4.8 57.9%, and MLC 53.9% ranked from the second to the fourth. We also estimated the OCAs using a subset of our test samples 8629 each of which represented a homogeneous area greater than 500 m × 500 m. Using this subset, we found the OCA for the SVM to be 71.5%. As a consistent source for estimating the coverage of global land-cover types in the world, estimation from the test samples shows that only 6.90% of the world is planted for agricultural production. The total area of cropland is 11.51% if unplanted croplands are included. The forests, grasslands, and shrublands cover 28.35%, 13.37%, and 11.49% of the world, respectively. The impervious surface covers only 0.66% of the world. Inland waterbodies, barren lands, and snow and ice cover 3.56%, 16.51%, and 12.81% of the world, respectively.

1,212 citations