scholarly journals Harmonized in situ datasets for agricultural land use mapping and monitoring in tropical countries

2021 ◽  
Vol 13 (12) ◽  
pp. 5951-5967
Author(s):  
Audrey Jolivot ◽  
Valentine Lebourgeois ◽  
Louise Leroux ◽  
Mael Ameline ◽  
Valérie Andriamanga ◽  
...  
Keyword(s):  
Land Use ◽  
Agricultural Land ◽  
Satellite Image ◽  
Farming Systems ◽  
Agricultural Land Use ◽  
In Situ Data ◽  
Assessment And Monitoring ◽  
Crop Types ◽  
The Tropics

Abstract. The availability of crop type reference datasets for satellite image classification is very limited for complex agricultural systems as observed in developing and emerging countries. Indeed, agricultural land use is very dynamic, agricultural censuses are often poorly georeferenced and crop types are difficult to interpret directly from satellite imagery. In this paper, we present a database made of 24 datasets collected in a standardized manner over nine sites within the framework of the international JECAM (Joint Experiment for Crop Assessment and Monitoring) initiative; the sites were spread over seven countries of the tropical belt, and the number of data collection years depended on the site (from 1 to 7 years between 2013 and 2020). These quality-controlled datasets are distinguished by in situ data collected at the field scale by local experts, with precise geographic coordinates, and following a common protocol. Altogether, the datasets completed 27 074 polygons (20 257 crops and 6817 noncrops, ranging from 748 plots in 2013 (one site visited) to 5515 in 2015 (six sites visited)) documented by detailed keywords. These datasets can be used to produce and validate agricultural land use maps in the tropics. They can also be used to assess the performances and robustness of classification methods of cropland and crop types/practices in a large range of tropical farming systems. The dataset is available at https://doi.org/10.18167/DVN1/P7OLAP (Jolivot et al., 2021).

scholarly journals Harmonized in situ JECAM datasets for agricultural land use mapping and monitoring in tropical countries

2021 ◽  
Author(s):  
Audrey Jolivot ◽  
Valentine Lebourgeois ◽  
Mael Ameline ◽  
Valérie Andriamanga ◽  
Beatriz Bellón ◽  
...  
Keyword(s):  
Land Use ◽  
Agricultural Land ◽  
Satellite Image ◽  
Farming Systems ◽  
Agricultural Land Use ◽  
In Situ Data ◽  
Agricultural Census ◽  
Crop Types ◽  
The Tropics

Abstract. The availability of crop type reference datasets for satellite image classification is very limited for complex agricultural systems as observed in developing and emerging countries. Indeed, agricultural land use is very dynamic, agricultural census are often poorly georeferenced, and crop types are difficult to photo-interpret directly from satellite imagery. In this paper, we present nine datasets collected in a standardized manner between 2013 and 2020 in seven tropical and subtropical countries within the framework of the international JECAM (Joint Experiment for Crop Assessment and Monitoring) initiative. These quality-controlled datasets are distinguished by in situ data collected at field scale by local experts, with precise geographic coordinates, and following a common protocol. Altogether, the datasets completed 27 074 polygons (20 257 crop and 6 817 non-crop) documented by detailed keywords. These datasets can be used to produce and validate agricultural land use maps in the tropics, but also, to assess the performances and the robustness of classification methods of cropland and crop types/practices in a large range of tropical farming systems. The dataset is available at https://doi.org/10.18167/DVN1/P7OLAP.

Determination of Drought Threshold for Agricultural Land-use using Satellite Image Analysis Techniques

2020 ◽  
Author(s):  
Jieun Kim ◽  
Jaehyung Yu ◽  
Sang Kee Seo ◽  
Jin-Hee Baek ◽  
Byung Chil Jeon
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Near Infrared ◽  
Agricultural Land ◽  
Irrigation Management ◽  
Satellite Image ◽  
Agricultural Drought ◽  
Agricultural Land Use ◽  
Landsat 8 ◽  
Eastern Area

<p>The climate change causes major problems in natural disasters such as storms and droughts and has significant impacts on agricultural activities. Especially, global warming changed crops cultivated causing changes in agricultural land-use, and droughts along with land-use change accompanied serious problems in irrigation management. Moreover, it is very problematic to detect drought impacted areas with field survey and it burdens irrigation management. In South Korea, drought in 2012 occurred in western area while 2015 drought occurred in eastern area. The drought cycle in Korea is irregular but the drought frequency has shown an increasing pattern. Remote sensing approaches has been used as a solution to detect drought areas in agricultural land-use and many approaches has been introduced for drought monitoring. This study introduces remote sensing approaches to detect agricultural drought by calculation of local threshold associated with agricultural land-use. We used Landsat-8 satellite images for drought and non-drought years, and Vegetation Health Index(VHI) was calculated using red, near-infrared, and thermal-infrared bands. The comparative analysis of VHI values for the same agricultural land-use between drought year and non-drought year derived the threshold values for each type of land-use. The results showed very effective detection of drought impacted areas showing distinctive differences in VHI value distributions between drought and non-drought years.</p>

Agricultural land use changes and water quality: a case study in the watershed of the lagoon of Venice

1999 ◽  
Vol 39 (3) ◽  
pp. 135-148 ◽  
Author(s):  
Carlo Giupponi ◽  
Paolo Rosato
Keyword(s):  
Land Use ◽  
Environmental Impact ◽  
Impact Assessment ◽  
Environmental Impact Assessment ◽  
Agricultural Land ◽  
Farming Systems ◽  
Geographical Information ◽  
Agricultural Land Use ◽  
Lagoon Of Venice ◽  
The Impact

The effects of alternative agricultural land use scenarios in terms of environmental impact assessment on surface and ground water were simulated by means of combined socio-economic and environmental models. The economic model produced and evaluated alternative farming systems, defined in terms of land use (in farm crop allocations and regional statistics of crop distributions) and cultivation practices as influenced by different macro-economic scenarios of agricultural policies. These scenarios were defined on the basis of the present Common Agricultural Policy of the European Union and possible future measures for reducing the impact of current agricultural systems on the environment. The farmers' decisional process has been simulated with multi-objective functions aimed at maximising profits and minimising risk. The methodology for the environmental impact assessment of farming systems is based on a simulation model for non-point source agricultural pollution which determines the impact of agriculture on a single field basis as influenced by environmental variables (soil and climate) and farmers' decisions (crop, soil management, fertilisation, etc.). The results obtained from this model were used to calculate a series of comparative indices capable of describing the effects of the use of fertilisers and pesticides on surface and ground waters. A geographical information system supported the spatial data management in particular for: a) the definition of simulation environments; b) the integration of physical and statistical geographical information; c) the cartographic presentation of results and the comparison of alternative scenarios. The model has been applied in the area of the Watershed of the Lagoon of Venice (WLV), located in northern Italy and has demonstrated how alternative policy scenarios determine not only significant variations in the overall environmental impacts in the study area, but also remarkable differences in their spatial distribution.

scholarly journals Mapping Seasonal Agricultural Land Use Types Using Deep Learning on Sentinel-2 Image Time Series

2021 ◽  
Vol 13 (2) ◽  
pp. 289
Author(s):  
Misganu Debella-Gilo ◽  
Arnt Kristian Gjertsen
Keyword(s):  
Land Use ◽  
Time Series ◽  
Deep Learning ◽  
Agricultural Land ◽  
Satellite Image ◽  
Agricultural Land Use ◽  
Monitoring Method ◽  
Multiple Observations ◽  
Land Use Types ◽  
Sentinel 2

The size and location of agricultural fields that are in active use and the type of use during the growing season are among the vital information that is needed for the careful planning and forecasting of agricultural production at national and regional scales. In areas where such data are not readily available, an independent seasonal monitoring method is needed. Remote sensing is a widely used tool to map land use types, although there are some limitations that can partly be circumvented by using, among others, multiple observations, careful feature selection and appropriate analysis methods. Here, we used Sentinel-2 satellite image time series (SITS) over the land area of Norway to map three agricultural land use classes: cereal crops, fodder crops (grass) and unused areas. The Multilayer Perceptron (MLP) and two variants of the Convolutional Neural Network (CNN), are implemented on SITS data of four different temporal resolutions. These enabled us to compare twelve model-dataset combinations to identify the model-dataset combination that results in the most accurate predictions. The CNN is implemented in the spectral and temporal dimensions instead of the conventional spatial dimension. Rather than using existing deep learning architectures, an autotuning procedure is implemented so that the model hyperparameters are empirically optimized during the training. The results obtained on held-out test data show that up to 94% overall accuracy and 90% Cohen’s Kappa can be obtained when the 2D CNN is applied on the SITS data with a temporal resolution of 7 days. This is closely followed by the 1D CNN on the same dataset. However, the latter performs better than the former in predicting data outside the training set. It is further observed that cereal is predicted with the highest accuracy, followed by grass. Predicting the unused areas has been found to be difficult as there is no distinct surface condition that is common for all unused areas.

scholarly journals Influence of topography and human activity on apparent in situ <sup>10</sup>Be-derived erosion rates in Yunnan, SW China

2016 ◽  
Vol 4 (4) ◽  
pp. 819-830 ◽  
Author(s):  
Amanda H. Schmidt ◽  
Thomas B. Neilson ◽  
Paul R. Bierman ◽  
Dylan H. Rood ◽  
William B. Ouimet ◽  
...  
Keyword(s):  
Land Use ◽  
Human Activity ◽  
River Sediment ◽  
Agricultural Land ◽  
Agricultural Land Use ◽  
Weighted Mean ◽  
Erosion Rates ◽  
The North ◽  
Small Watersheds

Abstract. In order to understand better if and where erosion rates calculated using in situ 10Be are affected by contemporary changes in land use and attendant deep regolith erosion, we calculated erosion rates using measurements of in situ 10Be in quartz from 52 samples of river sediment collected from three tributaries of the Mekong River (median basin area = 46.5 km2). Erosion rates range from 12 to 209 mm kyr−1 with an area-weighted mean of 117 ± 49 mm kyr−1 (1 standard deviation) and median of 74 mm kyr−1. We observed a decrease in the relative influence of human activity from our steepest and least altered watershed in the north to the most heavily altered landscapes in the south. In the areas of the landscape least disturbed by humans, erosion rates correlate best with measures of topographic steepness. In the most heavily altered landscapes, measures of modern land use correlate with 10Be-estimated erosion rates but topographic steepness parameters cease to correlate with erosion rates. We conclude that, in some small watersheds with high rates and intensity of agricultural land use that we sampled, tillage and resultant erosion has excavated deeply enough into the regolith to deliver subsurface sediment to streams and thus raise apparent in situ 10Be-derived erosion rates by as much as 2.5 times over background rates had the watersheds not been disturbed.

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